TEDxBoston 2011
Jean-Baptiste Michel + Erez Lieberman Aiden: What we learned from 5 million books
從五百萬本書學到的事
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你是否使用過Google實驗室開發的Ngram瀏覽器?這是一款吸引人的工具，能讓你從跨世紀以來五百萬本書的資料庫中搜尋字詞和想法。Erez Lieberman Aiden和Jean-Baptiste Michel將為我們展示這款工具如何運作，以及一些我們能從這五千億字中學到的一些驚喜發現。
Jean-Baptiste Michel - Data researcher
Jean-Baptiste Michel looks at how we can use large volumes of data to better understand our world. Full bioErez Lieberman Aiden - Researcher
Erez Lieberman Aiden pursues a broad range of research interests, spanning genomics, linguistics, mathematics ... Full bio
Jean-Baptiste Michel looks at how we can use large volumes of data to better understand our world. Full bioErez Lieberman Aiden - Researcher
Erez Lieberman Aiden pursues a broad range of research interests, spanning genomics, linguistics, mathematics ... Full bio
Double-click the English transcript below to play the video.
00:15
Erez Lieberman Aiden: Everyone knows
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Erez Lieberman Aiden：大家都知道
00:17
that a picture is worth a thousand words.
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一張圖勝過千言萬語
00:22
But we at Harvard
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但我們在哈佛時
00:24
were wondering if this was really true.
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卻在思考這道理是否真是如此
00:27
(Laughter)
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(笑聲)
00:29
So we assembled a team of experts,
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所以我們由來自哈佛大學
00:33
spanning Harvard, MIT,
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麻省理工學院
00:35
The American Heritage Dictionary, The Encyclopedia Britannica
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美國傳統英語詞典，大英百科全書
00:38
and even our proud sponsors,
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甚至我們偉大的贊助商─Google的專家們
00:40
the Google.
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組成一個團隊
00:43
And we cogitated about this
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我們花了四年的時間
00:45
for about four years.
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在思考這個問題
00:47
And we came to a startling conclusion.
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然後我們得到了一個驚人的結論
00:52
Ladies and gentlemen, a picture is not worth a thousand words.
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女士先生們，一張圖片其實不只勝過千言萬語
00:55
In fact, we found some pictures
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事實上，我們發現某些圖片
00:57
that are worth 500 billion words.
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更是勝過五千億個字
01:02
Jean-Baptiste Michel: So how did we get to this conclusion?
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Jean-Baptiste Michel：我們是如何得出這項結論的呢？
01:04
So Erez and I were thinking about ways
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Erez和我思考了不同的方式
01:06
to get a big picture of human culture
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想更加了解人類文化
01:08
and human history: change over time.
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以及人類歷史從古到今的變化的全景
01:11
So many books actually have been written over the years.
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事實上，多年來已經出版了許多書籍。
01:13
So we were thinking, well the best way to learn from them
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所以我們認為最好的學習方式
01:15
is to read all of these millions of books.
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就是將這上百萬的書全讀過一遍
01:17
Now of course, if there's a scale for how awesome that is,
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如果能有一個尺規來說明此舉的驚人程度
01:20
that has to rank extremely, extremely high.
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這將會相當驚人
01:23
Now the problem is there's an X-axis for that,
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但問題是這裡的X軸
01:25
which is the practical axis.
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是表示實用程度
01:27
This is very, very low.
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這相當不實用
01:29
(Applause)
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(掌聲)
01:32
Now people tend to use an alternative approach,
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現在人們希望用別的方式
01:35
which is to take a few sources and read them very carefully.
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可以讀少一點書，但讀得非常仔細
01:37
This is extremely practical, but not so awesome.
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這會相當實用，但這一點都不吸引人
01:39
What you really want to do
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我們真正想做的是
01:42
is to get to the awesome yet practical part of this space.
