TEDxCMU
Luis von Ahn: Massive-scale online collaboration
路易斯･范･安：大型在线共同创作
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路易斯･范･安带来了新一代的验证码，通过真人回复来将扫描书籍电子化。他琢磨这种无数个在线合作还能为我们带来什么更大的益处。他在 TEDxCMU介绍了他的新项目，叫Duolingo。这个项目能帮助成千上万的人学习外语，同时又把网页内容进行快速准确地翻译，而这一切都是免费的。
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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有多少人在填写网页表格时
00:17
where you've been asked to read a distorted sequence of characters like this?
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需要识别像这样扭曲的词语？
00:19
How many of you found it really, really annoying?
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有多少人觉得很烦人？
00:21
Okay, outstanding. So I invented that.
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哇，不少呢。我就是发明这个的人。
00:24
(Laughter)
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（笑声）
00:26
Or I was one of the people who did it.
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或者说我是其中之一
00:28
That thing is called a CAPTCHA.
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这个称作验证码
00:30
And the reason it is there is to make sure you, the entity filling out the form,
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其理由是保证填写表格的是一个真人
00:32
are actually a human and not some sort of computer program
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而不是什么电脑程序在操作
00:35
that was written to submit the form millions and millions of times.
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可以不停地填写表格
00:37
The reason it works is because humans,
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这是因为人类
00:39
at least non-visually-impaired humans,
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至少是没有视力问题的人
00:41
have no trouble reading these distorted squiggly characters,
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可以识别这些扭曲的文字
00:43
whereas computer programs simply can't do it as well yet.
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而机器做不到
00:46
So for example, in the case of Ticketmaster,
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比如说在票务大全网站上
00:48
the reason you have to type these distorted characters
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你输入这些扭曲字符的原因
00:50
is to prevent scalpers from writing a program
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是防止黄牛写一个电脑程序
00:52
that can buy millions of tickets, two at a time.
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一次购买上万张票
00:54
CAPTCHAs are used all over the Internet.
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验证码在网络上普遍应用
00:56
And since they're used so often,
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因其普遍性
00:58
a lot of times the precise sequence of random characters that is shown to the user
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很多时候使用者就会看到一些
01:00
is not so fortunate.
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异常搭配的文字排序
01:02
So this is an example from the Yahoo registration page.
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这个例子来自雅虎注册网页
01:05
The random characters that happened to be shown to the user
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使用者看到的这几个随机字母
01:07
were W, A, I, T, which, of course, spell a word.
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W，A，I， T，正好组成了“等”
01:10
But the best part is the message
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最有意思的是
01:13
that the Yahoo help desk got about 20 minutes later.
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这是20分钟后的帮助页面
01:16
Text: "Help! I've been waiting for over 20 minutes, and nothing happens."
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文字：“帮忙！我已经等了二十多分钟，没有任何变化。”
01:19
(Laughter)
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（笑声）
01:23
This person thought they needed to wait.
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这人以为网站让他等着
01:25
This of course, is not as bad as this poor person.
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当然还有更倒霉的
01:28
(Laughter)
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（笑声）
01:30
CAPTCHA Project is something that we did here at Carnegie Melllon over 10 years ago,
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验证码计划是我们十多年前在卡内基梅隆大学做起来的
01:33
and it's been used everywhere.
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并被广泛应用
01:35
Let me now tell you about a project that we did a few years later,
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现在谈谈几年后我们做的一个项目
01:37
which is sort of the next evolution of CAPTCHA.
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算是验证码的新生代版本
01:40
This is a project that we call reCAPTCHA,
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这个计划我们称之“reCAPTCHA”
01:42
which is something that we started here at Carnegie Mellon,
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这个计划是从卡内基梅隆大学起步
01:44
then we turned it into a startup company.
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成为我们的启动公司
01:46
And then about a year and a half ago,
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一年半前
01:48
Google actually acquired this company.
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谷歌收购了这个公司
01:50
So let me tell you what this project started.
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现在我来说说这个项目的初始
01:52
So this project started from the following realization:
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这个项目是出于以下认识：
01:55
It turns out that approximately 200 million CAPTCHAs
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每天全球范围内有大约2亿次
01:57
are typed everyday by people around the world.
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验证码输入
02:00
When I first heard this, I was quite proud of myself.
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我头次听到的时候还挺自豪
02:02
I thought, look at the impact that my research has had.
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我想 我们的研究影响力不小啊
02:04
But then I started feeling bad.
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接着我就感觉很难受
02:06
See here's the thing, each time you type a CAPTCHA,
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因为每次你输入一个验证码
02:08
essentially you waste 10 seconds of your time.
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你就浪费了10秒钟
02:11
And if you multiply that by 200 million,
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这个乘以2亿
02:13
you get that humanity as a whole is wasting about 500,000 hours every day
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全人类每天就浪费了50万个小时
02:16
typing these annoying CAPTCHAs.
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来输入烦人的验证码
02:18
So then I started feeling bad.
