TED2015
Jim Simons: The mathematician who cracked Wall Street
詹姆斯‧西蒙斯: 專訪顛覆華爾街的數學家
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詹姆斯‧西蒙斯曾是數學家與密碼解密員，他發現解密用的複雜數學，可來解釋常見的金融現象。西蒙斯在成為億萬富翁後，目前致力資助新生代的數學教師與學者。TED 總監克里斯‧安德森與西蒙斯促膝同坐，一起聊聊西蒙斯不凡的數字人生。
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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(安德森) 你算是數學界的奇葩
00:15
You had already taught at Harvard
and MIT at a young age.
and MIT at a young age.
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很早便在哈佛和麻省理工教書
00:18
And then the NSA came calling.
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之後NSA找上門
00:21
What was that about?
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這段故事是？
00:23
Jim Simons: Well the NSA --
that's the National Security Agency --
that's the National Security Agency --
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(西蒙斯) 喔，NSA是美國國家安全局
00:27
they didn't exactly come calling.
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並沒有實際找我
00:29
They had an operation at Princeton,
where they hired mathematicians
where they hired mathematicians
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它在普林斯頓有個機構
請了許多數學家
請了許多數學家
00:33
to attack secret codes
and stuff like that.
and stuff like that.
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來破解密碼之類的
00:37
And I knew that existed.
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我知道這事
00:39
And they had a very good policy,
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它們的規定挺不錯
00:41
because you could do half your time
at your own mathematics,
at your own mathematics,
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你可以一半研究數學
00:45
and at least half your time
working on their stuff.
working on their stuff.
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只要一半做它們的事
00:49
And they paid a lot.
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給薪很優渥
00:51
So that was an irresistible pull.
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這點很難抗拒
00:54
So, I went there.
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所以我就去了
00:56
CA: You were a code-cracker.
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(安) 你曾是密碼破解員
00:57
JS: I was.
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(西) 對，是的
00:58
CA: Until you got fired.
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(安) 直到被解雇
00:59
JS: Well, I did get fired. Yes.
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(西) 對，我被炒魷魚
01:01
CA: How come?
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(安) 怎會這樣?
01:03
JS: Well, how come?
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(西) 嗯，原因嘛
01:05
I got fired because,
well, the Vietnam War was on,
well, the Vietnam War was on,
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被解雇因為...那時越戰爆發
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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我單位的上司迷上越戰
01:16
and wrote a New York Times article,
a magazine section cover story,
a magazine section cover story,
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他替紐約時報寫了文章
成為封面故事
成為封面故事
01:20
about how we would win in Vietnam.
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談如何打贏越戰
01:22
And I didn't like that war,
I thought it was stupid.
I thought it was stupid.
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我不喜歡越戰，覺得很愚蠢
01:25
And I wrote a letter to the Times,
which they published,
which they published,
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便寫信給《時代》雜誌，後來被登出來
01:28
saying not everyone
who works for Maxwell Taylor,
who works for Maxwell Taylor,
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說並非麥克斯維爾·泰勒所有屬下
01:32
if anyone remembers that name,
agrees with his views.
agrees with his views.
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都贊同他，如果還有人記得這個名字
01:37
And I gave my own views ...
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我提出我的看法
01:39
CA: Oh, OK. I can see that would --
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(安) OK，我瞭解這會...
01:41
JS: ... which were different
from General Taylor's.
from General Taylor's.
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(西) 與泰勒將軍不同
01:44
But in the end, nobody said anything.
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但最後也沒人說什麼
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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那年我29歲，有個小子來找我
01:49
and said he was a stringer
from Newsweek magazine
from Newsweek magazine
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自稱是《新聞週刊》特約記者
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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想問我怎麼實踐自己的看法
01:58
And I told him, "I'm doing
mostly mathematics now,
mostly mathematics now,
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我說:「現在我幾乎都弄數學
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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等戰爭結束，才會做他們的事」
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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於是我便做了那天最明智的事
02:08
I told my local boss
that I gave that interview.
that I gave that interview.
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我把訪談一事告訴主管
02:13
And he said, "What'd you say?"
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他問我:「你說了什麼?」
02:14
And I told him what I said.
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我就照實說
02:16
And then he said,
"I've got to call Taylor."
"I've got to call Taylor."
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接著他說:「我要打電話給泰勒」
02:18
He called Taylor; that took 10 minutes.
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他打給泰勒，講了10分鐘
02:20
I was fired five minutes after that.
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再5分鐘我就被解雇了
02:23
CA: OK.
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(安) 這樣啊
02:24
JS: But it wasn't bad.
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(西) 不過這並非壞事
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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(安) 這不糟，因為你去了
紐約州立大學石溪分校
紐約州立大學石溪分校
02:28
and stepped up your mathematical career.
