How algorithms shape our world | Kevin Slavin

484,251 views ・ 2011-07-21

TED


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譯者: Resa CC 審譯者: Kuo-Yuan Cheng
00:15
This is a photograph
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這是張相片
00:17
by the artist Michael Najjar,
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由藝術家Michael Najjar 拍攝的
00:19
and it's real,
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這張相片是真的
00:21
in the sense that he went there to Argentina
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也就是說,他親自到阿根廷,那座山的所在處
00:23
to take the photo.
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拍攝這張照片。
00:25
But it's also a fiction. There's a lot of work that went into it after that.
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但也可以說,這是張虛構的相片。這張相片的完作花了很多功夫。
00:28
And what he's done
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他對相片動了些手腳:
00:30
is he's actually reshaped, digitally,
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數位化重整
00:32
all of the contours of the mountains
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整片山脈的形體輪廓,
00:34
to follow the vicissitudes of the Dow Jones index.
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使其隨道瓊指數曲線變化。
00:37
So what you see,
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你所看到的
00:39
that precipice, that high precipice with the valley,
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那個峭壁, 那個有處凹陷的高聳峭壁
00:41
is the 2008 financial crisis.
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代表2008年的金融危機。
00:43
The photo was made
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拍攝這張相片時
00:45
when we were deep in the valley over there.
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我們的金融情勢正處於低谷,
00:47
I don't know where we are now.
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不曉得我們現在處於何種形勢。
00:49
This is the Hang Seng index
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這是恆生指數,
00:51
for Hong Kong.
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香港股市價格的重要指標。
00:53
And similar topography.
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(兩張相片)地形相似,
00:55
I wonder why.
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我想知道為什麼
00:57
And this is art. This is metaphor.
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這是藝術;這是種象徵。
01:00
But I think the point is
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但我認為重點是
01:02
that this is metaphor with teeth,
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這個象徵有“牙齒”。
01:04
and it's with those teeth that I want to propose today
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就是因為這些“牙齒”,我今天提議
01:07
that we rethink a little bit
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我們稍微重新思考
01:09
about the role of contemporary math --
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當代數學的角色;
01:12
not just financial math, but math in general.
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不只金融數學,還有普通數學。
01:15
That its transition
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「它」的演變:
01:17
from being something that we extract and derive from the world
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從我們鑽研這個世界,抽絲撥繭而取得的發現
01:20
to something that actually starts to shape it --
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到實際開始形成「它」的重要發現,
01:23
the world around us and the world inside us.
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這含括我們的外在世界和我們內在的世界。
01:26
And it's specifically algorithms,
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說明確些,它是演算法,
01:28
which are basically the math
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基本上,是種數學─
01:30
that computers use to decide stuff.
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─電腦用來測定東西的數學。
01:33
They acquire the sensibility of truth
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演算法掌握高度精確的計量,
01:35
because they repeat over and over again,
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因為它們一而再,再而三的重覆著;
01:37
and they ossify and calcify,
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然後漸漸成型,發展出基本架構
01:40
and they become real.
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然後它們變得實際且可靠。
01:42
And I was thinking about this, of all places,
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我當時正在思考這點, 真是太湊巧了!
01:45
on a transatlantic flight a couple of years ago,
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就在幾年前,橫越大西洋的班機上,
01:48
because I happened to be seated
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因為我的座位碰巧
01:50
next to a Hungarian physicist about my age
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在一位年紀與我相仿的匈牙利物理學家隔壁
01:52
and we were talking
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我們談論關於
01:54
about what life was like during the Cold War
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匈牙利冷戰期間
01:56
for physicists in Hungary.
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物理學家的生活情況。
01:58
And I said, "So what were you doing?"
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我說:「你那時在做什麼?」
02:00
And he said, "Well we were mostly breaking stealth."
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他說:「嗯,我們大多在打擊祕密行動。」
02:02
And I said, "That's a good job. That's interesting.
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「那是個好工作,有趣吧,
02:04
How does that work?"
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那是怎麼運作的?」
02:06
And to understand that,
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要了解那之前
02:08
you have to understand a little bit about how stealth works.