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要用一種吸引人且實用的方法來閱讀這些書
01:45
So it turns out there was a company across the river called Google
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所以在河的對岸有間公司叫做Google
01:48
who had started a digitization project a few years back
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他們幾年之前開始了一項數字化計畫
01:50
that might just enable this approach.
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這項計畫讓我們能實踐剛說的方法
01:52
They have digitized millions of books.
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他們已將數百萬本書給數位化
01:54
So what that means is, one could use computational methods
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這意味著，我們可以透過電腦
01:57
to read all of the books in a click of a button.
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簡單按個按鈕就能閱讀所有的書
01:59
That's very practical and extremely awesome.
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這非常實用而且相當棒
02:03
ELA: Let me tell you a little bit about where books come from.
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ELA：讓我為各位介紹這些書都來自何方
02:05
Since time immemorial, there have been authors.
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自古以來，有非常多作家
02:08
These authors have been striving to write books.
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這些作家一直努力寫作
02:11
And this became considerably easier
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但現在寫作變得相當容易
02:13
with the development of the printing press some centuries ago.
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這歸功於幾世紀前印刷術的革新
02:15
Since then, the authors have won
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自那時起作家們
02:18
on 129 million distinct occasions,
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能在一億兩千九百萬個不同的地方
02:20
publishing books.
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出版書籍
02:22
Now if those books are not lost to history,
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如果那些書沒有因為時代交替而遺失
02:24
then they are somewhere in a library,
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那麼那些書可能在某個圖書館的一處
02:26
and many of those books have been getting retrieved from the libraries
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有相當多書可以從圖書館中被借閱
02:29
and digitized by Google,
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由Google將其數位化
02:31
which has scanned 15 million books to date.
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迄今Google已經掃描了一千五百萬本書
02:33
Now when Google digitizes a book, they put it into a really nice format.
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Google將一本書數位化，並以優良的型式呈現
02:36
Now we've got the data, plus we have metadata.
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現在我們有了這些數據，加上這些詮釋資料
02:38
We have information about things like where was it published,
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我們有了相關的資訊，比如出版地區，
02:41
who was the author, when was it published.
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作者，出版時間
02:43
And what we do is go through all of those records
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我們所做的就是透過這些記錄
02:46
and exclude everything that's not the highest quality data.
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並剔除不是最精華的資料
02:50
What we're left with
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我們後來得到的是
02:52
is a collection of five million books,
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五百萬本書
02:55
500 billion words,
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五千億個詞
02:58
a string of characters a thousand times longer
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這是一串比人類基因組
03:00
than the human genome --
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還要長上一千倍的字符
03:03
a text which, when written out,
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如果寫成文章
03:05
would stretch from here to the Moon and back
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將會是從這裡到月球來回距離
03:07
10 times over --
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的十倍以上
03:09
a veritable shard of our cultural genome.
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這是我們文化基因名副其實的的一部分
03:13
Of course what we did
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當然當我們面臨
03:15
when faced with such outrageous hyperbole ...
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如此誇張的情況時
03:18
(Laughter)
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(笑聲)
03:20
was what any self-respecting researchers
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我們也跟每一位有自尊心的研究人員一樣
03:23
would have done.
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會做相同的事
03:26
We took a page out of XKCD,
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我們也和四格漫畫一樣
03:28
and we said, "Stand back.
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我們決定「等等
03:30
We're going to try science."
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我們要用科學的方式來處理。」
03:32
(Laughter)
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(笑聲)
03:34
JM: Now of course, we were thinking,
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JM：當然，我們在思考
03:36
well let's just first put the data out there
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首先我們先把資料提取出來
03:38
for people to do science to it.
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讓其他人以科學的方式去分析
03:40
Now we're thinking, what data can we release?
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現在我們在思考，我們能發行何種數據？
03:42
Well of course, you want to take the books
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當然，我們想拿這些書
03:44
and release the full text of these five million books.
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將這五百萬本書的內容全部釋出
03:46
Now Google, and Jon Orwant in particular,
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現在Google，特別是Jon Orwant
03:48
told us a little equation that we should learn.
