How algorithms shape our world | Kevin Slavin

484,372 views ・ 2011-07-21

TED


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翻译人员: Felix Chen 校对人员: Chunxiang Qian
00:15
This is a photograph
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这是一张由艺术家
00:17
by the artist Michael Najjar,
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迈克尔·纳贾尔拍摄的照片,
00:19
and it's real,
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这是真实的,
00:21
in the sense that he went there to Argentina
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他在阿根廷
00:23
to take the photo.
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拍摄的这张照片。
00:25
But it's also a fiction. There's a lot of work that went into it after that.
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但其中也有虚构的成分。在拍摄后还做了许多工作。
00:28
And what he's done
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他所做的是,
00:30
is he's actually reshaped, digitally,
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他实际上数字化地重塑了
00:32
all of the contours of the mountains
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所有山峰的轮廓
00:34
to follow the vicissitudes of the Dow Jones index.
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以遵循道琼斯指数的变化。
00:37
So what you see,
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因此各位所看到的,
00:39
that precipice, that high precipice with the valley,
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这悬崖,深沟险壑,
00:41
is the 2008 financial crisis.
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是2008年的金融危机。
00:43
The photo was made
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这张照片是在我们
00:45
when we were deep in the valley over there.
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陷入深谷时制作的。
00:47
I don't know where we are now.
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我不知道我们现在在哪儿。
00:49
This is the Hang Seng index
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这是为香港恒生指数
00:51
for Hong Kong.
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制作的。
00:53
And similar topography.
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类似的形状。
00:55
I wonder why.
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我不知道为什么。
00:57
And this is art. This is metaphor.
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这是艺术。这是隐喻。
01:00
But I think the point is
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但我认为这是个
01:02
that this is metaphor with teeth,
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带着牙齿会咬人的隐喻。
01:04
and it's with those teeth that I want to propose today
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带着这些齿状的线条,今天我建议
01:07
that we rethink a little bit
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我们重新思考一下
01:09
about the role of contemporary math --
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当代数学的角色 --
01:12
not just financial math, but math in general.
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不仅是金融数学,还有一般数学。
01:15
That its transition
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它由
01:17
from being something that we extract and derive from the world
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我们从世界中提炼出的某种事物
01:20
to something that actually starts to shape it --
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转变为事实上开始塑造世界的事物 --
01:23
the world around us and the world inside us.
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我们周围的世界和我们内心的世界。
01:26
And it's specifically algorithms,
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特别是算法,
01:28
which are basically the math
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它基本上是
01:30
that computers use to decide stuff.
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计算机用于决策的数学。
01:33
They acquire the sensibility of truth
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它们具有了真理的敏感性,
01:35
because they repeat over and over again,
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因为它们会不断地重复。
01:37
and they ossify and calcify,
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它们固化下来,
01:40
and they become real.
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变得真实。
01:42
And I was thinking about this, of all places,
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我随时随地都在思考这些,
01:45
on a transatlantic flight a couple of years ago,
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数年前在一次跨越大西洋的航班上,
01:48
because I happened to be seated
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因为我恰好坐在一名
01:50
next to a Hungarian physicist about my age
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与我年纪相仿的匈牙利物理学家旁边,
01:52
and we were talking
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我们谈论
01:54
about what life was like during the Cold War
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冷战期间在匈牙利的
01:56
for physicists in Hungary.
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物理学家生活是什么样的。
01:58
And I said, "So what were you doing?"
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我说道,“你们都做些什么?”
02:00
And he said, "Well we were mostly breaking stealth."
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他回答道,“嗯,我们主要是在破解隐形飞机。”
02:02
And I said, "That's a good job. That's interesting.
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我说,“不错。很有趣。
02:04
How does that work?"
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怎么做呢?”
02:06
And to understand that,
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要理解这个,
02:08
you have to understand a little bit about how stealth works.
