TEDxCMU
Luis von Ahn: Massive-scale online collaboration
Luis von Ahn: 大規模在線協作
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在重新定義了CAPTCHA之後，每一次嘅人為輸入都會幫手數字化圖書，Luis von Ahn心諗我們縱可以點樣利用互聯網上許多個人嘅小小力量來實現巨大嘅價值。在TEDxCMU上，佢同我地分享咗佢充滿野心嘅新項目-Duoling。哩個項目系快速、準確嘅翻譯網頁嘅同時，幫助千萬人學習新嘅語言。而所有這一切都是免費嘅。
Luis von Ahn - Computer scientist
Luis von Ahn builds systems that combine humans and computers to solve large-scale problems that neither can solve alone. Full bio
Luis von Ahn builds systems that combine humans and computers to solve large-scale problems that neither can solve alone. Full bio
Double-click the English transcript below to play the video.
00:15
How many of you had to fill out some sort of web form
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有幾多人系填寫網頁表格時
00:17
where you've been asked to read a distorted sequence of characters like this?
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需要識別甘樣扭曲嘅文字？
00:19
How many of you found it really, really annoying?
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有幾多人覺得哩樣嘢真系好煩？
00:21
Okay, outstanding. So I invented that.
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都唔少啊。哩樣嘢就系我發明嘅。
00:24
(Laughter)
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（笑聲）
00:26
Or I was one of the people who did it.
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或者講我系其中一個發明人。
00:28
That thing is called a CAPTCHA.
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果樣嘢叫CAPTCHA（驗證碼）
00:30
And the reason it is there is to make sure you, the entity filling out the form,
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之所以佢會出現系網頁中，系因為要確認你，填空嘅哩個行為人，
00:32
are actually a human and not some sort of computer program
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系一個真正嘅人類，而唔系某某專門寫出來、
00:35
that was written to submit the form millions and millions of times.
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為咗千萬次重複填表嘅電腦程式。
00:37
The reason it works is because humans,
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甘樣做系因為人、
00:39
at least non-visually-impaired humans,
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至少系視覺正常的人，
00:41
have no trouble reading these distorted squiggly characters,
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都唔會覺得讀出哩嘀扭曲嘅文字系一種困難，
00:43
whereas computer programs simply can't do it as well yet.
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而電腦就縱未可以好似人甘樣讀得甘好。
00:46
So for example, in the case of Ticketmaster,
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比如講，在Ticketmaster網站上，
00:48
the reason you have to type these distorted characters
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你要輸入哩嘀扭曲字符的原因
00:50
is to prevent scalpers from writing a program
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系為咗防止“黃牛”寫程式
00:52
that can buy millions of tickets, two at a time.
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兩張一次甘買幾萬張菲
00:54
CAPTCHAs are used all over the Internet.
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驗證碼在網絡上嘅應用十分普遍
00:56
And since they're used so often,
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既然我們如此頻繁嘅使用佢
00:58
a lot of times the precise sequence of random characters that is shown to the user
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很多時候用戶就會見到一嘀
01:00
is not so fortunate.
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奇怪嘅文字排序。
01:02
So this is an example from the Yahoo registration page.
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哩度系一個來自雅虎註冊頁嘅例子
01:05
The random characters that happened to be shown to the user
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展示俾用戶嘅隨機字符“W,A,I,T"
01:07
were W, A, I, T, which, of course, spell a word.
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啱好可以組成一個詞，等待。
01:10
But the best part is the message
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最有趣嘅系
01:13
that the Yahoo help desk got about 20 minutes later.
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20分鐘後幫助後台收到嘅訊息。
01:16
Text: "Help! I've been waiting for over 20 minutes, and nothing happens."
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文字：救命啊！我都等咗廿幾分鐘啦，都冇任何變化啊。
01:19
(Laughter)
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（笑聲）
01:23
This person thought they needed to wait.
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佢縱以為網站系叫佢等。
01:25
This of course, is not as bad as this poor person.
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當然縱有更黑嘅
01:28
(Laughter)
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（笑聲）
01:30
CAPTCHA Project is something that we did here at Carnegie Melllon over 10 years ago,
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驗證碼計劃系我哋十多年前系卡內基梅隆大學搞起嘅
01:33
and it's been used everywhere.
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並開始被廣泛應用
01:35
Let me now tell you about a project that we did a few years later,
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以嘎等我哋來傾傾我哋幾年後搞嘅另一個項目
01:37
which is sort of the next evolution of CAPTCHA.
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亦即系驗證碼的新版本
01:40
This is a project that we call reCAPTCHA,
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我哋稱之為“reCAPTCHA”
01:42
which is something that we started here at Carnegie Mellon,
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哩個計劃系從卡內基梅隆大學開始
01:44
then we turned it into a startup company.
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成為我哋嘅啟動公司
01:46
And then about a year and a half ago,
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一年半之前
01:48
Google actually acquired this company.
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Google收購咗裡個公司
01:50
So let me tell you what this project started.
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以嘎我來講講哩個項目系點開始噶
01:52
So this project started from the following realization:
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哩個項目出於如下認識：
01:55
It turns out that approximately 200 million CAPTCHAs
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每天在全球範圍之內有大概2億次
01:57
are typed everyday by people around the world.