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你必須稍稍了解祕密行動的運作。
02:11
And so -- this is an over-simplification --
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這是個超簡單化的例子,
02:14
but basically, it's not like
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基本上,它不像是
02:16
you can just pass a radar signal
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你可以藉由156噸在天空飛的鋼鐵
02:18
right through 156 tons of steel in the sky.
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傳送雷達信號。
02:21
It's not just going to disappear.
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飛機不會消失不見。
02:24
But if you can take this big, massive thing,
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但若你能將這個龐大、具規模的東西
02:27
and you could turn it into
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變成
02:30
a million little things --
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百萬個小玩意
02:32
something like a flock of birds --
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─像鳥群一樣的東西─
02:34
well then the radar that's looking for that
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那麼雷達偵測到那一群群的小東西
02:36
has to be able to see
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必定會看到
02:38
every flock of birds in the sky.
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在空中有“一群群的鳥”
02:40
And if you're a radar, that's a really bad job.
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若你是一個雷達,事情可就糟了。
02:44
And he said, "Yeah." He said, "But that's if you're a radar.
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他說:「對,但那是,如果你是個雷達。
02:47
So we didn't use a radar;
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所以我們不用雷達,
02:49
we built a black box that was looking for electrical signals,
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我們建造一個黑箱子,用它搜尋電波,
02:52
electronic communication.
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電子通訊。
02:55
And whenever we saw a flock of birds that had electronic communication,
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任何時候,我們發現帶有電子通訊的鳥群,
02:58
we thought, 'Probably has something to do with the Americans.'"
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我們會認為這很可能跟美國人有關。」
03:01
And I said, "Yeah.
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我接著說:「是啊,
03:03
That's good.
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真行,
03:05
So you've effectively negated
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你們成功地消磨了
03:07
60 years of aeronautic research.
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60年的航空學研究心血。
03:09
What's your act two?
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你接著要做什麼?
03:11
What do you do when you grow up?"
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當你長大成人以後,你從事什麼工作?」
03:13
And he said,
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他回答:
03:15
"Well, financial services."
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「嗯,金融服務業。」
03:17
And I said, "Oh."
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我驚呼:「喔!」
03:19
Because those had been in the news lately.
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那一陣子相關報導一直在新聞出現。
03:22
And I said, "How does that work?"
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我問:「進行的如何?」
03:24
And he said, "Well there's 2,000 physicists on Wall Street now,
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他說:「現有2,000名物理學家在華爾街(美國金融中心),
03:26
and I'm one of them."
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我是他們其中一人。」
03:28
And I said, "What's the black box for Wall Street?"
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我接著問:「華爾街用的『黑箱』是什麼?」
03:31
And he said, "It's funny you ask that,
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他回說:「你這樣問很好笑,
03:33
because it's actually called black box trading.
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事實上,人們會稱它為『黑箱交易』
03:36
And it's also sometimes called algo trading,
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有時也稱為
03:38
algorithmic trading."
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「演算法交易」(algorithmic trading 或algo trading)
03:41
And algorithmic trading evolved in part
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「演算法交易」的演進發展,有部分是因為
03:44
because institutional traders have the same problems
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某些機構交易人遇到相同的問題;
03:47
that the United States Air Force had,
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而那問題美國空軍也同樣遭遇到,
03:50
which is that they're moving these positions --
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他們都在「移動這些位置」──
03:53
whether it's Proctor & Gamble or Accenture, whatever --
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不論是寶僑(Proctor&Gamble)或埃森哲(Accenture:管理顧問、技術服務公司)
03:55
they're moving a million shares of something
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他們都在移動一百萬股的東西,
03:57
through the market.
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透過市場交易而進行。
03:59
And if they do that all at once,
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如果他們一次就挪動全部,
04:01
it's like playing poker and going all in right away.
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就像玩撲克牌,把剩下的所有籌碼一次全部壓上,
04:03
You just tip your hand.
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你只會過早洩露底餡;
04:05
And so they have to find a way --
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所以他們必須找到方法
04:07
and they use algorithms to do this --
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─他們用演算法,有系統的操作─
04:09
to break up that big thing
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將龐然大數化整為零
04:11
into a million little transactions.