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要先对隐形飞机如何工作有点了解。
02:11
And so -- this is an over-simplification --
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因此 -- 有点过于简化 --
02:14
but basically, it's not like
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但基本上,不是
02:16
you can just pass a radar signal
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仅仅让156吨的钢铁
02:18
right through 156 tons of steel in the sky.
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在天空中穿过雷达信号就完事了。
02:21
It's not just going to disappear.
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它不会就这么消失了。
02:24
But if you can take this big, massive thing,
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但如果能把这巨大的东西
02:27
and you could turn it into
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变成
02:30
a million little things --
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上百万个小东西 --
02:32
something like a flock of birds --
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有点像一群鸟 --
02:34
well then the radar that's looking for that
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那么寻找目标的雷达
02:36
has to be able to see
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能看到天空中的
02:38
every flock of birds in the sky.
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每个鸟群。
02:40
And if you're a radar, that's a really bad job.
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如果你是雷达,这真是个糟糕的工作。
02:44
And he said, "Yeah." He said, "But that's if you're a radar.
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他说道,“是的。”他说,“如果你是雷达的话。
02:47
So we didn't use a radar;
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所以我们不用雷达;
02:49
we built a black box that was looking for electrical signals,
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我们造了个黑盒子来探测电子信号,
02:52
electronic communication.
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电子通讯。
02:55
And whenever we saw a flock of birds that had electronic communication,
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当我们看到有电子通讯的一群鸟时,
02:58
we thought, 'Probably has something to do with the Americans.'"
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我们就认为这可能与美国有关。
03:01
And I said, "Yeah.
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我说,”是的。
03:03
That's good.
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这很不错。
03:05
So you've effectively negated
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你们有效地让60年的
03:07
60 years of aeronautic research.
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航空研究无效了。
03:09
What's your act two?
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你的下一步是什么?
03:11
What do you do when you grow up?"
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你长大后,你想要做什么?“
03:13
And he said,
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他说,
03:15
"Well, financial services."
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”嗯,金融服务。“
03:17
And I said, "Oh."
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我说,”哦。“
03:19
Because those had been in the news lately.
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因为这已经在最近的新闻里了。
03:22
And I said, "How does that work?"
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我说,”这工作怎么样?“
03:24
And he said, "Well there's 2,000 physicists on Wall Street now,
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他说,”嗯,现在有2000名物理学家在华尔街工作,
03:26
and I'm one of them."
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我是其中之一。“
03:28
And I said, "What's the black box for Wall Street?"
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我说,”华尔街的黑盒子是什么?“
03:31
And he said, "It's funny you ask that,
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他说,”你问的这个很有趣,
03:33
because it's actually called black box trading.
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因为这实际上被称为暗箱交易。
03:36
And it's also sometimes called algo trading,
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也被称为算法交易,
03:38
algorithmic trading."
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算法交易。“
03:41
And algorithmic trading evolved in part
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算法交易的演化某种程度上
03:44
because institutional traders have the same problems
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是因为机构交易员碰到了
03:47
that the United States Air Force had,
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与美国空军一样的问题,
03:50
which is that they're moving these positions --
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他们要移动这些点 --
03:53
whether it's Proctor & Gamble or Accenture, whatever --
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不管是宝洁还是埃森哲,不管是什么 --
03:55
they're moving a million shares of something
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他们在市场上交易上百万的
03:57
through the market.
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某公司的股票。
03:59
And if they do that all at once,
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如果他们一次移动全部,
04:01
it's like playing poker and going all in right away.
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有点像象玩扑克室,所有筹码全部下注。
04:03
You just tip your hand.
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你就露了底牌。
04:05
And so they have to find a way --
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因此他们不得不找一个方法 --
04:07
and they use algorithms to do this --
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他们用算法来完成这项工作 --
04:09
to break up that big thing
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把巨大的交易
04:11
into a million little transactions.