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告訴我們一個我們該注意的小方程式
03:50
So you have five million, that is, five million authors
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我們有五百萬本書，也就是有五百萬名作者
03:53
and five million plaintiffs is a massive lawsuit.
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而五百萬名原告是一場龐大的訴訟
03:56
So, although that would be really, really awesome,
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雖然這個過程是相當地驚人
03:58
again, that's extremely, extremely impractical.
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但這還是極度的不切實際
04:01
(Laughter)
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(笑聲)
04:03
Now again, we kind of caved in,
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然後，我們似乎有點妥協
04:05
and we did the very practical approach, which was a bit less awesome.
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我們試了比較實際的方式，這方法不怎麼吸引人
04:08
We said, well instead of releasing the full text,
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我們認為，與其釋出全部的書籍資料
04:10
we're going to release statistics about the books.
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我們選擇將這些書的數據資料給呈現出來
04:12
So take for instance "A gleam of happiness."
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舉個例子「幸福的光」
04:14
It's four words; we call that a four-gram.
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這是四個字，我們稱做「四字詞」
04:16
We're going to tell you how many times a particular four-gram
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我們要告訴各位一個特定的四字詞
04:18
appeared in books in 1801, 1802, 1803,
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從1801，1802，1803年開始出現在書本裡
04:20
all the way up to 2008.
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直到2008年
04:22
That gives us a time series
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這給我們一個時間軸來了解
04:24
of how frequently this particular sentence was used over time.
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這些特定的字句從過去到現在的使用頻率
04:26
We do that for all the words and phrases that appear in those books,
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我們計算了所有出現在這些書中的字詞
04:29
and that gives us a big table of two billion lines
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彙整出的資料畫出了二十億條曲線
04:32
that tell us about the way culture has been changing.
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這告訴了我們文化是如何改變的
04:34
ELA: So those two billion lines,
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ELA：這二十億條曲線
04:36
we call them two billion n-grams.
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我們稱為二十億組詞
04:38
What do they tell us?
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這告訴了我們
04:40
Well the individual n-grams measure cultural trends.
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每一組詞代表了不同的文化趨勢
04:42
Let me give you an example.
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讓我舉個例子
04:44
Let's suppose that I am thriving,
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假設我做了件不得了的事
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then tomorrow I want to tell you about how well I did.
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明天我要告訴你是多不得了
04:48
And so I might say, "Yesterday, I throve."
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我可能會說「"Yesterday, I throve."」
04:51
Alternatively, I could say, "Yesterday, I thrived."
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或者，我也可以說「"Yesterday, I thrived."」
04:54
Well which one should I use?
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但我應該說哪一種呢？
04:57
How to know?
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要怎麼知道
04:59
As of about six months ago,
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大概在六個月前
05:01
the state of the art in this field
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要知道這一領域最尖端的方法
05:03
is that you would, for instance,
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你可能得要去詢問
05:05
go up to the following psychologist with fabulous hair,
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一位有著時髦髮型的心理學家
05:07
and you'd say,
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你可能會問
05:09
"Steve, you're an expert on the irregular verbs.
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「史蒂夫，你是不規則動詞的專家。
05:12
What should I do?"
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我該怎麼說呢？」
05:14
And he'd tell you, "Well most people say thrived,
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而他會告訴你「嗯，大部分的人會說"thrive"
05:16
but some people say throve."
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但有些人會說"throve"。」
05:19
And you also knew, more or less,
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而你也或多或少知道
05:21
that if you were to go back in time 200 years
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如果我們回到兩百年前
05:24
and ask the following statesman with equally fabulous hair,
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去問一位同樣也有時髦髮型的政治家
05:27
(Laughter)
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(笑聲)
05:30
"Tom, what should I say?"
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「湯姆，我應該怎麼說呢？」
05:32
He'd say, "Well, in my day, most people throve,
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他說「嗯，在我的年代，大部份的人說"throve"，
05:34
but some thrived."