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成為百萬個小交易。
04:13
And the magic and the horror of that
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恐怖的是這個魔術正是
04:15
is that the same math
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「相同的數學」
04:17
that you use to break up the big thing
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─用來瓦解龐然巨物
04:19
into a million little things
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變成百萬個小東西─
04:21
can be used to find a million little things
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可以用來計算出百萬個零星單位
04:23
and sew them back together
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又將他們統整在一起
04:25
and figure out what's actually happening in the market.
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並推算出實際在市場上發生的事情。
04:27
So if you need to have some image
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如果你立即需要
04:29
of what's happening in the stock market right now,
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一些股市交易的樣貌,
04:32
what you can picture is a bunch of algorithms
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你可以構想到的是,成串的運算法
04:34
that are basically programmed to hide,
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基本上被設計為隱藏不顯示
04:37
and a bunch of algorithms that are programmed to go find them and act.
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和成串的運算法被設計為可搜尋並執行。
04:40
And all of that's great, and it's fine.
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整個設計的真是太棒了,又精確。
04:43
And that's 70 percent
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那是百分之七十的
04:45
of the United States stock market,
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美國股票市場,
04:47
70 percent of the operating system
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這個百分之七十的營運系統
04:49
formerly known as your pension,
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之前堪稱為某些人的“退休金”
04:52
your mortgage.
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某人的“抵押借款”。
04:55
And what could go wrong?
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會有什麼錯呢?
04:57
What could go wrong
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事情出了差池:
04:59
is that a year ago,
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一年前
05:01
nine percent of the entire market just disappears in five minutes,
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整體股市的百分之九突然消失了五分鐘,
05:04
and they called it the Flash Crash of 2:45.
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人們稱之為『瞬間當機2:45』
05:07
All of a sudden, nine percent just goes away,
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突然, 百分之九就這樣不見了,
05:10
and nobody to this day
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直到今天,仍沒有人
05:12
can even agree on what happened
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對發生的事取得一致的意見,
05:14
because nobody ordered it, nobody asked for it.
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因為沒人“下令”當機;沒人自找麻煩。
05:17
Nobody had any control over what was actually happening.
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大家對實際正在發生的事情束手無策
05:20
All they had
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他們只有
05:22
was just a monitor in front of them
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盯著面前的電腦螢幕,
05:24
that had the numbers on it
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電腦螢幕上的數字,
05:26
and just a red button
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和一顆紅色按紐
05:28
that said, "Stop."
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上面寫著: 『停止』
05:30
And that's the thing,
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事情就是這樣,
05:32
is that we're writing things,
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我們正在編寫的「東西」,
05:34
we're writing these things that we can no longer read.
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我們正在編寫這些連自己都看不懂的東西。
05:37
And we've rendered something
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我們已經對「某種東西」投降了,
05:39
illegible,
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某種「難以辨識」的東西。
05:41
and we've lost the sense
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而且我們失去了
05:44
of what's actually happening
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對實際正發生之事的判別力
05:46
in this world that we've made.
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就在我們自己創造的這個世界中,
05:48
And we're starting to make our way.
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況且我們正開始邁向成功。
05:50
There's a company in Boston called Nanex,
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在波士頓有間公司叫Nanex(該公司開發市場數據供給系統),
05:53
and they use math and magic
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他們用數學和魔法
05:55
and I don't know what,
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和我不知道的什麼來的
05:57
and they reach into all the market data
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他們深入研究市場數據資料
05:59
and they find, actually sometimes, some of these algorithms.
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他們確實發現值得重視的東西:某些演算法
06:02
And when they find them they pull them out
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當他們發現這些演算程序,便把它們擷取出來
06:05
and they pin them to the wall like butterflies.
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並將它們像蝴蝶一樣釘在牆上。
06:08
And they do what we've always done
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他們做大家總是會做的事情,
06:10
when confronted with huge amounts of data that we don't understand --
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當面臨龐大又不懂的數據資料時,
06:13
which is that they give them a name
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為其命名
06:15
and a story.
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和揑造故事。
06:17
So this is one that they found,
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這是他們的發現:
06:19
they called the Knife,
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他們稱為『刀』
06:23
the Carnival,
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『嘉年華會』(Carnival)
06:25
the Boston Shuffler,
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『波士頓通勤者』(Boston Shuffler )
06:29
Twilight.
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『暮光』
06:31
And the gag is
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好玩的是
06:33
that, of course, these aren't just running through the market.