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數學生涯更上層樓
02:31
You started working with this man here.
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也開始跟這人合作
02:34
Who is this?
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他是誰？
02:36
JS: Oh, [Shiing-Shen] Chern.
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(西) 喔，陳省身
02:37
Chern was one of the great
mathematicians of the century.
mathematicians of the century.
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陳是那世紀最厲害的數學家
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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我在柏克萊念碩士時，就知道他
02:46
And I had some ideas,
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我有些想法
02:48
and I brought them to him
and he liked them.
and he liked them.
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告訴了他，他很喜歡
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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我們便一起努力，就上面你看到的
02:57
There it is.
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就是這個
02:59
CA: It led to you publishing
a famous paper together.
a famous paper together.
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(安) 你們共同發表了著名論文
03:02
Can you explain at all what that work was?
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可以談研究內容嗎?
03:07
JS: No.
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(西) 不行
03:08
(Laughter)
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(笑聲)
03:10
JS: I mean, I could
explain it to somebody.
explain it to somebody.
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(西) 我是說，可以講給別人聽
03:13
(Laughter)
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(笑聲)
03:15
CA: How about explaining this?
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(安) 如果說明這個呢?
03:17
JS: But not many. Not many people.
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(西) 可是, 不會向太多人
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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(安) 你曾告訴我，這跟球體有關
03:23
so let's start here.
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從這說起吧
03:25
JS: Well, it did,
but I'll say about that work --
but I'll say about that work --
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(西) 我要講那研究－
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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是有關球形, 但我想先說
03:32
that work was good mathematics.
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那是一流的數學研究
03:36
I was very happy with it; so was Chern.
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我非常高興，陳也是
03:39
It even started a little sub-field
that's now flourishing.
that's now flourishing.
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它甚至促成一個次領域，現在很興盛
03:44
But, more interestingly,
it happened to apply to physics,
it happened to apply to physics,
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更棒的是它被用於物理
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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一個未知領域，至少我不懂物理
03:54
and I don't think Chern
knew a heck of a lot.
knew a heck of a lot.
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我想陳也只略知皮毛
03:56
And about 10 years
after the paper came out,
after the paper came out,
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論文發表10年後
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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普林斯頓的愛德華·維騰
把它用在弦理論
把它用在弦理論
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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俄國人則用於所謂"凝聚體"研究
04:09
Today, those things in there
called Chern-Simons invariants
called Chern-Simons invariants
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如今這些被稱為"陳-西蒙不變式"
04:14
have spread through a lot of physics.
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廣泛應用在物理界
04:16
And it was amazing.
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這太不可思議
04:17
We didn't know any physics.
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我們完全是物理門外漢
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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從沒想過會被用於物理
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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然而，這就是數學
你總猜不到它的去向
你總猜不到它的去向
04:26
CA: This is so incredible.
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(安) 真難以置信
04:27
So, we've been talking about
how evolution shapes human minds
how evolution shapes human minds
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我們談到演化如何形塑人類思想
04:32
that may or may not perceive the truth.
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無論思想是否關於真理
04:34
Somehow, you come up
with a mathematical theory,
with a mathematical theory,
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你就這樣得出一個數學理論
04:38
not knowing any physics,
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完全不懂物理
04:40
discover two decades later
that it's being applied
that it's being applied
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這理論20年後被用來
04:42
to profoundly describe
the actual physical world.
the actual physical world.
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深入描述實際物理世界
04:45
How can that happen?
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怎麼辦到的?
04:46
JS: God knows.
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(西) 天曉得
04:47
(Laughter)
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(笑聲)
04:50
But there's a famous physicist
named [Eugene] Wigner,
named [Eugene] Wigner,
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知名物理學家尤金·維格納
04:54
and he wrote an essay on the unreasonable
effectiveness of mathematics.
effectiveness of mathematics.
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曾撰文談到數學不合理的有效性
04:59
Somehow, this mathematics,
which is rooted in the real world
which is rooted in the real world
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不管怎樣, 數學本就源自真實世界
05:03
in some sense -- we learn to count,
measure, everyone would do that --
measure, everyone would do that --
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例如學計算、測量，大家都這麼做
05:08
and then it flourishes on its own.
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這學門自己繁盛起來
05:10
But so often it comes
back to save the day.
back to save the day.
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常常一回到數學，困難就迎刃而解
05:14
General relativity is an example.
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廣義相對論就是一例
05:16
[Hermann] Minkowski had this geometry,
and Einstein realized,
and Einstein realized,
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愛因斯坦學了閔可夫斯基的幾何學後
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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驚呼「就是它了! 幫我釐清廣義相對論」
05:23
So, you never know. It is a mystery.