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但少部分的人說"thrived"」
05:37
So now what I'm just going to show you is raw data.
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現在我要向各位展示原始數據
05:39
Two rows from this table of two billion entries.
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這二十億條目資料中的其中兩條數據
05:43
What you're seeing is year by year frequency
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各位將會看到的是"thrived"和"throve"兩個字
05:45
of "thrived" and "throve" over time.
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在各年時期的出現頻率
05:49
Now this is just two
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這只是二十億筆資料中
05:51
out of two billion rows.
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其中兩個詞條的資訊
05:54
So the entire data set
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這全部的數據資料
05:56
is a billion times more awesome than this slide.
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將會比此張投影片還要驚人億萬倍
05:59
(Laughter)
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(笑聲)
06:01
(Applause)
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(掌聲)
06:05
JM: Now there are many other pictures that are worth 500 billion words.
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JM：還有其他圖片也具有五千億字的價值
06:07
For instance, this one.
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例如這張
06:09
If you just take influenza,
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如果談到感冒
06:11
you will see peaks at the time where you knew
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從這幾個高峰點我們可以知道
06:13
big flu epidemics were killing people around the globe.
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感冒病毒的大流行在全球造成人類死亡
06:16
ELA: If you were not yet convinced,
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ELA：如果各位還不太相信
06:19
sea levels are rising,
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其他像是海平面升高
06:21
so is atmospheric CO2 and global temperature.
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大氣中的二氧化碳和全球暖化
06:24
JM: You might also want to have a look at this particular n-gram,
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JM：你也許會想看看這組特別的詞組
06:27
and that's to tell Nietzsche that God is not dead,
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「告訴尼采，上帝還沒死」
06:30
although you might agree that he might need a better publicist.
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也許你可能還會認為，他可能需要一個更好的公關
06:33
(Laughter)
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(笑聲)
06:35
ELA: You can get at some pretty abstract concepts with this sort of thing.
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ELA：從這當中，各位也能獲得一些相當抽象的概念
06:38
For instance, let me tell you the history
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例如，讓我跟各位說說
06:40
of the year 1950.
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有關「1950年」的歷史
06:42
Pretty much for the vast majority of history,
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幾乎在絕大多數的歷史裡
06:44
no one gave a damn about 1950.
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沒有特別談論1950這一年
06:46
In 1700, in 1800, in 1900,
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在1700年，在1800年，1900年
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no one cared.
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沒有人在乎
06:52
Through the 30s and 40s,
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甚至到30年代和40年代
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no one cared.
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也沒有人在談論
06:56
Suddenly, in the mid-40s,
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突然到了40年代中期
06:58
there started to be a buzz.
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開始出現了風潮
07:00
People realized that 1950 was going to happen,
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人們意識到1950年就要來臨
07:02
and it could be big.
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這是件大事
07:04
(Laughter)
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(笑聲)
07:07
But nothing got people interested in 1950
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但也沒有因此讓大眾對該年份產生興趣
07:10
like the year 1950.
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像是「那1950年」
07:13
(Laughter)
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(笑聲)
07:16
People were walking around obsessed.
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人們開始對這一年著迷
07:18
They couldn't stop talking
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大家無法停止談論
07:20
about all the things they did in 1950,
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有關他們在1950年所做的一切
07:23
all the things they were planning to do in 1950,
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所有他們計畫要在1950年所做的事
07:26
all the dreams of what they wanted to accomplish in 1950.
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所有他們要在1950年完成的夢想
07:31
In fact, 1950 was so fascinating
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事實上，1950年跟往後幾年相較
07:33
that for years thereafter,
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是相當迷人的一年
07:35
people just kept talking about all the amazing things that happened,
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人們不停談論所有發生在
07:38
in '51, '52, '53.
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'51，'52，'53年的驚奇事件
07:40
Finally in 1954,
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直到1954年
07:42
someone woke up and realized
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有人驚覺而且意識到
07:44
that 1950 had gotten somewhat passé.