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驗證碼嘅輸入
02:00
When I first heard this, I was quite proud of myself.
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我第一次聽到嘅時候都幾自豪
02:02
I thought, look at the impact that my research has had.
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我諗，我哋嘅研究都幾大影響噶喔
02:04
But then I started feeling bad.
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跟住我就覺得很難受
02:06
See here's the thing, each time you type a CAPTCHA,
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因為你每輸入一次驗證碼
02:08
essentially you waste 10 seconds of your time.
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你就浪費咗10秒鐘嘅時間。
02:11
And if you multiply that by 200 million,
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如果你將佢乘以2億
02:13
you get that humanity as a whole is wasting about 500,000 hours every day
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甘人類就因為輸入驗證碼
02:16
typing these annoying CAPTCHAs.
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而每天浪費咗50萬個小時
02:18
So then I started feeling bad.
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所以我就開始覺得唔爽。
02:20
(Laughter)
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（笑聲）
02:22
And then I started thinking, well, of course, we can't just get rid of CAPTCHAs,
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跟住我就開始諗，恩，當然啦，我哋冇可能就此拋棄驗證碼系統
02:25
because the security of the Web sort of depends on them.
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因為網頁嘅安全指意緊佢
02:27
But then I started thinking, is there any way we can use this effort
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但是否有乜辦法可以將佢利用起來
02:30
for something that is good for humanity?
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為人類做嘀好事？
02:32
So see, here's the thing.
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恩，關鍵在於，
02:34
While you're typing a CAPTCHA, during those 10 seconds,
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當你輸入一個驗證碼嘅時候，在果10秒鐘，
02:36
your brain is doing something amazing.
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你嘅大腦系度做緊一嘀好神奇嘅嘢。
02:38
Your brain is doing something that computers cannot yet do.
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哩個系電腦未可以做到嘅嘢。
02:40
So can we get you to do useful work for those 10 seconds?
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我哋可唔可以用裡10秒做嘀有用嘅嘢呢？
02:43
Another way of putting it is,
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換種講法，
02:45
is there some humongous problem that we cannot yet get computers to solve,
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系唔系有嘀計算機無法解決嘅龐大問題
02:47
yet we can split into tiny 10-second chunks
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而我哋可以將之分為10秒10秒嘅子問題呢？
02:50
such that each time somebody solves a CAPTCHA
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甘樣，每次有人輸入一個驗證碼嘅時候，
02:52
they solve a little bit of this problem?
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佢哋就解決咗哩個問題嘅其中小小部分。
02:54
And the answer to that is "yes," and this is what we're doing now.
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答案系肯定嘅，而這就是我哋做緊嘅嘢。
02:56
So what you may not know is that nowadays while you're typing a CAPTCHA,
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你可能唔知道以嘎你每一次輸入驗證碼嘅時候，
02:59
not only are you authenticating yourself as a human,
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你唔單止證明咗自己人類嘅身份，
03:01
but in addition you're actually helping us to digitize books.
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同時亦系度幫緊我哋數字化圖書。
03:03
So let me explain how this works.
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等我來解釋一下：
03:05
So there's a lot of projects out there trying to digitize books.
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目前已經有好多嘅項目做緊數字化圖書，
03:07
Google has one. The Internet Archive has one.
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Google有一個，“互聯網檔案”都有一個。
03:10
Amazon, now with the Kindle, is trying to digitize books.
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亞馬遜同埋Kindle，亦都試圖數字化圖書。
03:12
Basically the way this works
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基本上數字化圖書的方式
03:14
is you start with an old book.
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系要從一本舊書開始。
03:16
You've seen those things, right? Like a book?
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你睇過書葛嚯？ 一本書？
03:18
(Laughter)
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（笑聲）
03:20
So you start with a book, and then you scan it.
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你從一本紙質書開始，然後掃描佢。
03:22
Now scanning a book
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掃描一本書
03:24
is like taking a digital photograph of every page of the book.
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就好似對一本書嘅每一頁影一張數字相片。
03:26
It gives you an image for every page of the book.
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佢可以給你書中每一頁嘅圖像。
03:28
This is an image with text for every page of the book.
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哩嘀圖像包括了書中每一頁上的文字。
03:30
The next step in the process
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下一步就係
03:32
is that the computer needs to be able to decipher all of the words in this image.
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電腦需要能夠識別這個圖像中嘅所有文字。
03:35
That's using a technology called OCR,
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目前使用的技術叫做OCR
03:37
for optical character recognition,
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亦即系，光學字符識別。
03:39
which takes a picture of text
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OCR首先獲取文字的圖像，
03:41
and tries to figure out what text is in there.
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然後嘗試辨認出什麼文字在那個圖像中。
03:43
Now the problem is that OCR is not perfect.
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問題在於OCR並不完美。
03:45
Especially for older books
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尤其對於古舊嘅圖書而言，
03:47
where the ink has faded and the pages have turned yellow,
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上面嘅墨跡已經變淡，紙頁亦開始變黃，
03:50
OCR cannot recognize a lot of the words.