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所以, 你搞不懂的，這太奧秘了
05:27
It is a mystery.
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超乎常理
05:28
CA: So, here's a mathematical
piece of ingenuity.
piece of ingenuity.
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(安) 關於數學的獨創性
05:31
Tell us about this.
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講講這個
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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(西) 這是顆球－球體，球面被格狀劃分
05:38
you know, those squares.
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就那些四方形
05:42
What I'm going to show here was
originally observed by [Leonhard] Euler,
originally observed by [Leonhard] Euler,
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我要講的是（萊昂納多）歐拉發現的
05:47
the great mathematician, in the 1700s.
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18世紀偉大的數學家
05:50
And it gradually grew to be
a very important field in mathematics:
a very important field in mathematics:
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這現象逐漸成為重要的數學領域
05:55
algebraic topology, geometry.
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代數拓樸學、幾何學
05:59
That paper up there had its roots in this.
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我的研究即從這來
06:03
So, here's this thing:
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是這樣的
06:05
it has eight vertices,
12 edges, six faces.
12 edges, six faces.
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這裡有8頂點、12邊和6面
06:09
And if you look at the difference --
vertices minus edges plus faces --
vertices minus edges plus faces --
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如果加以運算：頂點數-邊數+面數
06:13
you get two.
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得到2
06:14
OK, well, two. That's a good number.
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嗯，好一個2
06:17
Here's a different way of doing it --
these are triangles covering --
these are triangles covering --
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換方法做，佈滿三角形
06:21
this has 12 vertices and 30 edges
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有12個頂點，30個邊
06:25
and 20 faces, 20 tiles.
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和20個面
06:30
And vertices minus edges
plus faces still equals two.
plus faces still equals two.
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此時點-邊+面仍是2
06:35
And in fact, you could do this
any which way --
any which way --
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事實上，你可用任何方法
06:38
cover this thing with all kinds
of polygons and triangles
of polygons and triangles
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球上蓋滿各種多邊形和三角形
06:41
and mix them up.
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混合在一起
06:42
And you take vertices minus edges
plus faces -- you'll get two.
plus faces -- you'll get two.
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再把點-邊+面，得到2
06:46
Here's a different shape.
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這是另一種形狀
06:48
This is a torus, or the surface
of a doughnut: 16 vertices
of a doughnut: 16 vertices
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這是環面，甜甜圈形表面16頂點
06:53
covered by these rectangles,
32 edges, 16 faces.
32 edges, 16 faces.
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覆蓋長方形，32邊，16面
06:58
Vertices minus edges comes out to be zero.
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點-邊+面得出0
07:01
It'll always come out to zero.
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答案永遠是0
07:02
Every time you cover a torus
with squares or triangles
with squares or triangles
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只要用長方或三角形
覆蓋環面
覆蓋環面
07:07
or anything like that,
you're going to get zero.
you're going to get zero.
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答案總是0
07:12
So, this is called
the Euler characteristic.
the Euler characteristic.
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這稱為歐拉示性數
07:14
And it's what's called
a topological invariant.
a topological invariant.
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也叫做拓樸不變量
07:18
It's pretty amazing.
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這很神奇
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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不論你怎麼劃，答案總是一樣
07:22
So that was the first sort of thrust,
from the mid-1700s,
from the mid-1700s,
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這是18世紀中以來第一個刺激
07:29
into a subject which is now called
algebraic topology.
algebraic topology.
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後來變成代數拓樸學
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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(安) 你對此更深入研究
07:35
into higher-dimensional theory,
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到更高維度理論
07:38
higher-dimensional objects,
and found new invariances?
and found new invariances?
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更高維度的物體，找新的不變量?
07:41
JS: Yes. Well, there were already
higher-dimensional invariants:
higher-dimensional invariants:
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是, 高維不變量已找到了
07:46
Pontryagin classes --
actually, there were Chern classes.
actually, there were Chern classes.
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龐特里亞金示性類，還有陳示性類
07:50
There were a bunch
of these types of invariants.
of these types of invariants.
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一大堆這類不變量
07:54
I was struggling to work on one of them
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那時我努力研究其中一個
07:58
and model it sort of combinatorially,
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發展成某種組合模型
08:02
instead of the way it was typically done,
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不用既有標準方法
08:05
and that led to this work
and we uncovered some new things.
and we uncovered some new things.
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這變成我們的研究，也發現新東西
08:10
But if it wasn't for Mr. Euler --
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但如果沒有歐拉
08:13
who wrote almost 70 volumes of mathematics
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寫下70卷數學書
08:17
and had 13 children,
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養育13個子女
08:19
who he apparently would dandle on his knee
while he was writing --
while he was writing --
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想必是邊寫邊逗弄幼兒
08:25
if it wasn't for Mr. Euler, there wouldn't
perhaps be these invariants.
perhaps be these invariants.