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1950年已經變得過時了
07:48
(Laughter)
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(笑聲)
07:50
And just like that, the bubble burst.
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這一切就像泡沫破滅一樣
07:52
(Laughter)
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(笑聲)
07:54
And the story of 1950
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1950年的情況
07:56
is the story of every year that we have on record,
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其實就是我們數據上每一個年份的情況一樣
07:58
with a little twist, because now we've got these nice charts.
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稍微編排一下，我們有這些精美的圖表
08:01
And because we have these nice charts, we can measure things.
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因為有這些不錯的圖表，我們就能計算
08:04
We can say, "Well how fast does the bubble burst?"
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我們可以了解「風潮消逝的速度是多快？」
08:06
And it turns out that we can measure that very precisely.
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結果就是我們能很精確測量出一份數據
08:09
Equations were derived, graphs were produced,
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有了方程式，也有圖表
08:12
and the net result
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最終的結果就是
08:14
is that we find that the bubble bursts faster and faster
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談論年份的風潮一年比一年
08:17
with each passing year.
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消退的更快
08:19
We are losing interest in the past more rapidly.
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我們對於過去的興趣日漸消逝
08:24
JM: Now a little piece of career advice.
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JM：這張圖是有關職業建議
08:26
So for those of you who seek to be famous,
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對於那些想成名的人
08:28
we can learn from the 25 most famous political figures,
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我們可以知道二十五位最有名的政治人物
08:30
authors, actors and so on.
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作家、演員等等
08:32
So if you want to become famous early on, you should be an actor,
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如果各位想在年輕時就成名，那麼各位應該要當演員
08:35
because then fame starts rising by the end of your 20s --
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因為你的名氣會從二十歲後開始累積
08:37
you're still young, it's really great.
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那時正值青春年華，會相當不錯
08:39
Now if you can wait a little bit, you should be an author,
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如果各位有耐心一點，那麼就應該當個作家
08:41
because then you rise to very great heights,
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因為各位就能攀上高峰
08:43
like Mark Twain, for instance: extremely famous.
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成為像是馬克吐溫這樣有名望的作家
08:45
But if you want to reach the very top,
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但如果各位想攀上最頂尖的位置
08:47
you should delay gratification
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就得延後滿足自己的慾望
08:49
and, of course, become a politician.
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然後當一位政治家
08:51
So here you will become famous by the end of your 50s,
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那麼各位會在五十歲過後開始成名
08:53
and become very, very famous afterward.
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然後你的名氣會在未來持續延續
08:55
So scientists also tend to get famous when they're much older.
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科學家也往往是在老年時才成名
08:58
Like for instance, biologists and physics
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而生物學家和物理學家一樣
09:00
tend to be almost as famous as actors.
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往往也是和演員一樣著名
09:02
One mistake you should not do is become a mathematician.
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唯一不要做的職業就是變成數學家
09:05
(Laughter)
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(笑聲)
09:07
If you do that,
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如果各位真要做這行
09:09
you might think, "Oh great. I'm going to do my best work when I'm in my 20s."
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各位可能會想「太好了，當我在二十多歲時，我會盡一切努力。」
09:12
But guess what, nobody will really care.
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但事實上，沒人會真正去在乎你所做的事
09:14
(Laughter)
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(笑聲)
09:17
ELA: There are more sobering notes
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ELA：在我們的資料裡
09:19
among the n-grams.
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還有其他更發人省思的紀錄
09:21
For instance, here's the trajectory of Marc Chagall,
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例如馬克‧夏卡爾的名字出現的頻率軌跡
09:23
an artist born in 1887.
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夏卡爾是位1887年出生的藝術家
09:25
And this looks like the normal trajectory of a famous person.
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這看起來是一位名人名字正常出現在書中的軌跡
09:28
He gets more and more and more famous,
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他的名氣日益響亮
09:32
except if you look in German.