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好多文字OCR唔可以識別
03:52
For example, for things that were written more than 50 years ago,
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比如，對五十多年前嘅書，
03:54
the computer cannot recognize about 30 percent of the words.
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大概有30%的文字唔可以被電腦識別。
03:57
So what we're doing now
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所以，我哋正在做嘅事
03:59
is we're taking all of the words that the computer cannot recognize
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就係將所有哩嘀電腦唔可以識別嘅文字摞出來，
04:01
and we're getting people to read them for us
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然後讓其他人在網上輸入驗證碼的同時，
04:03
while they're typing a CAPTCHA on the Internet.
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幫我地將佢哋讀出來。
04:05
So the next time you type a CAPTCHA, these words that you're typing
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所以，下一次你輸入驗證碼嘅時候，你輸入嘅果嘀文字
04:08
are actually words that are coming from books that are being digitized
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實質上來自於正在被數字化嘅圖書之中
04:11
that the computer could not recognize.
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而所有這些文字都是電腦無法職別嘅。
04:13
And now the reason we have two words nowadays instead of one
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另外，現在同時出現兩個詞而唔系一個詞
04:15
is because, you see, one of the words
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系因為其中一個詞
04:17
is a word that the system just got out of a book,
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系系統從書中撿出嘅無法識別嘅單詞
04:19
it didn't know what it was, and it's going to present it to you.
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系統並唔知道哩個詞系乜嘢，
04:22
But since it doesn't know the answer for it, it cannot grade it for you.
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但系既然佢唔知答案系乜嘢，佢都唔可以判斷你是否答啱。
04:25
So what we do is we give you another word,
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所以我哋嘅做法就系俾你另一個詞，
04:27
one for which the system does know the answer.
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另一個系統知道答案嘅詞。
04:29
We don't tell you which one's which, and we say, please type both.
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我哋唔講俾你知邊個打邊個，我哋淨系講，請將兩個詞都輸入。
04:31
And if you type the correct word
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如果你正確甘輸入咗
04:33
for the one for which the system already knows the answer,
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電腦知道答案嘅果個詞，
04:35
it assumes you are human,
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電腦就認為你系人類，
04:37
and it also gets some confidence that you typed the other word correctly.
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同時亦對你正確輸入另一個詞多咗幾分信心。
04:39
And if we repeat this process to like 10 different people
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如果我哋對10個人重複哩個過程，
04:42
and all of them agree on what the new word is,
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而佢地都同意果個新詞系乜嘢。
04:44
then we get one more word digitized accurately.
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甘我哋就可以正確嘅多數字化一個新詞。
04:46
So this is how the system works.
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這就係哩個系統嘅工作原理
04:48
And basically, since we released it about three or four years ago,
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同時，因為我哋已經系三四年前發布咗哩個新系統，
04:51
a lot of websites have started switching
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許多網站已經開始從以前浪費用戶時間嘅舊驗證碼系統
04:53
from the old CAPTCHA where people wasted their time
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許多網站已經開始從以前浪費用戶時間嘅舊驗證碼系統
04:55
to the new CAPTCHA where people are helping to digitize books.
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轉去哩個新嘅可以幫助數字化圖書嘅新系統。
04:57
So for example, Ticketmaster.
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例如，系Ticketmaster的網站上，
04:59
So every time you buy tickets on Ticketmaster, you help to digitize a book.
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你每一次買票，都系幫緊數字化圖書。
05:02
Facebook: Every time you add a friend or poke somebody,
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Facebook，你每一次加好友或者打招呼，
05:04
you help to digitize a book.
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你都在幫緊數字化圖書。
05:06
Twitter and about 350,000 other sites are all using reCAPTCHA.
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twitter同埋另外350000個網站都用緊reCAPTCHA。
05:09
And in fact, the number of sites that are using reCAPTCHA is so high
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事實上，使用reCAPTCHA 嘅網站數量如此之多，
05:11
that the number of words that we're digitizing per day is really, really large.
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以至於我哋每天數字化嘅文字數量亦都十分之高。
05:14
It's about 100 million a day,
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大概有1000萬之多，
05:16
which is the equivalent of about two and a half million books a year.
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相當於每年數字化咗250萬本書。
05:20
And this is all being done one word at a time
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而這一切都系一個字一個字甘
05:22
by just people typing CAPTCHAs on the Internet.
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由用戶在網上輸入驗證碼得來嘅。
05:24
(Applause)
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（鼓掌）
05:32
Now of course,
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當然，
05:34
since we're doing so many words per day,
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既然我哋每天都可以完成甘多字，
05:36
funny things can happen.
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有趣嘅事可能就會發生。
05:38
And this is especially true because now we're giving people
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尤其現在我哋同時向用戶
05:40
two randomly chosen English words next to each other.
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展示兩個隨機產生、並列出現嘅英文單詞。
05:42
So funny things can happen.
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好笑的事會發生。
05:44
For example, we presented this word.
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比如，我哋展示了哩個詞。
05:46
It's the word "Christians"; there's nothing wrong with it.