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若非歐拉, 就沒有這些不變量
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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(安) 恩, 我們瞭解了，奇特的心路歷程
08:36
Let's talk about Renaissance.
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現在讓我們談談文藝復興科技公司
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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由於你曾任NSA解碼員的研究經歷
08:44
you started to become a code-cracker
in the financial industry.
in the financial industry.
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你開始當金融界的解碼員
08:47
I think you probably didn't buy
efficient market theory.
efficient market theory.
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我想你不相信效率市場理論
08:50
Somehow you found a way of creating
astonishing returns over two decades.
astonishing returns over two decades.
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20年來，你有辦法獲利驚人
08:56
The way it's been explained to me,
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對我來說你的方法
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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驚人之處不在於獲利金額多寡
09:01
it's that you took them
with surprisingly low volatility and risk,
with surprisingly low volatility and risk,
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而是大幅降低變動性與風險
09:05
compared with other hedge funds.
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相較其他對沖基金
09:07
So how on earth did you do this, Jim?
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你到底怎麼辦到的?
09:10
JS: I did it by assembling
a wonderful group of people.
a wonderful group of people.
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(西) 我靠集合一群優秀的人
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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我開始經商時，我對數學已有些厭煩
09:18
I was in my late 30s,
I had a little money.
I had a little money.
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年紀快40，手頭有點錢
09:22
I started trading and it went very well.
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便開始做買賣，結果非常成功
09:25
I made quite a lot of money
with pure luck.
with pure luck.
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純靠運氣賺了一大筆錢
09:27
I mean, I think it was pure luck.
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我說，我認為是好運
09:29
It certainly wasn't mathematical modeling.
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而肯定不是數學模型
09:31
But in looking at the data,
after a while I realized:
after a while I realized:
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但我審視這些數字後
09:35
it looks like there's some structure here.
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發覺現似有固定模式
09:38
And I hired a few mathematicians,
and we started making some models --
and we started making some models --
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我便請幾位數學家，弄了幾個模型
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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類似我在防衛分析研究所做的
09:46
You design an algorithm,
you test it out on a computer.
you test it out on a computer.
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設計一套演算法，用電腦測試
09:48
Does it work? Doesn't it work? And so on.
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能用?不能用? 之類的
09:51
CA: Can we take a look at this?
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(安) 可否看看這個?
09:52
Because here's a typical graph
of some commodity.
of some commodity.
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這是常見的商品銷售圖
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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我想:「不過是隨機走高走低－
10:02
maybe a slight upward trend
over that whole period of time."
over that whole period of time."
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整體趨勢緩升」
你到底怎麼看這隨機圖
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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就能做生意、發現東西?
(西) 這圖很老套了
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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10:13
commodities or currencies
had a tendency to trend.
had a tendency to trend.
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商品或貨幣有其趨勢
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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不必然像這樣，但一段時間有其走向
10:23
And if you decided, OK,
I'm going to predict today,
I'm going to predict today,
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如果你決定，好，我要預測今天
10:27
by the average move in the past 20 days --
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靠前20日的平均變化
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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也許可猜得準，也賺到錢
10:35
And in fact, years ago,
such a system would work --
such a system would work --
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事實上幾年前，這系統還可行
10:41
not beautifully, but it would work.
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不漂亮，但過得去
10:43
You'd make money, you'd lose
money, you'd make money.
money, you'd make money.
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賺了，賠了，又賺
10:46
But this is a year's worth of days,
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但這是一年內表現最好的幾天
10:48
and you'd make a little money
during that period.
during that period.
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這期間賺得不多
這系統老掉牙了
10:53
It's a very vestigial system.
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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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(安) 所以你用不同期間長短
11:00
and see whether, for example,
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的趨勢來檢視，例如
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10天還是15天的走勢預測較準
(西) 沒錯，都得試過才知道
11:06
JS: Sure, you would try all those things
and see what worked best.
and see what worked best.
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順勢投資法
在60年代或許非常好用
在60年代或許非常好用
11:13
Trend-following would
have been great in the '60s,
have been great in the '60s,
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11:16
and it was sort of OK in the '70s.
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70年代還可以
11:19
By the '80s, it wasn't.
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到80年代就玩不通了
11:20
CA: Because everyone could see that.
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(安) 因為任何人都看得出來
11:23
So, how did you stay ahead of the pack?
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你是怎麼持續領先的?
11:27
JS: We stayed ahead of the pack
by finding other approaches --
by finding other approaches --
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(西) 我們靠開發其他方法保持領先－
11:33
shorter-term approaches to some extent.
