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00:00
Translator: Timothy Covell
Reviewer: Morton Bast
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譯者: Ai-Ying (Erin) Chiang
審譯者: Gina Wang
00:15
So it turns out that mathematics is a very powerful language.
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數學是十分強大的語言
00:18
It has generated considerable insight in physics,
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可幫助人更深入探討物理學
00:21
in biology and economics,
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生物學和經濟學
00:23
but not that much in the humanities and in history.
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但對人文和歷史卻沒有太大幫助
00:26
I think there's a belief that it's just impossible,
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我想大家都認為這是不可能的
00:28
that you cannot quantify the doings of mankind,
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因為我們無法量化人類的行為
00:31
that you cannot measure history.
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也無法計量歷史
00:33
But I don't think that's right.
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但對此,我抱持著不同的看法
00:35
I want to show you a couple of examples why.
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我想舉幾個例子跟大家解釋原因
00:37
So my collaborator Erez and I were considering the following fact:
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我和合作夥伴 Erez 對於以下這件史實是這麼想的
00:40
that two kings separated by centuries
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相隔幾個世紀的兩位君王
00:42
will speak a very different language.
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會說截然不同的語言
00:44
That's a powerful historical force.
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這就是歷史的強大力量
00:46
So the king of England, Alfred the Great,
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因此古代英國國王艾佛烈大帝
00:48
will use a vocabulary and grammar
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使用的字彙和文法
00:50
that is quite different from the king of hip hop, Jay-Z.
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跟當代嘻哈之王 Jay-Z 非常不一樣
00:54
(Laughter)
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(笑聲)
00:55
Now it's just the way it is.
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事實就是這樣
00:57
Language changes over time, and it's a powerful force.
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語言隨時間變化,而且還是一股強大的力量
01:00
So Erez and I wanted to know more about that.
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Erez 和我想更深入了解這件事
01:02
So we paid attention to a particular grammatical rule, past-tense conjugation.
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我們因而注意到一條特別的文法規則:過去式動詞變化
01:06
So you just add "ed" to a verb at the end to signify the past.
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也就是在動詞後加上「ed」來代表過去
01:09
"Today I walk. Yesterday I walked."
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今天我走 (walk),昨天我走了 (walked)
01:11
But some verbs are irregular.
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但是還有一些不規則動詞
01:12
"Yesterday I thought."
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昨天我想 (thought)
01:14
Now what's interesting about that
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而這件事的有趣之處在於
01:15
is irregular verbs between Alfred and Jay-Z have become more regular.
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從艾佛列大帝到 Jay-Z 的年代
不規則動詞是否變得更加規則?
01:19
Like the verb "to wed" that you see here has become regular.
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如你所見,像動詞「結婚」就變得更加規則
01:22
So Erez and I followed the fate of over 100 irregular verbs
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因此 Erez 和我觀察了超過 100 個不規則動詞的演進變化
01:26
through 12 centuries of English language,
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時間橫跨 12 世紀的英文
01:28
and we saw that there's actually a very simple mathematical pattern
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我們發現其中存在著一個很簡單的數學模式
01:31
that captures this complex historical change,
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足以描述這個複雜的歷史演變
01:33
namely, if a verb is 100 times more frequent than another,
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當一動詞的常用程度比其他動詞高出 100 倍時
01:37
it regularizes 10 times slower.
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其規則化的速度就比其他動詞慢 10 倍
01:40
That's a piece of history, but it comes in a mathematical wrapping.
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這就是一個可以用數學概括描述的歷史片段
01:43
Now in some cases math can even help explain,
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而在其他狀況下,數學也有助於解釋歷史
01:47
or propose explanations for, historical forces.
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或者為其提出假說
01:50
So here Steve Pinker and I
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Steve Pinker 和我
01:52
were considering the magnitude of wars during the last two centuries.
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思考兩個世紀以來戰爭規模的變化
01:56
There's actually a well-known regularity to them
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其中其實存在著大家都很孰悉的規則性
01:58
where the number of wars that are 100 times deadlier
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死傷人數高達其它戰爭100 倍的戰爭數量
02:02
is 10 times smaller.