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單詞“基督徒”，並冇乜問題。
05:48
But if you present it along with another randomly chosen word,
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但系如果你同另外一個隨機選擇出來嘅詞擺埋一齊，
05:51
bad things can happen.
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就可能搞獲。
05:53
So we get this. (Text: bad christians)
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我哋就見到甘樣嘅組合。（文字：壞基督徒）
05:55
But it's even worse, because the particular website where we showed this
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但更衰嘅系，这组文字啱好出现系一个叫
05:58
actually happened to be called The Embassy of the Kingdom of God.
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“上帝王国的使館”的网站上。
06:01
(Laughter)
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（笑声）
06:03
Oops.
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哎呀
06:05
(Laughter)
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（笑声）
06:08
Here's another really bad one.
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哩度縱有一個
06:10
JohnEdwards.com
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JohnEdwards.com
06:12
(Text: Damn liberal)
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（文字：可恶的自由派）
06:15
(Laughter)
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（笑声）
06:17
So we keep on insulting people left and right everyday.
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所以我哋每天都不停嘅侮辱
06:20
Now, of course, we're not just insulting people.
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当然，我哋不单只侮辱人。
06:22
See here's the thing, since we're presenting two randomly chosen words,
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就这样，既然我哋展示两个随机产生的单词，
06:25
interesting things can happen.
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有趣嘅事可能发生。
06:27
So this actually has given rise
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所以佢实际上催生了
06:29
to a really big Internet meme
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一个十分庞大嘅、
06:32
that tens of thousands of people have participated in,
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有成千上万人参与嘅互联网流行，
06:34
which is called CAPTCHA art.
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叫做“验证码艺术”。
06:36
I'm sure some of you have heard about it.
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我相信你地其中一些人已经听说过佢了。
06:38
Here's how it works.
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佢系甘运行嘅。
06:40
Imagine you're using the Internet and you see a CAPTCHA
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想像你自己正系度用紧互联网，
06:42
that you think is somewhat peculiar,
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如果你看到一个有嘀奇怪嘅验证码，
06:44
like this CAPTCHA. (Text: invisible toaster)
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好似甘。（文字：隐形嘅土司机）
06:46
Then what you're supposed to do is you take a screen shot of it.
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然后，你应该做嘅系将佢截图落来，
06:48
Then of course, you fill out the CAPTCHA
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跟住，当然啦，你将个验证码填好，
06:50
because you help us digitize a book.
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因为甘你就帮紧我哋数字化图书。
06:52
But then, first you take a screen shot,
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所以，你首先截图落来，
06:54
and then you draw something that is related to it.
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然后画一些与之相关嘅嘢。
06:56
(Laughter)
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（笑声）
06:58
That's how it works.
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就系甘。
07:01
There are tens of thousands of these.
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哩度有成千上万甘样嘅作品。
07:04
Some of them are very cute. (Text: clenched it)
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有嘀都好可爱咖。（文字：捉緊佢）
07:06
(Laughter)
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（笑声）
07:08
Some of them are funnier.
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有嘀更加搞笑。
07:10
(Text: stoned founders)
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（文字：飲high咗嘅建國者）
07:13
(Laughter)
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（笑声）
07:16
And some of them,
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縱有一嘀
07:18
like paleontological shvisle,
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比如“古生物嘅史維錘”
07:21
they contain Snoop Dogg.
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佢哋包括Snoop Dogg。
07:23
(Laughter)
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（笑聲）
07:26
Okay, so this is my favorite number of reCAPTCHA.
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哩個系我最中意嘅reCAPTCHA數字
07:28
So this is the favorite thing that I like about this whole project.
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我最中意嘅部分
07:31
This is the number of distinct people
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哩個數字系
07:33
that have helped us digitize at least one word out of a book through reCAPTCHA:
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通過reCAPTCHA幫助我哋數字化圖書嘅人數
07:36
750 million,
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7億5千萬，
07:38
which is a little over 10 percent of the world's population,
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剛好小小地超過世界人口的百分之一，
07:40
has helped us digitize human knowledge.
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已經幫助咗我哋數字化人類知識，
07:42
And it is numbers like these that motivate my research agenda.
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就是甘樣嘅數字激勵咗我嘅研究計劃。
07:45
So the question that motivates my research is the following:
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所以激勵我研究嘅問題如下：
07:48
If you look at humanity's large-scale achievements,
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如果你睇睇人類的大規模的成就，
07:50
these really big things
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果嘀歷史上嘅、人類聚集起來
07:52
that humanity has gotten together and done historically --
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一起完成嘅真正“大”事
07:55
like for example, building the pyramids of Egypt
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譬如，建造埃及嘅金字塔
07:57
or the Panama Canal
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或者建成巴拿馬運河
07:59
or putting a man on the Moon --
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或者將人類送上月球
08:01
there is a curious fact about them,
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哩度有一個幾有趣嘅關於佢嘅事實
08:03
and it is that they were all done with about the same number off people.
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那就系佢哋全部都系被差唔多數量嘅人完成嘅。
08:05
It's weird; they were all done with about 100,000 people.