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像是期間更短的方法
實際上是蒐集無數資料
11:37
The real thing was to gather
a tremendous amount of data --
a tremendous amount of data --
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11:40
and we had to get it by hand
in the early days.
in the early days.
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早期都一筆筆抄回來
11:44
We went down to the Federal Reserve
and copied interest rate histories
and copied interest rate histories
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我們到聯準會影印歷史利率
11:47
and stuff like that,
because it didn't exist on computers.
because it didn't exist on computers.
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之類的，那時還沒有電腦
11:50
We got a lot of data.
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我們取得大批資料
11:52
And very smart people -- that was the key.
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和絕頂聰明的人——這是關鍵
11:57
I didn't really know how to hire
people to do fundamental trading.
people to do fundamental trading.
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我不太會找人做實際買賣
12:01
I had hired a few -- some made money,
some didn't make money.
some didn't make money.
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我請過幾個——有人能賺，有的不行
12:04
I couldn't make a business out of that.
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我不能這樣做生意
12:06
But I did know how to hire scientists,
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但我知道怎麼請科學家
12:08
because I have some taste
in that department.
in that department.
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這方面我比較有品味
12:12
So, that's what we did.
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所以就這麼做了
12:13
And gradually these models
got better and better,
got better and better,
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模型表現越來越好
12:17
and better and better.
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越來越順
12:18
CA: You're credited with doing
something remarkable at Renaissance,
something remarkable at Renaissance,
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(安) 你帶領文藝復興公司的成果驚艷
12:21
which is building this culture,
this group of people,
this group of people,
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塑造了一種文化、一群人
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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他們不是老想錢的傭兵
12:27
Their motivation was doing
exciting mathematics and science.
exciting mathematics and science.
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而一心想玩數學和科學
12:31
JS: Well, I'd hoped that might be true.
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(西) 我希望這是真的
12:34
But some of it was money.
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但有些動機真的是錢
12:37
CA: They made a lot of money.
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(安) 他們賺了好多
12:39
JS: I can't say that no one came
because of the money.
because of the money.
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(西) 我不信沒人在乎錢
12:41
I think a lot of them
came because of the money.
came because of the money.
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我想許多人來都想賺錢
但他們也想樂在其中
12:44
But they also came
because it would be fun.
because it would be fun.
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2021
12:46
CA: What role did machine learning
play in all this?
play in all this?
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(安) 當中機器學習的角色是?
12:48
JS: In a certain sense,
what we did was machine learning.
what we did was machine learning.
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(西) 某些情況下，我們就是做機器學習
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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你面對成堆資料，試著模擬各種預測系統
12:59
until you get better and better at it.
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直到越發熟練
13:01
It doesn't necessarily feed back on itself
the way we did things.
the way we did things.
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它不一定會跟人一樣主動回饋資料
13:05
But it worked.
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但仍滿好用的
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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(安) 所以不同預測系統很難駕馭與掌握
13:12
I mean, you looked at everything, right?
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意思是，你什麼都算，是嗎?
13:14
You looked at the weather,
length of dresses, political opinion.
length of dresses, political opinion.
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天氣、裙長、政治評論
13:17
JS: Yes, length of dresses we didn't try.
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(西) 是的，裙長倒沒試過
13:20
CA: What sort of things?
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(安) 哪類東西?
13:22
JS: Well, everything.
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(西) 所有東西
13:23
Everything is grist for the mill --
except hem lengths.
except hem lengths.
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什麼都可用－除了衣擺長度
13:28
Weather, annual reports,
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天氣、年報
13:31
quarterly reports, historic data itself,
volumes, you name it.
volumes, you name it.
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季報、歷史資料、冊數，只要你叫得出來
13:35
Whatever there is.
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管他是什麼
13:37
We take in terabytes of data a day.
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我們每天取得1T的資料
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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接著儲存、處理、準備分析
13:45
You're looking for anomalies.
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尋找突出的現象
13:46
You're looking for -- like you said,
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在找——就像你說的
13:49
the efficient market
hypothesis is not correct.
hypothesis is not correct.
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效率市場假說並不正確
13:52
CA: But any one anomaly
might be just a random thing.
might be just a random thing.
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(安) 但任何奇特現象都可能只是隨機現象
13:55
So, is the secret here to just look
at multiple strange anomalies,
at multiple strange anomalies,
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所以秘訣是在與注意多次出現的異狀，
13:59
and see when they align?
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並觀察何時接連出現嗎?
14:01
JS: Any one anomaly
might be a random thing;
might be a random thing;
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(西) 任何異常狀可能只是恰巧
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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不過看夠多資料後
就知並非如此
就知並非如此
14:07
You can see an anomaly that's persistent
for a sufficiently long time --
for a sufficiently long time --
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會發現異常持續很久
14:12
the probability of it being
random is not high.
random is not high.