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其實只有 10 分之 1
02:04
So there are 30 wars that are about as deadly as the Six Days War,
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有 30 場戰爭其死亡人數與以阿六日戰爭相同
02:07
but there's only four wars that are 100 times deadlier --
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但死傷人數是其 100 倍的戰爭只有四場
02:10
like World War I.
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例如第一次世界大戰
02:12
So what kind of historical mechanism can produce that?
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而這又是哪種歷史機制造成的呢?
02:15
What's the origin of this?
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其起因又是什麼?
02:17
So Steve and I, through mathematical analysis,
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Steve 和我透過數學分析
02:19
propose that there's actually a very simple phenomenon at the root of this,
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提出了其實起因來自很簡單的現象
02:23
which lies in our brains.
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這種現象就存在大腦之中
02:25
This is a very well-known feature
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是種大家都熟知的特性
02:27
in which we perceive quantities in relative ways --
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也就是我們對「量」的觀感是相對的
02:30
quantities like the intensity of light or the loudness of a sound.
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如光線「強度」或音量「大小」
02:33
For instance, committing 10,000 soldiers to the next battle sounds like a lot.
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舉例來說,派 1 萬名士兵去打下一場仗
感覺好像很多
02:39
It's relatively enormous if you've already committed 1,000 soldiers previously.
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假使先前已派了 1 千名士兵的狀況下
感覺的確是如此
02:42
But it doesn't sound so much,
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但是有時我們並不覺得人數有這麼多
02:44
it's not relatively enough, it won't make a difference
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因為「量」的對比不大,因此無法感受差異
02:47
if you've already committed 100,000 soldiers previously.
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假使先前其實已經派了 10 萬大軍
02:50
So you see that because of the way we perceive quantities,
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所以大家現在可以體會我們對「量」的觀感
02:54
as the war drags on,
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隨著戰爭持續下去
02:55
the number of soldiers committed to it and the casualties
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派遣的士兵和死傷人數
02:59
will increase not linearly --
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將不會呈線性成長
03:00
like 10,000, 11,000, 12,000 --
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趨勢不是 1 萬、1萬1、1 萬2
03:02
but exponentially -- 10,000, later 20,000, later 40,000.
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而會呈指數成長,從 1 萬到 2 萬到 4 萬
03:06
And so that explains this pattern that we've seen before.
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這解釋了我們先前看過的模式
03:09
So here mathematics is able to link a well-known feature of the individual mind
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在此數學可以連結大眾熟知的思維特性
03:15
with a long-term historical pattern
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以及長期歷史模式
03:18
that unfolds over centuries and across continents.
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這種模式跨越幾個世紀橫跨幾大洲慢慢成形
03:21
So these types of examples, today there are just a few of them,
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這種例子即便在今日仍然屈指可數
03:25
but I think in the next decade they will become commonplace.
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但我認為十年之後便將成為常態
03:27
The reason for that is that the historical record
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原因是歷史記錄
03:30
is becoming digitized at a very fast pace.
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以很快的速度數位化
03:32
So there's about 130 million books
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有史以來
03:35
that have been written since the dawn of time.
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人類已經寫了13 億本書
03:37
Companies like Google have digitized many of them --
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Google 這類公司已經將其中的一大部分數位化
03:40
above 20 million actually.
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實際數量超過 2 億本
03:41
And when the stuff of history is available in digital form,
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因此當歷史記錄被數位化後
03:45
it makes it possible for a mathematical analysis
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就能拿來做數學分析
03:47
to very quickly and very conveniently
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讓我們能很快又便利的
03:50
review trends in our history and our culture.
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檢視歷史和文化趨勢
03:52
So I think in the next decade,
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因此我認為接下來十年
03:55
the sciences and the humanities will come closer together
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科學和人文將更緊密結合
03:58
to be able to answer deep questions about mankind.
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而且能夠回答一些與人類相關的深層問題
04:01
And I think that mathematics will be a very powerful language to do that.
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我更認為數學這種強大語言將能做到此點
04:05
It will be able to reveal new trends in our history,
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數學將能揭開歷史的新趨勢
04:08
sometimes to explain them,
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有時加以解釋
04:10
and maybe even in the future to predict what's going to happen.
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最後甚至也有可能可以預測未來
04:13
Thank you very much.
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謝謝各位
04:15
(Applause)
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(掌聲)
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