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很奇怪，佢哋全部都系被差唔多10萬人完成嘅。
08:08
And the reason for that is because, before the Internet,
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其中嘅原因系，系互聯網出現之前，
08:11
coordinating more than 100,000 people,
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聯合超過10萬人——更不用說付錢俾佢哋
08:13
let alone paying them, was essentially impossible.
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系幾乎冇可能嘅。
08:16
But now with the Internet, I've just shown you a project
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但系因為有咗互聯網，我剛剛展示俾你地嘅項目
08:18
where we've gotten 750 million people
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就有7億5千萬人參與
08:20
to help us digitize human knowledge.
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來幫助我哋數字化人類知識。
08:22
So the question that motivates my research is,
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所以激勵我哋研究嘅問題就系
08:24
if we can put a man on the Moon with 100,000,
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如果我哋可以用10萬人就將人類送上月球，
08:27
what can we do with 100 million?
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我哋可以用1億人做嘀乜嘢？
08:29
So based on this question,
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基於裡個問題，
08:31
we've had a lot of different projects that we've been working on.
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我哋開展咗許多唔同嘅項目。
08:33
Let me tell you about one that I'm most excited about.
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等我同你哋介紹下我最為之興奮嘅一個。
08:36
This is something that we've been semi-quietly working on
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哩個項目我哋已經“半地下”甘進行咗
08:38
for the last year and a half or so.
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差唔多一年半。
08:40
It hasn't yet been launched. It's called Duolingo.
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佢縱未正式運行，叫做Duolingo.
08:42
Since it hasn't been launched, shhhhh!
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因為我哋縱沒投入使用，所以，噓！
08:44
(Laughter)
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（笑聲）
08:46
Yeah, I can trust you'll do that.
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我相信你哋都會守口如瓶嘅。
08:48
So this is the project. Here's how it started.
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哩個項目系甘樣開始嘅。
08:50
It started with me posing a question to my graduate student,
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佢開始於我向我嘅研究生Severin Hacker提出嘅一個問題
08:52
Severin Hacker.
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佢開始於我向我嘅研究生Severin Hacker提出嘅一個問題
08:54
Okay, that's Severin Hacker.
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這就系佢。
08:56
So I posed the question to my graduate student.
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我向佢提咗一個問題
08:58
By the way, you did hear me correctly;
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順便一提，你冇聽錯，
09:00
his last name is Hacker.
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佢確實姓“Hacker（駭客）”
09:02
So I posed this question to him:
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我向佢提出咗哩個問題：
09:04
How can we get 100 million people
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我哋點樣先可以讓1億人免費甘
09:06
translating the Web into every major language for free?
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將互聯網翻譯為每一種主要嘅語言？
09:09
Okay, so there's a lot of things to say about this question.
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恩，關於哩個題目我哋可以有好多可以講。
09:11
First of all, translating the Web.
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首先系翻譯網頁。
09:13
So right now the Web is partitioned into multiple languages.
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目前，互聯網被分為多種語言。
09:16
A large fraction of it is in English.
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其中很大一部分系英文。
09:18
If you don't know any English, you can't access it.
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如果你唔使任何英文，你就冇辦法接觸到佢哋。
09:20
But there's large fractions in other different languages,
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但同時亦有很大部分系其他語言，
09:22
and if you don't know those languages, you can't access it.
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同樣的，如果你唔識果嘀語言，你亦無法接觸到。
09:25
So I would like to translate all of the Web, or at least most of the Web,
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所以我好想可以將整個互聯網，或者至少系大部分
09:28
into every major language.
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翻譯成每一種主要語言。
09:30
So that's what I would like to do.
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這就係我想做嘅事。
09:32
Now some of you may say, why can't we use computers to translate?
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有嘀人可能會講，點解唔用電腦來翻譯呢？
09:35
Why can't we use machine translation?
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點解我哋唔用機器翻譯？
09:37
Machine translation nowadays is starting to translate some sentences here and there.
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機器翻譯以嘎已經開始時不時嘅出現，
09:39
Why can't we use it to translate the whole Web?
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點解我哋唔用佢翻譯整個互聯網呢？
09:41
Well the problem with that is that it's not yet good enough
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恩， 問題在於，機器翻譯縱未夠好。
09:43
and it probably won't be for the next 15 to 20 years.
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而且哩個問題在未來15、20年後亦唔一定能解決。
09:45
It makes a lot of mistakes.
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佢出錯太多。
09:47
Even when it doesn't make a mistake,
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即使佢冇出錯，
09:49
since it makes so many mistakes, you don't know whether to trust it or not.
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但因為佢出錯太多，你好難知道系唔系應該相信佢。
09:52
So let me show you an example
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舉個用機器翻譯嘅例子吧。
09:54
of something that was translated with a machine.
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舉個用機器翻譯嘅例子吧。
09:56
Actually it was a forum post.
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哩個系一篇網上論壇嘅文章，
09:58
It was somebody who was trying to ask a question about JavaScript.
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文章想系一個網民想問一個關於Java語言嘅問題。
10:01
It was translated from Japanese into English.
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佢從日文被翻譯成英文。
10:04
So I'll just let you read.
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你可以睇睇。
10:06
This person starts apologizing
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哩個人首先為使用電腦翻譯
10:08
for the fact that it's translated with a computer.