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隨機出現的機率反而不高
14:17
But these things fade after a while;
anomalies can get washed out.
anomalies can get washed out.
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一陣子它會不見，異常會消失
14:22
So you have to keep on top
of the business.
of the business.
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所以我們得保持領先
14:24
CA: A lot of people look
at the hedge fund industry now
at the hedge fund industry now
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(安) 目前人們看對沖基金產業
14:27
and are sort of ... shocked by it,
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都感到震驚
14:31
by how much wealth is created there,
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竟創造這麼多財富
14:34
and how much talent is going into it.
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又得投入大量腦力
14:37
Do you have any worries
about that industry,
about that industry,
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你擔心這產業嗎?
14:41
and perhaps the financial
industry in general?
industry in general?
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2414
或對整個金融業?
14:43
Kind of being on a runaway train that's --
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好似脫韁野馬
14:46
I don't know --
helping increase inequality?
helping increase inequality?
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我不曉得——助長社會不平等?
14:50
How would you champion what's happening
in the hedge fund industry?
in the hedge fund industry?
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你為何支持對沖基金的近來發展？
14:54
JS: I think in the last
three or four years,
three or four years,
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(西) 我想近3、4年
14:57
hedge funds have not done especially well.
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對沖基金表現平平
14:59
We've done dandy,
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我們曾風光一時
15:00
but the hedge fund industry as a whole
has not done so wonderfully.
has not done so wonderfully.
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4001
但這產業走得不太順
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
眾所周知，股市向來平步青雲
15:09
and price-earnings ratios have grown.
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3445
本益比增加了
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
過去5、6年錢賺到嚇死人
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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3350
但對沖基金就較差
15:20
People would ask me,
"What's a hedge fund?"
"What's a hedge fund?"
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3221
人們問我:「什麼是對沖基金?」
15:23
And I'd say, "One and 20."
281
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2260
我說:「1和20」
15:25
Which means -- now it's two and 20 --
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3566
意思是——現在是2和20
15:29
it's two percent fixed fee
and 20 percent of profits.
and 20 percent of profits.
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3353
2％的固定手續費，20％的獲利抽成
15:32
Hedge funds are all
different kinds of creatures.
different kinds of creatures.
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各家對沖基金差異很大
15:35
CA: Rumor has it you charge
slightly higher fees than that.
slightly higher fees than that.
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3239
(安) 有流言說，你收的高些
15:39
JS: We charged the highest fees
in the world at one time.
in the world at one time.
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3081
(西) 我們的手續費一度是世界最高
15:42
Five and 44, that's what we charge.
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3226
5和44，就這個價格
15:45
CA: Five and 44.
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1398
(安) 5和44
15:47
So five percent flat,
44 percent of upside.
44 percent of upside.
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3234
5%固定費用，44％獲利抽成
15:50
You still made your investors
spectacular amounts of money.
spectacular amounts of money.
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2783
你仍幫客戶賺進大把鈔票
(西) 是的，收益很不錯
15:53
JS: We made good returns, yes.
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1452
15:54
People got very mad:
"How can you charge such high fees?"
"How can you charge such high fees?"
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3000
人們氣我:「這太貴了」
15:57
I said, "OK, you can withdraw."
293
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1627
我說:「OK，你可退出」
15:59
But "How can I get more?"
was what people were --
was what people were --
294
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2818
但「如何賺更多」就是人們...
16:02
(Laughter)
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1504
(笑聲)
16:03
But at a certain point,
as I think I told you,
as I think I told you,
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2440
但重點是，我跟你提過
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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我們收購了所有投資者，因為這基金能賺
16:11
CA: But should we worry
about the hedge fund industry
about the hedge fund industry
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2704
(安) 但該替對沖基金業擔心嗎?
16:14
attracting too much of the world's
great mathematical and other talent
great mathematical and other talent
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它吸走太多全球優秀的數學等人才
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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只做這事，而無視世界其他問題
16:22
JS: Well, it's not just mathematical.
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(西) 這個嘛，不單數學家
我們也聘請天文學家和物理學家等
16:24
We hire astronomers and physicists
and things like that.
and things like that.
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16:27
I don't think we should worry
about it too much.
about it too much.
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我不覺得我們應當過於擔心
16:30
It's still a pretty small industry.
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這產業規模仍小
16:33
And in fact, bringing science
into the investing world
into the investing world
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事實上，把科學引入投資界
16:39
has improved that world.
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對世界有益
16:41
It's reduced volatility.
It's increased liquidity.
It's increased liquidity.