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转化为上百万次小的交易。
04:13
And the magic and the horror of that
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其中的神奇和可怕之处是
04:15
is that the same math
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你用于把庞然大物分解成
04:17
that you use to break up the big thing
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上百万份的数学方法
04:19
into a million little things
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也可以用于
04:21
can be used to find a million little things
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找到上百万个小东西,
04:23
and sew them back together
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重新拼接起来
04:25
and figure out what's actually happening in the market.
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并算出市场上到底发生了什么。
04:27
So if you need to have some image
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因此如果你需要一些
04:29
of what's happening in the stock market right now,
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描绘了当前市场中的情景的图像,
04:32
what you can picture is a bunch of algorithms
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你能呈现出的是一组
04:34
that are basically programmed to hide,
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被设定为隐藏的算法,
04:37
and a bunch of algorithms that are programmed to go find them and act.
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一组被设定为可找到并执行的算法。
04:40
And all of that's great, and it's fine.
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这一切太伟大了,太棒了。
04:43
And that's 70 percent
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美国股票市场
04:45
of the United States stock market,
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中的百分之70,
04:47
70 percent of the operating system
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操作系统的百分之70
04:49
formerly known as your pension,
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前身为退休金,
04:52
your mortgage.
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按揭。
04:55
And what could go wrong?
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什么可能出问题?
04:57
What could go wrong
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一年前
04:59
is that a year ago,
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出的问题是
05:01
nine percent of the entire market just disappears in five minutes,
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整个市场的百分之九消失了五分钟,
05:04
and they called it the Flash Crash of 2:45.
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这被称为“2:45的瞬间崩溃”。
05:07
All of a sudden, nine percent just goes away,
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突然之间,百分之九就消失了,
05:10
and nobody to this day
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直到今天大家
05:12
can even agree on what happened
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对发生了什么还不能达成一致,
05:14
because nobody ordered it, nobody asked for it.
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因为没人下命令,没人要这么做。
05:17
Nobody had any control over what was actually happening.
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对那天所发生的大家束手无策。
05:20
All they had
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他们就是
05:22
was just a monitor in front of them
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看着面前的屏幕
05:24
that had the numbers on it
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上的数字
05:26
and just a red button
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和一个红色按钮
05:28
that said, "Stop."
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上面写着,“停。”
05:30
And that's the thing,
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事情就是这样
05:32
is that we're writing things,
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这就是我们正在编写的东西,
05:34
we're writing these things that we can no longer read.
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我们在编写我们读不懂的东西。
05:37
And we've rendered something
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我们把一些事情变得
05:39
illegible,
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难以理解。
05:41
and we've lost the sense
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我们已经对
05:44
of what's actually happening
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这个我们创造的世界中
05:46
in this world that we've made.
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正在发生的事情失去理解能力。
05:48
And we're starting to make our way.
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我们开始前进。
05:50
There's a company in Boston called Nanex,
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在波士顿有个名为Nanex的公司,
05:53
and they use math and magic
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他们运用数学和魔法
05:55
and I don't know what,
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和我不知道是什么的东西,
05:57
and they reach into all the market data
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他们深入研究所有他们能找到的
05:59
and they find, actually sometimes, some of these algorithms.
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市场数据,实际上有时候是一些算法。
06:02
And when they find them they pull them out
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当他们找到这些数据时,就把数据抽取出来
06:05
and they pin them to the wall like butterflies.
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像蝴蝶似的把它们钉在墙上。
06:08
And they do what we've always done
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他们所做的也是我们在
06:10
when confronted with huge amounts of data that we don't understand --
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面对大量我们无法理解的数据时所做的 --
06:13
which is that they give them a name
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给它们一个名字
06:15
and a story.
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和一个故事。
06:17
So this is one that they found,
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这就是他们找的一个,
06:19
they called the Knife,
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他们称之为‘小刀’,
06:23
the Carnival,
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‘嘉年华’,
06:25
the Boston Shuffler,
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‘波士顿洗牌者’,
06:29
Twilight.
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暮光。
06:31
And the gag is
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有意思的是
06:33
that, of course, these aren't just running through the market.