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當然,這些不光是存在於金融市場;
06:36
You can find these kinds of things wherever you look,
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你能在任何你看得到的地方,發現這些東西,
06:39
once you learn how to look for them.
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一旦你明白如何找尋到它們(演算法)。
06:41
You can find it here: this book about flies
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從這兒你可以發現:這是本關於蒼蠅的書,
06:44
that you may have been looking at on Amazon.
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你可能已在亞馬遜看到這本書;
06:46
You may have noticed it
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你可能已經注意到
06:48
when its price started at 1.7 million dollars.
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它的價格從一百七十萬元起價時,
06:50
It's out of print -- still ...
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這本書是絶版的......仍然絶版中。
06:52
(Laughter)
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(笑笑)
06:54
If you had bought it at 1.7, it would have been a bargain.
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如果能以一百七十萬的價格買下它是很划算的
06:57
A few hours later, it had gone up
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稍後幾小時,它飆漲至
06:59
to 23.6 million dollars,
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兩千三百六十萬元,
07:01
plus shipping and handling.
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包含運費和手續費。
07:03
And the question is:
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問題是:
07:05
Nobody was buying or selling anything; what was happening?
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這並無產生任何買賣行為;發生了什麼事?
07:07
And you see this behavior on Amazon
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你在亞馬遜見到這樣的行為,
07:09
as surely as you see it on Wall Street.
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確實跟你在華爾街看到的一般。
07:11
And when you see this kind of behavior,
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當你見到這種行為:
07:13
what you see is the evidence
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你所看到的顯然正是
07:15
of algorithms in conflict,
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矛盾的演算程序,
07:17
algorithms locked in loops with each other,
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演算程序被彼此套住,卡在電腦程式回路中;
07:19
without any human oversight,
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沒有任何“人類監管”
07:21
without any adult supervision
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沒有任何“成人監護”
07:24
to say, "Actually, 1.7 million is plenty."
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來告訴你,“其實,一百七十萬已經夠多了!”
07:27
(Laughter)
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(笑笑)
07:30
And as with Amazon, so it is with Netflix.
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如同亞馬遜,Netflix(美國公司,經營線上串流影片)也一樣。
07:33
And so Netflix has gone through
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多年來, Netflix採用過
07:35
several different algorithms over the years.
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好幾個不同的演算程序。
07:37
They started with Cinematch, and they've tried a bunch of others --
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他們從Cinematch(推薦系統軟體)開始,也試了一連串其他的軟體。
07:40
there's Dinosaur Planet; there's Gravity.
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有Dinosaur Planet團隊、Gravity團隊各別研發的推薦系統。
07:42
They're using Pragmatic Chaos now.
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他們現在使用 Pragmatic Chaos研發的系統。
07:44
Pragmatic Chaos is, like all of Netflix algorithms,
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像所有Netflix的運算系統,
07:46
trying to do the same thing.
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Pragmatic Chaos研發的推薦系統,試圖做相同的事。
07:48
It's trying to get a grasp on you,
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它試著去掌控你們,
07:50
on the firmware inside the human skull,
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控制人類頭顱內的思考邏輯,
07:52
so that it can recommend what movie
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以便它能推薦你
07:54
you might want to watch next --
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下次你也許想看的電影─
07:56
which is a very, very difficult problem.
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─這是非常高難度的難題。
07:59
But the difficulty of the problem
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但問題和事實的艱難度
08:01
and the fact that we don't really quite have it down,
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─我們不是真的掌握問題的事實─
08:04
it doesn't take away
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並沒減損
08:06
from the effects Pragmatic Chaos has.
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Pragmatic Chaos的影嚮。
08:08
Pragmatic Chaos, like all Netflix algorithms,
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Pragmatic Chaos,如同所有Netflix運算系統,
08:11
determines, in the end,
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至終裁定
08:13
60 percent
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百分之六十的
08:15
of what movies end up being rented.
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哪些電影最後會被租借。
08:17
So one piece of code
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所以一片程式編碼
08:19
with one idea about you
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─紀錄著你們看片的喜好─
08:22
is responsible for 60 percent of those movies.
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得為百分之六十的電影負責。
08:25
But what if you could rate those movies
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但倘若你能評估這些電影,
08:27
before they get made?