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可降低變動性，提高流動性
16:45
Spreads are narrower because
people are trading that kind of stuff.
people are trading that kind of stuff.
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因人們交易這東西，擴散範圍變更小
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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我不擔心愛因斯坦出走搞對沖基金
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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(安) 你現在的人生階段是，一方面進出市場
16:58
at the other end of the supply chain --
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但在供應鏈另一端
17:02
you're actually boosting
mathematics across America.
mathematics across America.
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也正促進全美數學發展
17:06
This is your wife, Marilyn.
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這是您的夫人，瑪麗蓮
17:08
You're working on
philanthropic issues together.
philanthropic issues together.
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您倆攜手從事慈善工作
17:13
Tell me about that.
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說說這個
17:14
JS: Well, Marilyn started --
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(西) 嗯，瑪麗蓮－
17:18
there she is up there,
my beautiful wife --
my beautiful wife --
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這就是她，我美麗的老婆
17:21
she started the foundation
about 20 years ago.
about 20 years ago.
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20年前她創立一基金會
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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我說1993, 她說1994
17:27
but it was one of those two years.
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就這兩年間
17:30
(Laughter)
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(笑聲)
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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我們創立基金會以便做慈善工作
17:40
She kept the books, and so on.
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她負責管帳等事
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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6714
那時我們沒太多想法，後來逐漸找到方向——
17:49
which was to focus on math and science,
to focus on basic research.
to focus on basic research.
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投入數學、科學和基礎研究
17:55
And that's what we've done.
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這就是我們在做的
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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6355
6年前我離開文藝復興公司，改在基金會工作
18:04
So that's what we do.
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我們在做這個
18:06
CA: And so Math for America
is basically investing
is basically investing
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2909
(安) Math for America計畫，基本上是投資
18:09
in math teachers around the country,
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全國數學教師
18:11
giving them some extra income,
giving them support and coaching.
giving them support and coaching.
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提供額外收入並給予支持和指導
18:15
And really trying
to make that more effective
to make that more effective
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讓計畫更有效運作
18:18
and make that a calling
to which teachers can aspire.
to which teachers can aspire.
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號召有理想的老師
18:21
JS: Yeah -- instead of beating up
the bad teachers,
the bad teachers,
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(西) 是的——與其懲罰不適任者
18:26
which has created morale problems
all through the educational community,
all through the educational community,
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會拖累教育士氣的人
18:31
in particular in math and science,
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特別在數理科
18:33
we focus on celebrating the good ones
and giving them status.
and giving them status.
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我們著重鼓勵好老師，給他們地位
18:39
Yeah, we give them extra money,
15,000 dollars a year.
15,000 dollars a year.
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是的，我們每年給他們1萬5千美元額外收入
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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目前紐約市有800名公立學校數理教師
18:47
as part of a core.
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是核心成員
18:49
There's a great morale among them.
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他們士氣高昂
18:52
They're staying in the field.
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專注在這領域
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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明年將增至1千人
18:58
of the math and science teachers
in New York [City] public schools.
in New York [City] public schools.
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即紐約公立學校10％的數理教師
19:01
(Applause)
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(掌聲)
19:07
CA: Jim, here's another project
that you've supported philanthropically:
that you've supported philanthropically:
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(安) 你還資助另一計畫
19:11
Research into origins of life, I guess.
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研究生命的起源, 是吧
19:13
What are we looking at here?
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這是什麼?
19:15
JS: Well, I'll save that for a second.
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1882
(西) 先擱一邊
19:17
And then I'll tell you
what you're looking at.
what you're looking at.
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等會再說這圖
19:19
Origins of life is a fascinating question.
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生命源起令人著迷
19:22
How did we get here?
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如何找到答案？
19:25
Well, there are two questions:
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這要處理兩個問題
19:26
One is, what is the route
from geology to biology --
from geology to biology --
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5868
一是，從地質學往生物學
19:32
how did we get here?
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1381
路在哪裡？
19:34
And the other question is,
what did we start with?
what did we start with?
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2364
二是，從哪下手?
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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一路上需哪些材料?
19:39
Those are two very,
very interesting questions.
very interesting questions.
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3061
這兩問題非常有趣
19:43
The first question is a tortuous path
from geology up to RNA
from geology up to RNA
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5834
問題一是條曲折路，從地質學到RNA
19:49
or something like that --
how did that all work?
how did that all work?
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2258
之類的——這如何可能?
19:51
And the other,
what do we have to work with?
what do we have to work with?
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1179913
2388
問題二是需要什麼東西
19:54
Well, more than we think.
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1771
這超乎我們想像
19:56
So what's pictured there
is a star in formation.
is a star in formation.