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但如果看德國的數據就不是如此
09:34
If you look in German, you see something completely bizarre,
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如果看德國的數據，會看到某部份是非常奇怪的
09:36
something you pretty much never see,
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這是幾乎不太可能看到的
09:38
which is he becomes extremely famous
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就是他變得非常有名
09:40
and then all of a sudden plummets,
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卻突然在1933年至1945年間
09:42
going through a nadir between 1933 and 1945,
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聲勢跌落谷底
09:45
before rebounding afterward.
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又反彈回升
09:48
And of course, what we're seeing
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當然我們看的出來
09:50
is the fact Marc Chagall was a Jewish artist
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這是因為馬克‧夏卡爾是一位猶太裔藝術家
09:53
in Nazi Germany.
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當時德國是納粹統治
09:55
Now these signals
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這些指標
09:57
are actually so strong
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事實上相當明確
09:59
that we don't need to know that someone was censored.
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我們不需要知道有人在審查書籍
10:02
We can actually figure it out
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我們能運用基本的信號運算方式
10:04
using really basic signal processing.
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實際了解當時狀況
10:06
Here's a simple way to do it.
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我們可以用簡單的方式來做
10:08
Well, a reasonable expectation
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合理的預期是
10:10
is that somebody's fame in a given period of time
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在一段特定的時間裡某人的名氣指數
10:12
should be roughly the average of their fame before
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應該會是他們成名前
10:14
and their fame after.
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和成名後的指數的平均值
10:16
So that's sort of what we expect.
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這大概是我們預期的結果
10:18
And we compare that to the fame that we observe.
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我們比較了我們觀察到的名人
10:21
And we just divide one by the other
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我們將前後的數值相除
10:23
to produce something we call a suppression index.
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得到的數值，我們稱作抑制指數
10:25
If the suppression index is very, very, very small,
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如果抑制指數的值非常的小
10:28
then you very well might be being suppressed.
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那麼就表示此人也許遭受到打壓
10:30
If it's very large, maybe you're benefiting from propaganda.
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但如果數值非常大，也許此人獲得大量的推廣
10:34
JM: Now you can actually look at
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JM：各位現在可以看到
10:36
the distribution of suppression indexes over whole populations.
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抑制指數在抽樣整體人數中的分佈情況
10:39
So for instance, here --
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所以，例如這裡 --
10:41
this suppression index is for 5,000 people
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這個抑制指數的抽樣人數是五千人
10:43
picked in English books where there's no known suppression --
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選自出版時期沒有打壓限制的英文書籍來做調查
10:45
it would be like this, basically tightly centered on one.
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曲線基本上會在數值1的地方呈現高峰
10:47
What you expect is basically what you observe.
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基本上預期的會和觀察到的數值是相同的
10:49
This is distribution as seen in Germany --
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這份分佈圖則是德國的部分 --
10:51
very different, it's shifted to the left.
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相當不同，曲線移往左側
10:53
People talked about it twice less as it should have been.
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人們談論事物的次數比預期的少了兩倍
10:56
But much more importantly, the distribution is much wider.
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更重要的是，整體分佈的情況更寬廣
10:58
There are many people who end up on the far left on this distribution
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有相當多人是落在圖表較左側的位置
11:01
who are talked about 10 times fewer than they should have been.
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因為他們比應該被提及的次數少了十倍
11:04
But then also many people on the far right
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但也有相當多人是落在較右側的部分
11:06
who seem to benefit from propaganda.
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似乎是因為被大量宣傳
11:08
This picture is the hallmark of censorship in the book record.
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這張圖是明顯看出書本中具有審查制度
11:11
ELA: So culturomics
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ELA：文化組學
11:13
is what we call this method.
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是我們用的方法
11:15
It's kind of like genomics.
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這和基因組學有些類似
11:17
Except genomics is a lens on biology
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不過基因組學是透過生物學
11:19
through the window of the sequence of bases in the human genome.