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在電影製作前作預測呢?
08:29
Wouldn't that be handy?
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那不就簡便多了?
08:31
Well, a few data scientists from the U.K. are in Hollywood,
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嗯,在好萊塢,一些來自英國的數據科學家
08:34
and they have "story algorithms" --
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擁有故事情節演算程式系統──
08:36
a company called Epagogix.
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一間公司叫Epagogix(英國一家預測劇本未來票房好壞的公司)
08:38
And you can run your script through there,
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你可以拿劇本請這間公司幫你預測;
08:41
and they can tell you, quantifiably,
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他們會提供你數據:
08:43
that that's a 30 million dollar movie
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那是一部可賣三千萬的電影
08:45
or a 200 million dollar movie.
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或是一部兩億的賣座電影。
08:47
And the thing is, is that this isn't Google.
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事情是......這不是Google;
08:49
This isn't information.
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這不是情報資料;
08:51
These aren't financial stats; this is culture.
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這些不是金融統計;這是文化。
08:53
And what you see here,
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你們在這裡見到的,
08:55
or what you don't really see normally,
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或者說,實際上,你通常不會察覺的
08:57
is that these are the physics of culture.
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是物理文化
09:01
And if these algorithms,
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而且若這些演算系統
09:03
like the algorithms on Wall Street,
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像華爾街的演算系統
09:05
just crashed one day and went awry,
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某天突然當機,出岔子了
09:08
how would we know?
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我們如何會知道.....
09:10
What would it look like?
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那會如何?
09:12
And they're in your house. They're in your house.
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再者,它們就在你的房子內,它們就在你的房子內
09:15
These are two algorithms competing for your living room.
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兩個演算系統在競爭你的客廳。
09:17
These are two different cleaning robots
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兩個不同的清潔機器人
09:19
that have very different ideas about what clean means.
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對乾淨的定義有不同的概念。
09:22
And you can see it
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而且你能從中看到演算程序,
09:24
if you slow it down and attach lights to them,
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如果讓它慢下來,為它們裝上LCD燈的話,你們就能見識到。
09:27
and they're sort of like secret architects in your bedroom.
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而且他們有點像在你卧房內的袐密建築師。
09:30
And the idea that architecture itself
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況且建築學本身的概念
09:33
is somehow subject to algorithmic optimization
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從某種角度而言,是基於演算法的最佳化
09:35
is not far-fetched.
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一點也不牽強喔,
09:37
It's super-real and it's happening around you.
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超真實而且就在存在你週遭。
09:40
You feel it most
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你感受最深的時刻是,
09:42
when you're in a sealed metal box,
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當你在一個密閉的金屬箱子內
09:44
a new-style elevator;
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─一臺新型的電梯─
09:46
they're called destination-control elevators.
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他們被稱為「終點控制電梯」。
09:48
These are the ones where you have to press what floor you're going to go to
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這些是電梯,你可以按鈕到你要去的樓層
09:51
before you get in the elevator.
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在你“進電梯前”按鈕。
09:53
And it uses what's called a bin-packing algorithm.
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它使用所謂的「裝著演算法的盒子」。
09:55
So none of this mishegas
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也就是說,這一點也不異常或瘋狂,
09:57
of letting everybody go into whatever car they want.
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讓每個人選擇進入任何一台電梯。
09:59
Everybody who wants to go to the 10th floor goes into car two,
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要到十樓的人進入二號電梯;
10:01
and everybody who wants to go to the third floor goes into car five.
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要到三樓的人進入五號電梯。
10:04
And the problem with that
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問題是
10:06
is that people freak out.
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人們嚇壞了
10:08
People panic.
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人們驚慌失措。
10:10
And you see why. You see why.
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你看看為什麼......你看看為什麼......
10:12
It's because the elevator
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原因是:
10:14
is missing some important instrumentation, like the buttons.
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電梯缺少了某些種要的儀表,譬如說「按鈕」
10:17
(Laughter)
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(笑笑)
10:19
Like the things that people use.
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人們會使用那個東西。
10:21
All it has
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電梯內只顯示
10:23
is just the number that moves up or down
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上樓或下樓的數字
10:26
and that red button that says, "Stop."
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還有紅色的按鈕,寫著:『停止』
10:29
And this is what we're designing for.