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这不仅存在于股票市场上。
06:36
You can find these kinds of things wherever you look,
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一旦你知道如何寻找它们,
06:39
once you learn how to look for them.
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无论在哪儿你都能找到这类东西,
06:41
You can find it here: this book about flies
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你能在这儿找到它:这本关于苍蝇的书
06:44
that you may have been looking at on Amazon.
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你可能在亚马逊上看到过这本书。
06:46
You may have noticed it
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你或许已经注意到
06:48
when its price started at 1.7 million dollars.
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它的价格是一百七十万美元。
06:50
It's out of print -- still ...
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绝版 -- 仍然是绝版...
06:52
(Laughter)
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(笑声)
06:54
If you had bought it at 1.7, it would have been a bargain.
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如果你在一百七十万美元是购买了它,那还算便宜的。
06:57
A few hours later, it had gone up
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数小时后,它涨到了
06:59
to 23.6 million dollars,
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两千三百六十万美元,
07:01
plus shipping and handling.
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含运费和手续费。
07:03
And the question is:
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问题是:
07:05
Nobody was buying or selling anything; what was happening?
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没有人购买或销售任何东西;发生了什么?
07:07
And you see this behavior on Amazon
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你在亚马逊看到的这一行为
07:09
as surely as you see it on Wall Street.
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毫无疑问与在华尔街看到的一样。
07:11
And when you see this kind of behavior,
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当你看到这类行为时,
07:13
what you see is the evidence
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你所看到的就是
07:15
of algorithms in conflict,
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算法冲突的证据,
07:17
algorithms locked in loops with each other,
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算法相互锁定,
07:19
without any human oversight,
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没有人类的监管,
07:21
without any adult supervision
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没有任何成熟的监督
07:24
to say, "Actually, 1.7 million is plenty."
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说,“实际上,一百七十万美元是很大一笔了。”
07:27
(Laughter)
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(笑声)
07:30
And as with Amazon, so it is with Netflix.
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与亚马逊一样,Netflix也有这样的问题。
07:33
And so Netflix has gone through
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因此Netflix多年来已经
07:35
several different algorithms over the years.
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经历了若干不同算法。
07:37
They started with Cinematch, and they've tried a bunch of others --
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他们开始用的是Cinematch,后来又尝试了一些其他的。
07:40
there's Dinosaur Planet; there's Gravity.
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有Dinosaur Planet,Gravity。
07:42
They're using Pragmatic Chaos now.
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现在他们在使用Pragmatic Chaos。
07:44
Pragmatic Chaos is, like all of Netflix algorithms,
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Pragmatic Chaos,与所有Netflix算法相同,
07:46
trying to do the same thing.
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试着做同样的事情。
07:48
It's trying to get a grasp on you,
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它试图把握住你,
07:50
on the firmware inside the human skull,
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掌控人类头骨内的固件,
07:52
so that it can recommend what movie
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这样它就能向你推荐
07:54
you might want to watch next --
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你可能想看的电影 --
07:56
which is a very, very difficult problem.
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这是个非常非常困难的事情。
07:59
But the difficulty of the problem
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但问题和事实的难点
08:01
and the fact that we don't really quite have it down,
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在于我们没有真的掌握它,
08:04
it doesn't take away
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它没有消除
08:06
from the effects Pragmatic Chaos has.
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Pragmatic Chaos的影响。
08:08
Pragmatic Chaos, like all Netflix algorithms,
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Pragmatic Chaos,如同Netflix的所有算法,
08:11
determines, in the end,
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最后决定了
08:13
60 percent
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百分之60
08:15
of what movies end up being rented.
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最终被租用的电影。
08:17
So one piece of code
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因此一段带有
08:19
with one idea about you
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你的看法的代码
08:22
is responsible for 60 percent of those movies.
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对百分之60的电影负责。
08:25
But what if you could rate those movies
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但如果你在这些电影制作之前
08:27
before they get made?
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对它们进行评价会怎样?