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基本的序列基礎來檢視人類基因組
11:22
Culturomics is similar.
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文化組學是類似的
11:24
It's the application of massive-scale data collection analysis
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這是應用收集分析規模龐大的數據
11:27
to the study of human culture.
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來研究人類文化
11:29
Here, instead of through the lens of a genome,
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不透過檢視基因組
11:31
through the lens of digitized pieces of the historical record.
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而是檢視歷史紀錄的數位資料
11:34
The great thing about culturomics
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文化組學的好處是
11:36
is that everyone can do it.
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每個人都能執行
11:38
Why can everyone do it?
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為何每個人都能做呢？
11:40
Everyone can do it because three guys,
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因為這三位人士
11:42
Jon Orwant, Matt Gray and Will Brockman over at Google,
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Google的Jon Orwant，Matt Gray還有Will Brockman
11:45
saw the prototype of the Ngram Viewer,
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他們看到Ngram瀏覽器的原型
11:47
and they said, "This is so fun.
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他們說「這太有趣了。」
11:49
We have to make this available for people."
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我們要讓大家都可以使用這功能
11:52
So in two weeks flat -- the two weeks before our paper came out --
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所以在兩週的時間 -- 我們的報告出來的兩週前 --
11:54
they coded up a version of the Ngram Viewer for the general public.
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他們編寫了一個大眾版本的Ngram瀏覽器
11:57
And so you too can type in any word or phrase that you're interested in
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各位可以打上任何各位有興趣的字或詞組
12:00
and see its n-gram immediately --
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然後立即看到該字詞的頻率變化 --
12:02
also browse examples of all the various books
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同時根據你搜尋的字詞
12:04
in which your n-gram appears.
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瀏覽不同書籍中的各種例子
12:06
JM: Now this was used over a million times on the first day,
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JM：這功能在首日就被使用了超過一百萬次
12:08
and this is really the best of all the queries.
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這也是各種查詢工具中最好的一個
12:10
So people want to be their best, put their best foot forward.
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人們希望做到最好的，以最好的狀態像前進
12:13
But it turns out in the 18th century, people didn't really care about that at all.
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但事實證明在18世紀，人們一點也不關心這一切
12:16
They didn't want to be their best, they wanted to be their beft.
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他們不想做到最好，他們想變成"beft"
12:19
So what happened is, of course, this is just a mistake.
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這是怎麼回事，當然這只是個錯誤
12:22
It's not that strove for mediocrity,
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這並不是說他們想要平凡
12:24
it's just that the S used to be written differently, kind of like an F.
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這只是因為"S"常被寫的不一樣，寫得像"F"
12:27
Now of course, Google didn't pick this up at the time,
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當然，Google並沒有挑出來
12:30
so we reported this in the science article that we wrote.
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所以我們在自己寫科學文章中提到此事
12:33
But it turns out this is just a reminder
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不過這只是個提醒
12:35
that, although this is a lot of fun,
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雖然這相當有趣
12:37
when you interpret these graphs, you have to be very careful,
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當你要解讀這些圖表，你必須非常謹慎
12:39
and you have to adopt the base standards in the sciences.
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而且必須採納科學的基礎標準
12:42
ELA: People have been using this for all kinds of fun purposes.
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ELA：大家一直在使用這工具來滿足各種樂趣
12:45
(Laughter)
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(笑聲)
12:52
Actually, we're not going to have to talk,
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事實上，我們不需要說明的
12:54
we're just going to show you all the slides and remain silent.
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我們原本只想播放所有的投影片然後在一旁保持沉默
12:57
This person was interested in the history of frustration.
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此人對於挫折的歷史感興趣
13:00
There's various types of frustration.
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挫折有非常多種方式
13:03
If you stub your toe, that's a one A "argh."
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如果你踢到腳趾，哀叫聲「啊」就是一個"A"的"argh"
13:06
If the planet Earth is annihilated by the Vogons
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如果地球被外星人毀滅
13:08
to make room for an interstellar bypass,
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變成星際間的通道
13:10
that's an eight A "aaaaaaaargh."