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而這是我們正在設計的,
10:32
We're designing
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我們正在設計
10:34
for this machine dialect.
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這種「機器方言」。
10:36
And how far can you take that? How far can you take it?
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你可以作到什麼樣程度?你可以利用它到何種境界?
10:39
You can take it really, really far.
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你可以“搭乘它(演算法)”至無遠弗界。
10:41
So let me take it back to Wall Street.
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讓我們退回到華爾街,
10:45
Because the algorithms of Wall Street
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因為華爾街的演算系統
10:47
are dependent on one quality above all else,
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仰賴某種性質更勝於一切
10:50
which is speed.
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即「速度」。
10:52
And they operate on milliseconds and microseconds.
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他們以毫秒和微秒運作
10:55
And just to give you a sense of what microseconds are,
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讓你了解什麼是微秒,
10:57
it takes you 500,000 microseconds
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你需要花五十萬微秒
10:59
just to click a mouse.
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去點擊滑鼠;
11:01
But if you're a Wall Street algorithm
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若你是華爾街的演算法
11:03
and you're five microseconds behind,
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而你落後了五微秒,
11:05
you're a loser.
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你就是失敗者。
11:07
So if you were an algorithm,
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所以,倘若你是一個演算法,
11:09
you'd look for an architect like the one that I met in Frankfurt
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你會找一個建築師,像我在法蘭克福市遇到的那位,
11:12
who was hollowing out a skyscraper --
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掏空摩天大樓,
11:14
throwing out all the furniture, all the infrastructure for human use,
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扔掉所有傢俱、所有供人類使用的基礎建設,
11:17
and just running steel on the floors
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只有鋼鐵舖地
11:20
to get ready for the stacks of servers to go in --
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準備好讓大批的伺服器入駐。
11:23
all so an algorithm
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整個如此的演算程序
11:25
could get close to the Internet.
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能使網路通路密切而有效率。
11:28
And you think of the Internet as this kind of distributed system.
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再者,你們認為網路是種分散式系統。
11:31
And of course, it is, but it's distributed from places.
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當然,它是;可是,是從各個定點分散
11:34
In New York, this is where it's distributed from:
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在紐約,這裡是分佈的中心據點:
11:36
the Carrier Hotel
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電信機房(Carrier Hotel)
11:38
located on Hudson Street.
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座落在哈德森街(Hudson Street)
11:40
And this is really where the wires come right up into the city.
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這裡的確是電纜貫穿整座城市的源頭。
11:43
And the reality is that the further away you are from that,
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事實是,離那裡越遠
11:47
you're a few microseconds behind every time.
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每一次就落後數微秒。
11:49
These guys down on Wall Street,
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在華爾街這一帶的“這些傢伙”
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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他們落後八微秒,
11:55
behind all these guys
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落後所有“這些傢伙”
11:57
going into the empty buildings being hollowed out
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這些傢伙進入被掏空的建築物
12:01
up around the Carrier Hotel.
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而這些建築座落接近電信機房的周邊。
12:03
And that's going to keep happening.
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而且那將會持續發生
12:06
We're going to keep hollowing them out,
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─這些建築物將會持續被掏空─
12:08
because you, inch for inch
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因為每一英寸
12:11
and pound for pound and dollar for dollar,
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每一磅和每一(美)元
12:14
none of you could squeeze revenue out of that space
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你們沒人能從那個空間距離強擠出收益
12:17
like the Boston Shuffler could.
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像『波士頓通勤者』那般。
12:20
But if you zoom out,
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但如果縮小地圖
12:22
if you zoom out,
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縮小地圖
12:24
you would see an 825-mile trench
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你會看到825英里(1327.7公里)的溝渠
12:28
between New York City and Chicago
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在紐約和芝加哥之間,
12:30
that's been built over the last few years
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已建立有幾年了
12:32
by a company called Spread Networks.
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由Spread Networks 經營。
12:35
This is a fiber optic cable
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這一道光纖電纜
12:37
that was laid between those two cities
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被設置在兩城市間
12:39
to just be able to traffic one signal
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只為一個信號的傳遞
12:42
37 times faster than you can click a mouse --
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能以37倍速快過點擊滑鼠─
12:45
just for these algorithms,
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─只為了這些演算系統;
12:48
just for the Carnival and the Knife.