08:29
Wouldn't that be handy?
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这样岂不是很方便?
08:31
Well, a few data scientists from the U.K. are in Hollywood,
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嗯,一些来自英国的数据科学家在好莱坞,
08:34
and they have "story algorithms" --
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他们有故事算法 --
08:36
a company called Epagogix.
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一家名为Epagogix的公司。
08:38
And you can run your script through there,
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你可以向他们提供你的剧本,
08:41
and they can tell you, quantifiably,
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他们能量化地告诉你
08:43
that that's a 30 million dollar movie
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这是个三千万美元票房的电影
08:45
or a 200 million dollar movie.
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或是个两亿美元票房的电影。
08:47
And the thing is, is that this isn't Google.
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这不是Google。
08:49
This isn't information.
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不是信息。
08:51
These aren't financial stats; this is culture.
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不是金融统计;这是文化。
08:53
And what you see here,
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你在这儿看到的
08:55
or what you don't really see normally,
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或你没有真正察觉的,
08:57
is that these are the physics of culture.
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是文化的物理学。
09:01
And if these algorithms,
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如果这些算法,
09:03
like the algorithms on Wall Street,
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象华尔街中的算法,
09:05
just crashed one day and went awry,
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某天崩溃了出错了,
09:08
how would we know?
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我们怎么知道,
09:10
What would it look like?
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那会是什么样子?
09:12
And they're in your house. They're in your house.
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它们在你的屋子里,它们在你的屋子里。
09:15
These are two algorithms competing for your living room.
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有两种算法在争夺你的客厅。
09:17
These are two different cleaning robots
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有两种不同的清洁机器人
09:19
that have very different ideas about what clean means.
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它们对清洁的含义有着非常不同的理解。
09:22
And you can see it
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如果你让它慢下来,在它上面放上灯光
09:24
if you slow it down and attach lights to them,
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你就能够看到。
09:27
and they're sort of like secret architects in your bedroom.
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有点像你卧室里的秘密建筑师。
09:30
And the idea that architecture itself
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建筑本身
09:33
is somehow subject to algorithmic optimization
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某种程度上服从算法优化的想法
09:35
is not far-fetched.
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并非牵强。
09:37
It's super-real and it's happening around you.
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这是超现实,它就发生在你周围。
09:40
You feel it most
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当你在一个密封的金属盒子里时,
09:42
when you're in a sealed metal box,
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一种被称为目标控制电梯的
09:44
a new-style elevator;
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新式电梯,
09:46
they're called destination-control elevators.
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最能感受到它。
09:48
These are the ones where you have to press what floor you're going to go to
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在你进入电梯之前你要按下
09:51
before you get in the elevator.
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你所要去的楼层的按钮。
09:53
And it uses what's called a bin-packing algorithm.
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它使用装箱算法。
09:55
So none of this mishegas
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因此让每个人进入
09:57
of letting everybody go into whatever car they want.
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他们想进的电梯一点也不混乱。
09:59
Everybody who wants to go to the 10th floor goes into car two,
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想去10楼的人进入二号电梯,
10:01
and everybody who wants to go to the third floor goes into car five.
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想去三层的人进入五号电梯。
10:04
And the problem with that
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问题是
10:06
is that people freak out.
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人们吓坏了。
10:08
People panic.
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人们抓狂了。
10:10
And you see why. You see why.
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你知道为什么。你知道为什么。
10:12
It's because the elevator
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因为电梯
10:14
is missing some important instrumentation, like the buttons.
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缺少了些重要的东西,比如按钮。
10:17
(Laughter)
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(笑声)
10:19
Like the things that people use.
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正如人们使用的电梯。
10:21
All it has
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都有
10:23
is just the number that moves up or down
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标明向上或向下的数字
10:26
and that red button that says, "Stop."
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还有一个红色按钮,上写着,“停。”
10:29
And this is what we're designing for.
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这就是我们正在设计的。
10:32
We're designing
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我们正在设计
10:34
for this machine dialect.