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而道歉。
10:10
So the next sentence is is going to be the preamble to the question.
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下一個句子開始入題
10:13
So he's just explaining something.
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佢系度解釋緊一嘀嘢。
10:15
Remember, it's a question about JavaScript.
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請留意，哩個系一個關於Java語言嘅問題。
10:19
(Text: At often, the goat-time install a error is vomit.)
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（文字：常常，山羊時間安裝一個錯誤系嘔吐
10:23
(Laughter)
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（笑聲）
10:27
Then comes the first part of the question.
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接著系哩個問題嘅第一個部分。
10:30
(Text: How many times like the wind, a pole, and the dragon?)
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（文字：有幾多次好似風，柱，龍？）
10:34
(Laughter)
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（笑聲）
10:36
Then comes my favorite part of the question.
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跟住系我最中意嘅部分。
10:39
(Text: This insult to father's stones?)
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（文字：這對父親石嘅侮辱？）
10:42
(Laughter)
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（笑聲）
10:44
And then comes the ending, which is my favorite part of the whole thing.
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接著到咗問題的最後部分，整件事我最中意嘅部分。
10:47
(Text: Please apologize for your stupidity. There are a many thank you.)
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（文字：請為你嘅愚蠢而道歉。裡度有對你嘅許多感謝。）
10:51
(Laughter)
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（笑聲）
10:53
Okay, so computer translation, not yet good enough.
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所以，電腦翻譯並沒夠好。
10:55
So back to the question.
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回到問題
10:57
So we need people to translate the whole Web.
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我哋需要人來翻譯互聯網。
11:00
So now the next question you may have is,
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你可能要問嘅下一個問題可能系
11:02
well why can't we just pay people to do this?
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點解我哋唔使錢叫人來做呢？
11:04
We could pay professional language translators to translate the whole Web.
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我哋可以使錢請專業嘅語言翻譯家來翻譯整個互聯網。
11:07
We could do that.
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我哋可以甘做
11:09
Unfortunately, it would be extremely expensive.
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但不幸嘅系，甘樣可能會非常貴。
11:11
For example, translating a tiny, tiny fraction of the whole Web, Wikipedia,
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例如，將互聯網中嘅極小極小嘅一部分——維基百科，
11:14
into one other language, Spanish.
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翻譯為西班牙文。
11:17
Wikipedia exists in Spanish,
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雖然有西班牙文嘅維基百科，
11:19
but it's very small compared to the size of English.
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但相比於英文維基百科，佢嘅內容很少。
11:21
It's about 20 percent of the size of English.
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大概只有英文維基百科嘅20%。
11:23
If we wanted to translate the other 80 percent into Spanish,
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如果我哋想將另外果80%翻譯為西班牙文，
11:26
it would cost at least 50 million dollars --
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可能要使五千萬美元
11:28
and this is at even the most exploited, outsourcing country out there.
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即使系最便嘅服務外包國家
11:31
So it would be very expensive.
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因此人工翻譯會好貴。
11:33
So what we want to do is we want to get 100 million people
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我哋想做嘅系將1億人聯合起來，
11:35
translating the Web into every major language
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將互聯網翻譯為任何一種主要語言，
11:37
for free.
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而唔使一分錢。
11:39
Now if this is what you want to do,
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如果哩個系你想做嘅，
11:41
you pretty quickly realize you're going to run into two pretty big hurdles,
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你好快就會發現自己將面臨兩個幾大嘅攔路石
11:43
two big obstacles.
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兩個大障礙。
11:45
The first one is a lack of bilinguals.
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第一個就是缺少雙語人才。
11:48
So I don't even know
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我甚至唔知道
11:50
if there exists 100 million people out there using the Web
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系唔系有1億擁有足夠雙語能力嘅人網友
11:53
who are bilingual enough to help us translate.
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會幫我哋翻譯。
11:55
That's a big problem.
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哩個系個大問題。
11:57
The other problem you're going to run into is a lack of motivation.
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另一個你會遇到嘅問題系缺少激勵。
11:59
How are we going to motivate people
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我哋點樣激勵人哋
12:01
to actually translate the Web for free?
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去免費翻譯網頁呢？
12:03
Normally, you have to pay people to do this.
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通常來講，你必須使錢僱人來做哩嘀。
12:06
So how are we going to motivate them to do it for free?
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點樣能激勵人哋免費來翻譯呢？
12:08
Now when we were starting to think about this, we were blocked by these two things.
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當我哋開始諗哩嘀問題嘅時候，我哋就被哩兩個困難限制住咗。
12:11
But then we realized, there's actually a way
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但後來我哋發現系有辦法
12:13
to solve both these problems with the same solution.
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用同一個解決方案同時解決裡嘀問題。
12:15
There's a way to kill two birds with one stone.
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有一個一石二鳥嘅辦法。
12:17
And that is to transform language translation
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就系將語言翻譯轉變為
12:20
into something that millions of people want to do,
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大家都想做嘅事，
12:23
and that also helps with the problem of lack of bilinguals,
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同時用語言教育
12:26
and that is language education.