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4843
所以，這是張星體形成圖
20:01
Now, every year in our Milky Way,
which has 100 billion stars,
which has 100 billion stars,
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3425
在千億星體組成的銀河系裡
20:05
about two new stars are created.
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2495
每年都誕生兩顆星星
20:07
Don't ask me how, but they're created.
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2470
別問過程，反正就誕生了
20:10
And it takes them about a million
years to settle out.
years to settle out.
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3080
接著要百萬年才穩定下來
20:14
So, in steady state,
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2176
型態固定了
20:16
there are about two million stars
in formation at any time.
in formation at any time.
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3848
宇宙形成中的星星隨時都有兩百萬顆
20:20
That one is somewhere
along this settling-down period.
along this settling-down period.
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1208204
3458
那顆星正逐漸穩定
20:24
And there's all this crap
sort of circling around it,
sort of circling around it,
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2936
周遭圍繞著廢棄物
20:27
dust and stuff.
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1215027
1498
塵埃和其他東西
20:29
And it'll form probably a solar system,
or whatever it forms.
or whatever it forms.
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3023
它可能形成太陽系，或者其他什麼
20:32
But here's the thing --
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2176
但關鍵是——
20:34
in this dust that surrounds a forming star
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6348
形成中星體周遭的塵土裡
20:41
have been found, now,
significant organic molecules.
significant organic molecules.
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1229098
6035
現在研究發現重要的有機分子
20:47
Molecules not just like methane,
but formaldehyde and cyanide --
but formaldehyde and cyanide --
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6139
不只有甲烷，還有甲醛、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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6517
這種基礎物質——或者生命的種子
21:01
So, that may be typical.
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2692
這可能是典型過程
21:04
And it may be typical
that planets around the universe
that planets around the universe
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6934
宇宙星體也可能經此典型過程
21:11
start off with some of these
basic building blocks.
basic building blocks.
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1259353
3612
由基礎組成物建立起來
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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2715
這代表到處都存在生命?
21:18
Maybe.
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1364
也許
21:19
But it's a question
of how tortuous this path is
of how tortuous this path is
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4127
但問題在於這過程多麼迂迴曲折
21:24
from those frail beginnings,
those seeds, all the way to life.
those seeds, all the way to life.
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4394
從渺小的起頭, 種子演變成生命
21:28
And most of those seeds
will fall on fallow planets.
will fall on fallow planets.
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5192
這類種子絕大多數落在休眠星體上
21:33
CA: So for you, personally,
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1409
(安) 那麼對你個人來說
21:35
finding an answer to this question
of where we came from,
of where we came from,
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2722
尋找答案，關於你我的起源
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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和源起過程是你想知道的
21:41
JS: Would love to see.
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1786
(西) 我很期待
21:43
And like to know --
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1490
也想知道——
21:44
if that path is tortuous enough,
and so improbable,
and so improbable,
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5170
如果這路如此艱辛、渺茫
21:50
that no matter what you start with,
we could be a singularity.
we could be a singularity.
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4754
那不論源頭是什麼，你我都可能是個奇點
21:55
But on the other hand,
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1152
但另方面
21:56
given all this organic dust
that's floating around,
that's floating around,
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3478
由於懸浮在外的有機塵埃
22:00
we could have lots of friends out there.
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遠方我們也許有很多朋友
22:04
It'd be great to know.
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1161
知道這個感覺很好
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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(安) 幾年前，我有機會和伊隆·馬斯克對談
22:09
and I asked him the secret of his success,
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2837
我請教他成功的秘訣
22:12
and he said taking
physics seriously was it.
physics seriously was it.
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他說好好把物理當回事
22:16
Listening to you, what I hear you saying
is taking math seriously,
is taking math seriously,
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而你所說的，我覺得是把數學當回事
22:20
that has infused your whole life.
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它飽滿了你的人生
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
它給你帶來可觀的收入，可以投資
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
全美、甚至其他地方數千位孩童的未來
22:33
Could it be that science actually works?
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2858
真是這學科的功勞嗎?
22:36
That math actually works?
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1344449
2772
數學真起作用了?
22:39
JS: Well, math certainly works.
Math certainly works.
Math certainly works.
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4372
(西) 數學本身一定是確實有效的
22:43
But this has been fun.
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但有趣的是
22:44
Working with Marilyn and giving it away
has been very enjoyable.
has been very enjoyable.
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和瑪麗蓮同心捐助也真是人生至樂
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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2936
(安) 我發現——這啟發了我
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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1360793
4007
認真做好一門學問，更多好事由此而來
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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感謝你來 TED 分享不凡的人生
22:59
Thank you.
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751
謝謝你
23:00
Jim Simons!
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1101
詹姆士‧西蒙斯
23:01
(Applause)
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(掌聲)
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