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这种机器方言。
10:36
And how far can you take that? How far can you take it?
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能做到什么程度?能用它做到何种境界?
10:39
You can take it really, really far.
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用它可以走得很远很远。
10:41
So let me take it back to Wall Street.
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让我们回到华尔街。
10:45
Because the algorithms of Wall Street
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因为华尔街的算法
10:47
are dependent on one quality above all else,
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依赖于一个高于一切的特质,
10:50
which is speed.
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速度。
10:52
And they operate on milliseconds and microseconds.
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它们的运行时间以毫秒和微妙计算。
10:55
And just to give you a sense of what microseconds are,
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让你们对微秒有点感觉,
10:57
it takes you 500,000 microseconds
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点击一下鼠标
10:59
just to click a mouse.
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要花50万微秒的时间。
11:01
But if you're a Wall Street algorithm
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但如果你是一个华尔街的算法
11:03
and you're five microseconds behind,
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落后5微秒,
11:05
you're a loser.
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你就是失败者。
11:07
So if you were an algorithm,
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因此,如果你是一个算法,
11:09
you'd look for an architect like the one that I met in Frankfurt
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你得寻找一个像我在法兰克福所遇的那样的建筑师
11:12
who was hollowing out a skyscraper --
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把整个摩天大楼掏空 --
11:14
throwing out all the furniture, all the infrastructure for human use,
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扔掉所有的家具和人类使用的基础设施,
11:17
and just running steel on the floors
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仅用刚才铺至地面,
11:20
to get ready for the stacks of servers to go in --
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准备好大批的服务器入驻 --
11:23
all so an algorithm
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整个算法
11:25
could get close to the Internet.
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都能快速连入互联网。
11:28
And you think of the Internet as this kind of distributed system.
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把互联网看成一种分布式系统。
11:31
And of course, it is, but it's distributed from places.
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当然,它就是,但分布于不同地点。
11:34
In New York, this is where it's distributed from:
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在纽约,它分布在:
11:36
the Carrier Hotel
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位于哈德逊大街的
11:38
located on Hudson Street.
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电信酒店。
11:40
And this is really where the wires come right up into the city.
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这是线缆真正进入这座城市的地方。
11:43
And the reality is that the further away you are from that,
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事实上你距离这地方越远,
11:47
you're a few microseconds behind every time.
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每次都会落后几微秒。
11:49
These guys down on Wall Street,
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在华尔街上的这些家伙,
11:51
Marco Polo and Cherokee Nation,
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Marco Polo和Cherokee Nation,
11:53
they're eight microseconds
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他们比这些
11:55
behind all these guys
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在电信酒店周围的
11:57
going into the empty buildings being hollowed out
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被掏空了的大厦里的家伙
12:01
up around the Carrier Hotel.
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要落后八微秒。
12:03
And that's going to keep happening.
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这在不断发生。
12:06
We're going to keep hollowing them out,
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我们要把它们不断掏空,
12:08
because you, inch for inch
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因为你,每一英寸
12:11
and pound for pound and dollar for dollar,
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每一磅,每一美元,
12:14
none of you could squeeze revenue out of that space
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没人能像‘波士顿洗牌者’那样
12:17
like the Boston Shuffler could.
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从中榨取收益。
12:20
But if you zoom out,
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但如果你缩小地图,
12:22
if you zoom out,
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如果你缩小地图,
12:24
you would see an 825-mile trench
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你会看到一条长达825英里的
12:28
between New York City and Chicago
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位于纽约城和芝加哥之间的沟渠,
12:30
that's been built over the last few years
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它在过去几年中
12:32
by a company called Spread Networks.
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由一家名为Spread Networks的公司建造。
12:35
This is a fiber optic cable
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这条
12:37
that was laid between those two cities
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两座城市间的光缆
12:39
to just be able to traffic one signal
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就是为了以比你点击鼠标
12:42
37 times faster than you can click a mouse --
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快37倍的速度传输信号 --
12:45
just for these algorithms,
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就是为了这些算法,
12:48
just for the Carnival and the Knife.