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幫助果嘀雙語能力不足嘅人
12:29
So it turns out that today,
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實際上，現在有超過12億人在學習外語。
12:31
there are over 1.2 billion people learning a foreign language.
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實際上，現在有超過12億人在學習外語。
12:34
People really, really want to learn a foreign language.
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大家都十分、十分想學習外語，
12:36
And it's not just because they're being forced to do so in school.
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而且唔系因為系學校被逼甘做。
12:39
For example, in the United States alone,
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比如，單單系美國
12:41
there are over five million people who have paid over $500
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就有超過500萬人在軟件上花費咗超過500美元
12:43
for software to learn a new language.
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用於學習一種新語言。
12:45
So people really, really want to learn a new language.
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所以，大家真系好想學新語言。
12:47
So what we've been working on for the last year and a half is a new website --
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我哋在過去一年半嘅時間裡做嘅系一個新網站，
12:50
it's called Duolingo --
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叫做Duolingo
12:52
where the basic idea is people learn a new language for free
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Duolingo嘅基本理念系人們可以免費學習一種新語言，
12:55
while simultaneously translating the Web.
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同時義務翻譯網頁。
12:57
And so basically they're learning by doing.
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簡單來講，佢哋系度通過實踐來學習。
12:59
So the way this works
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Duolingo運作嘅方式系
13:01
is whenever you're a just a beginner, we give you very, very simple sentences.
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如果你系初學者，我哋會俾你非常非常簡單嘅句子。
13:04
There's, of course, a lot of very simple sentences on the Web.
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當然網上有許多簡單嘅句子。
13:06
We give you very, very simple sentences
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我哋俾你非常非常簡單嘅句子
13:08
along with what each word means.
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同每個單詞嘅意思。
13:10
And as you translate them, and as you see how other people translate them,
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隨著你翻譯佢哋，加上看其他人點樣翻譯佢哋，
13:13
you start learning the language.
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你就開始學習果種語言了。
13:15
And as you get more and more advanced,
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當你變得越來越進階嘅時候，
13:17
we give you more and more complex sentences to translate.
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我哋就會俾你更多複雜句子來翻譯。
13:19
But at all times, you're learning by doing.
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無論何時，你都系通過練習來學習。
13:21
Now the crazy thing about this method
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哩種方法嘅瘋狂在於
13:23
is that it actually really works.
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佢真嘅會成功。
13:25
First of all, people are really, really learning a language.
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首先，人們真嘅學緊語言。
13:27
We're mostly done building it, and now we're testing it.
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我哋已經建好網站，現在正在測試。
13:29
People really can learn a language with it.
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人們真嘅可以用佢來學習語言，
13:31
And they learn it about as well as the leading language learning software.
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也可以學得與使用其他領先嘅語言學習軟件一樣好
13:34
So people really do learn a language.
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人們真系可以學一門語言。
13:36
And not only do they learn it as well,
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不單止學得一樣好，
13:38
but actually it's way more interesting.
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佢實際上亦更加有趣。
13:40
Because you see with Duolingo, people are actually learning with real content.
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因為在Duolingo，人們使用真正嘅內容來學習，
13:43
As opposed to learning with made-up sentences,
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相對於用編造嘅句子，
13:45
people are learning with real content, which is inherently interesting.
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人們學緊真嘅內容，從本質上就有趣許多。
13:48
So people really do learn a language.
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人們真嘅學習一種語言。
13:50
But perhaps more surprisingly,
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可能更令人驚奇嘅系，
13:52
the translations that we get from people using the site,
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從用緊哩個網站嘅人——即使佢哋只系初學者，
13:55
even though they're just beginners,
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得到嘅翻譯
13:57
the translations that we get are as accurate as those of professional language translators,
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與果嘀專業嘅翻譯師竟然一樣精確。
14:00
which is very surprising.
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與果嘀專業嘅翻譯師竟然一樣精確。
14:02
So let me show you one example.
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讓我為你展示一個例子。
14:04
This is a sentence that was translated from German into English.
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哩個系一個從德文翻譯成英文嘅句子。
14:06
The top is the German.
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上面系德文。
14:08
The middle is an English translation
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中間系由專業譯者做嘅英文翻譯
14:10
that was done by somebody who was a professional English translator
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中間系由專業譯者做嘅英文翻譯
14:12
who we paid 20 cents a word for this translation.
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我哋為哩個翻譯嘅每個字使咗20美分。
14:14
And the bottom is a translation by users of Duolingo,
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最下面嘅翻譯系由之前完全唔識德文嘅
14:17
none of whom knew any German
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我哋嘅網站嘅用戶做嘅翻譯。
14:19
before they started using the site.
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我哋嘅網站嘅用戶做嘅翻譯。
14:21
You can see, it's pretty much perfect.
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你哋可以見到，哩個翻譯系幾完美嘅。
14:23
Now of course, we play a trick here
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當然，我哋使咗一嘀手段
14:25
to make the translations as good as professional language translators.
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來使哩個翻譯與專業翻譯一樣好。
14:27
We combine the translations of multiple beginners
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我哋將多個用戶嘅翻譯綜合起來，
14:30
to get the quality of a single professional translator.