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只為了『嘉年華會』和『刀』。
12:51
And when you think about this,
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當你們想著這點時,
12:53
that we're running through the United States
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我們正以炸藥與岩石鋸貫穿、
12:55
with dynamite and rock saws
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損耗美國,
12:58
so that an algorithm can close the deal
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以便一個演算法能快速達成交易
13:00
three microseconds faster,
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─以減少三微秒的速度─
13:03
all for a communications framework
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全都為了一個人類
13:05
that no human will ever know,
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將永不會明瞭的通訊機制
13:09
that's a kind of manifest destiny;
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那是一種顯而易見的定數
13:12
and we'll always look for a new frontier.
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且將永遠不斷地尋找未開拓的新領域。
13:15
Unfortunately, we have our work cut out for us.
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不幸的是,我們必須要完成這個任務。
13:18
This is just theoretical.
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這只是一個理論。
13:20
This is some mathematicians at MIT.
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這是某些在麻省理工學院(MIT)的數學家製作的
13:22
And the truth is I don't really understand
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事實上,我不真的都了解
13:24
a lot of what they're talking about.
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他們在談論些什麼
13:26
It involves light cones and quantum entanglement,
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它涉及光圓錐體和量子糾結
13:29
and I don't really understand any of that.
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我不真的了解那是什麼
13:31
But I can read this map,
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但我會讀這面地圖。
13:33
and what this map says
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這面地圖指示
13:35
is that, if you're trying to make money on the markets where the red dots are,
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如果你試圖在有紅色點點的市場中賺錢
13:38
that's where people are, where the cities are,
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也就是在人們聚集的地方及市鎮重心,
13:40
you're going to have to put the servers where the blue dots are
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你就必須將伺服器設置在藍色點點的地方
13:43
to do that most effectively.
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讓運作效率最大化。
13:45
And the thing that you might have noticed about those blue dots
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你也許注意到那些藍色點點的分佈,
13:48
is that a lot of them are in the middle of the ocean.
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很多藍色點點在海的中央;
13:51
So that's what we'll do: we'll build bubbles or something,
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所以,我們要怎麼做:我們要建立透明圓外罩(bubbles意同泡泡)或什麼來的
13:54
or platforms.
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或者很多平臺。
13:56
We'll actually part the water
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我們將能確實分開海水
13:58
to pull money out of the air,
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將錢從空氣中抽取出,
14:00
because it's a bright future
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未來是光明閃亮的
14:02
if you're an algorithm.
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如果你自己就是一個演算法的話。
14:04
(Laughter)
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(笑笑)
14:06
And it's not the money that's so interesting actually.
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然而,事實上,不是錢有趣
14:09
It's what the money motivates,
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而是錢激發的東西引人入勝─
14:11
that we're actually terraforming
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─我們能確實地地球化(terraforming)
14:13
the Earth itself
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地球本身,
14:15
with this kind of algorithmic efficiency.
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透過演算法具有的最佳效率(能)。
14:17
And in that light,
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根據這點,
14:19
you go back
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咱們回到前面,
14:21
and you look at Michael Najjar's photographs,
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看著Michael Najjar的相片
14:23
and you realize that they're not metaphor, they're prophecy.
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我們領悟到:他們不是象徵;他們是預言
14:26
They're prophecy
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他們預言了
14:28
for the kind of seismic, terrestrial effects
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數學之地震效應、陸地效應
14:32
of the math that we're making.
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即將發生在我們創造出來的數學世界中。
14:34
And the landscape was always made
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而且這風貌過去一直是由自然界和人之間
14:37
by this sort of weird, uneasy collaboration
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不可思議的協作及不易妥協而創作出來的,
14:40
between nature and man.
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是自然界和人之間的對話。
14:43
But now there's this third co-evolutionary force: algorithms --
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但現在有第三股共同演化勢力:演算系統
14:46
the Boston Shuffler, the Carnival.
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『波士頓通勤者』、『嘉年華會』
14:49
And we will have to understand those as nature,
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我們必須明白這些皆為自然。
14:52
and in a way, they are.
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在某種程度上,它們是!
14:54
Thank you.
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謝謝大家
14:56
(Applause)
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(掌聲熱烈)
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