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就是为了‘嘉年华’和‘小刀’。
12:51
And when you think about this,
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你想一想,
12:53
that we're running through the United States
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我们正在用炸药和岩石锯
12:55
with dynamite and rock saws
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穿过美国,
12:58
so that an algorithm can close the deal
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只是为了一个算法
13:00
three microseconds faster,
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能快三微秒完成交易,
13:03
all for a communications framework
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都是为了一个没人会知道的
13:05
that no human will ever know,
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通信框架,
13:09
that's a kind of manifest destiny;
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这有点命运天定论
13:12
and we'll always look for a new frontier.
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并总是在寻找新的领域。
13:15
Unfortunately, we have our work cut out for us.
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不幸地是,我们面前困难重重。
13:18
This is just theoretical.
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这仅仅是理论上的。
13:20
This is some mathematicians at MIT.
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这是MIT的一些数学家制作的。
13:22
And the truth is I don't really understand
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我并不太明白
13:24
a lot of what they're talking about.
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他们所谈论的。
13:26
It involves light cones and quantum entanglement,
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它涉及光锥体和量子纠缠,
13:29
and I don't really understand any of that.
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这些我真的都不太明白。
13:31
But I can read this map,
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但我能看明白这张地图。
13:33
and what this map says
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这张地图表明
13:35
is that, if you're trying to make money on the markets where the red dots are,
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如果你要在市场上赚钱,那些红点所在位置,
13:38
that's where people are, where the cities are,
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也是人所在位置,也是城市所在位置,
13:40
you're going to have to put the servers where the blue dots are
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就要把服务器放到蓝点所在位置
13:43
to do that most effectively.
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这样最有效率。
13:45
And the thing that you might have noticed about those blue dots
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各位也许已经注意到这些蓝点
13:48
is that a lot of them are in the middle of the ocean.
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许多都在大洋中。
13:51
So that's what we'll do: we'll build bubbles or something,
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那么我们要做的是,建造一些气泡之类的东西,
13:54
or platforms.
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或者是平台。
13:56
We'll actually part the water
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我们们确实能分离水,
13:58
to pull money out of the air,
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从空气中挖掘财富,
14:00
because it's a bright future
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因为这很有前途,
14:02
if you're an algorithm.
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如果你是一个算法的话。
14:04
(Laughter)
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(笑声)
14:06
And it's not the money that's so interesting actually.
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实际上有意思的不是钱。
14:09
It's what the money motivates,
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而是钱所激发的东西。
14:11
that we're actually terraforming
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我们实际上在用
14:13
the Earth itself
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这种算法的效率
14:15
with this kind of algorithmic efficiency.
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在改造地球本身。
14:17
And in that light,
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根据这点,
14:19
you go back
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各位回去看看
14:21
and you look at Michael Najjar's photographs,
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迈克尔·纳贾尔的照片,
14:23
and you realize that they're not metaphor, they're prophecy.
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会领悟到它们不是隐喻,而是预言。
14:26
They're prophecy
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它们是
14:28
for the kind of seismic, terrestrial effects
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我们正在数学上掀起的
14:32
of the math that we're making.
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那种地震效应的预言。
14:34
And the landscape was always made
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风景总是由
14:37
by this sort of weird, uneasy collaboration
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自然和人类之间的这种
14:40
between nature and man.
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怪异不安的协作产生的。
14:43
But now there's this third co-evolutionary force: algorithms --
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但现在有这些第三方协同进化力量:算法 --
14:46
the Boston Shuffler, the Carnival.
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‘波士顿洗牌者‘,’嘉年华’。
14:49
And we will have to understand those as nature,
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我们将不得不将这些视为自然。
14:52
and in a way, they are.
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某种程度上,它们是的。
14:54
Thank you.
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谢谢。
14:56
(Applause)
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(掌声)
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