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來得到與一個專業譯者同樣嘅質量。
14:33
Now even though we're combining the translations,
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而即使我哋綜合多個翻譯者，
14:38
the site actually can translate pretty fast.
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哩個網站嘅翻譯其實幾快。
14:40
So let me show you,
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等我展示一下
14:42
this is our estimates of how fast we could translate Wikipedia
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哩個系我哋估算嘅我哋可以幾快甘將維基百科
14:44
from English into Spanish.
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從英文翻譯為西班牙文。
14:46
Remember, this is 50 million dollars-worth of value.
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留意，哩度有5000萬嘅價值。
14:49
So if we wanted to translate Wikipedia into Spanish,
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如果我哋想將維基百科翻譯為西班牙文
14:51
we could do it in five weeks with 100,000 active users.
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用10萬活躍用戶我哋可以系5週內完成。
14:54
And we could do it in about 80 hours with a million active users.
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用100萬活躍用戶我哋可以系80個種之內完成。
14:57
Since all the projects that my group has worked on so far have gotten millions of users,
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既然我嘅團隊接觸嘅所有項目都達到百萬用戶，
15:00
we're hopeful that we'll be able to translate
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我哋希望可以用哩個項目
15:02
extremely fast with this project.
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極快甘翻譯。
15:04
Now the thing that I'm most excited about with Duolingo
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我對Duolingo最為興奮嘅系
15:07
is I think this provides a fair business model for language education.
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我認佢為語言教育提供咗一種公平交易嘅商業模式。
15:10
So here's the thing:
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系甘樣嘅：
15:12
The current business model for language education
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目前嘅語言教育模式系
15:14
is the student pays,
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學生付費，
15:16
and in particular, the student pays Rosetta Stone 500 dollars.
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特別一提嘅系，學生向Rosetta Stone付500美元。
15:18
(Laughter)
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（笑聲）
15:20
That's the current business model.
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這就係目前嘅商業模式。
15:22
The problem with this business model
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哩種商業模式嘅問題系
15:24
is that 95 percent of the world's population doesn't have 500 dollars.
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95%嘅人口並冇500美元
15:27
So it's extremely unfair towards the poor.
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所以佢對貧窮人口系極唔公平嘅。
15:30
This is totally biased towards the rich.
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佢完全偏向富裕人口。
15:32
Now see, in Duolingo,
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現在，因為你學緊嘅時候
15:34
because while you learn
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現在，因為你學緊嘅時候
15:36
you're actually creating value, you're translating stuff --
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你實際上創造緊價值——
15:39
which for example, we could charge somebody for translations.
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你翻譯緊果嘀本需要使錢翻譯嘅嘢
15:42
So this is how we could monetize this.
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這就係我哋貨幣化學習嘅方法
15:44
Since people are creating value while they're learning,
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既然佢哋學習嘅時候創造緊價值
15:46
they don't have to pay their money, they pay with their time.
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佢哋無需付出金錢，而付出佢哋嘅時間。
15:49
But the magical thing here is that they're paying with their time,
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但神奇嘅在於哩嘀你付出嘅時間
15:52
but that is time that would have had to have been spent anyways
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本事就系要來學語言嘅
15:54
learning the language.
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本事就系要來學語言嘅
15:56
So the nice thing about Duolingo is I think it provides a fair business model --
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所以我講Duolingo做嘅一件好事就系提供咗一個公平嘅商業模式——
15:59
one that doesn't discriminate against poor people.
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一個唔歧視貧窮嘅模式。
16:01
So here's the site. Thank you.
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這就是哩個網站。多謝
16:03
(Applause)
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（掌聲）
16:11
So here's the site.
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哩個就系Duolingo嘅網站。
16:13
We haven't yet launched,
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我們縱未上線，
16:15
but if you go there, you can sign up to be part of our private beta,
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但系如果你去哩個網站，你可以註冊成為非公開測試版本嘅一員。
16:18
which is probably going to start in about three or four weeks.
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測試版本可能在未來三四個星期就會開始。
16:20
We haven't yet launched this Duolingo.
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Duolingo縱未正式上線。
16:22
By the way, I'm the one talking here,
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順便講一句，雖然系我在這裡做這個演講，
16:24
but actually Duolingo is the work of a really awesome team, some of whom are here.
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但實際上Duolingo是一個出色團隊的產品，哩個團隊中嘅一些人今日都系哩度。
16:27
So thank you.
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多謝！
16:29
(Applause)
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掌聲
ABOUT THE SPEAKER
Luis von Ahn - Computer scientistLuis von Ahn builds systems that combine humans and computers to solve large-scale problems that neither can solve alone.
Why you should listen
Louis von Ahn is an associate professor of Computer Science at Carnegie Mellon University, and he's at the forefront of the crowdsourcing craze. His work takes advantage of the evergrowing Web-connected population to acheive collaboration in unprecedented numbers. His projects aim to leverage the crowd for human good. His company reCAPTCHA, sold to Google in 2009, digitizes human knowledge (books), one word at a time. His new project is Duolingo, which aims to get 100 million people translating the Web in every major language.
Luis von Ahn | Speaker | TED.com