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那麼哀叫聲「啊」就是有八個"A"的"aaaaaaaargh"
13:12
This person studies all the "arghs,"
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此人研究了所有書籍上出現的哀叫聲「啊」
13:14
from one through eight A's.
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有從一個"A"到八個"A"
13:16
And it turns out
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結果是
13:18
that the less-frequent "arghs"
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較不頻繁的「啊」“arghs”
13:20
are, of course, the ones that correspond to things that are more frustrating --
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對應了那些相對較令人沮喪的的事情
13:23
except, oddly, in the early 80s.
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也有例外，奇怪的是在80年代初
13:26
We think that might have something to do with Reagan.
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我們認為這也許是受到雷根的影響
13:28
(Laughter)
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(笑聲)
13:30
JM: There are many usages of this data,
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JM：這份書據資料有相當多用途
13:33
but the bottom line is that the historical record is being digitized.
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不過最終就是歷史紀錄都被數位化了
13:36
Google has started to digitize 15 million books.
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Google已經開始將一千五百萬本書數位化
13:38
That's 12 percent of all the books that have ever been published.
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其中百分之十二的書是已出版的
13:40
It's a sizable chunk of human culture.
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這涵蓋了相當大量的人類文化
13:43
There's much more in culture: there's manuscripts, there newspapers,
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這當中有非常多的文化資料：裡頭有手稿，報紙
13:46
there's things that are not text, like art and paintings.
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也有不是文字的資料，像是藝術品和畫作
13:48
These all happen to be on our computers,
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現在這都存放在我們的電腦裡
13:50
on computers across the world.
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在世界各處的電腦裡
13:52
And when that happens, that will transform the way we have
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如果這一切成真，就會改變
13:55
to understand our past, our present and human culture.
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我們了解過去、現在和人類文化的方式
13:57
Thank you very much.
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非常謝謝各位
13:59
(Applause)
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(掌聲)
ABOUT THE SPEAKERS
Jean-Baptiste Michel - Data researcherJean-Baptiste Michel looks at how we can use large volumes of data to better understand our world.
Why you should listen
Jean-Baptiste Michel holds joint academic appointments at Harvard (FQEB Fellow) and Google (Visiting Faculty). His research focusses on using large volumes of data as tools that help better understand the world around us -- from the way diseases progress in patients over years, to the way cultures change in human societies over centuries. With his colleague Erez Lieberman Aiden, Jean-Baptiste is a Founding Director of Harvard's Cultural Observatory, where their research team pioneers the use of quantitative methods for the study of human culture, language and history. His research was featured on the covers of Science and Nature, on the front pages of the New York Times and the Boston Globe, in The Economist, Wired and many other venues. The online tool he helped create -- ngrams.googlelabs.com -- was used millions of times to browse cultural trends. Jean-Baptiste is an Engineer from Ecole Polytechnique (Paris), and holds an MS in Applied Mathematics and a PhD in Systems Biology from Harvard.
Jean-Baptiste Michel | Speaker | TED.com
Erez Lieberman Aiden - Researcher
Erez Lieberman Aiden pursues a broad range of research interests, spanning genomics, linguistics, mathematics ...
Why you should listen
Erez Lieberman Aiden is a fellow at the Harvard Society of Fellows and Visiting Faculty at Google. His research spans many disciplines and has won numerous awards, including recognition for one of the top 20 "Biotech Breakthroughs that will Change Medicine", by Popular Mechanics; the Lemelson-MIT prize for the best student inventor at MIT; the American Physical Society's Award for the Best Doctoral Dissertation in Biological Physics; and membership in Technology Review's 2009 TR35, recognizing the top 35 innovators under 35. His last three papers -- two with JB Michel -- have all appeared on the cover of Nature and Science.
Erez Lieberman Aiden | Speaker | TED.com