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我就很难受了
02:20
(Laughter)
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（笑声）
02:22
And then I started thinking, well, of course, we can't just get rid of CAPTCHAs,
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我开始思考 既然不能放弃验证码
02:25
because the security of the Web sort of depends on them.
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因为网页安全依赖于此
02:27
But then I started thinking, is there any way we can use this effort
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那么有什么方法可以利用它
02:30
for something that is good for humanity?
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来做点好事呢？
02:32
So see, here's the thing.
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关键在于
02:34
While you're typing a CAPTCHA, during those 10 seconds,
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当你在10秒钟内输入验证码的时候
02:36
your brain is doing something amazing.
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你的大脑在做了不起的工作
02:38
Your brain is doing something that computers cannot yet do.
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这是电脑目前尚无法做到的
02:40
So can we get you to do useful work for those 10 seconds?
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那么能不能让这10秒钟的工作变得有意义呢？
02:43
Another way of putting it is,
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也就是说
02:45
is there some humongous problem that we cannot yet get computers to solve,
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有没有什么目前电脑无法解决的难题
02:47
yet we can split into tiny 10-second chunks
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但是可以分割成10秒的单位小块
02:50
such that each time somebody solves a CAPTCHA
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这样每个人通过验证码
02:52
they solve a little bit of this problem?
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解决这个问题的一个小单位？
02:54
And the answer to that is "yes," and this is what we're doing now.
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答案是肯定的话 这就是我们目前在做的
02:56
So what you may not know is that nowadays while you're typing a CAPTCHA,
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也许你不知道 如今当你输入一个验证码
02:59
not only are you authenticating yourself as a human,
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不仅仅是在证明你是真人
03:01
but in addition you're actually helping us to digitize books.
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也是在把书电子化
03:03
So let me explain how this works.
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我来解释一下
03:05
So there's a lot of projects out there trying to digitize books.
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目前有很多书籍电子化的项目
03:07
Google has one. The Internet Archive has one.
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谷歌有一个。 “互联网档案”有一个
03:10
Amazon, now with the Kindle, is trying to digitize books.
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现亚马逊的Kindle也有一个
03:12
Basically the way this works
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方法就是
03:14
is you start with an old book.
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从一本旧书开始
03:16
You've seen those things, right? Like a book?
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你见过书对吧？一本书？
03:18
(Laughter)
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（笑声）
03:20
So you start with a book, and then you scan it.
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首先扫描一本书
03:22
Now scanning a book
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扫描就是
03:24
is like taking a digital photograph of every page of the book.
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相当于把每一页照一张数码照片
03:26
It gives you an image for every page of the book.
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你就有了这本书每一页的照片
03:28
This is an image with text for every page of the book.
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这是一本书每一页文字内容的照片
03:30
The next step in the process
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下一步就是
03:32
is that the computer needs to be able to decipher all of the words in this image.
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电脑得解读这些照片上的每一个字
03:35
That's using a technology called OCR,
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这涉及到一个叫做OCR的技术
03:37
for optical character recognition,
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也就是光学字符识别
03:39
which takes a picture of text
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拍下一段文字的照片
03:41
and tries to figure out what text is in there.
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然后识别出文字内容
03:43
Now the problem is that OCR is not perfect.
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问题是光学字符识别的技术并不能解决所有问题
03:45
Especially for older books
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特别对于旧书
03:47
where the ink has faded and the pages have turned yellow,
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墨水褪色，书页泛黄
03:50
OCR cannot recognize a lot of the words.
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很多字OCR无法识别
03:52
For example, for things that were written more than 50 years ago,
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比如，五十多年前的书
03:54
the computer cannot recognize about 30 percent of the words.
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有百分之三十的单词电脑无法识别
03:57
So what we're doing now
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我们做的就是
03:59
is we're taking all of the words that the computer cannot recognize
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摘录出电脑无法识别的单词
04:01
and we're getting people to read them for us
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通过真人在网上输入验证码时
04:03
while they're typing a CAPTCHA on the Internet.
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阅读识别出来
04:05
So the next time you type a CAPTCHA, these words that you're typing
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下次当你输入一个验证码时，你输入的那个单词
04:08
are actually words that are coming from books that are being digitized
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实际是我们电子化书籍里
04:11
that the computer could not recognize.
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电脑无法识别的单词
04:13
And now the reason we have two words nowadays instead of one
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现在我们使用两个而非一个单词的理由是
04:15
is because, you see, one of the words
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其中一个词是
04:17
is a word that the system just got out of a book,
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系统把一个电脑无法识别的单词
04:19
it didn't know what it was, and it's going to present it to you.
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提供给你
04:22
But since it doesn't know the answer for it, it cannot grade it for you.
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因为系统不认识这个单词 所以无法判断你的答案
04:25
So what we do is we give you another word,
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我们就加入另一个单词
04:27
one for which the system does know the answer.
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一个系统已经认识的单词
04:29
We don't tell you which one's which, and we say, please type both.
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不告诉你哪个是已知的，哪个是未知的 请你将两者都输入
04:31
And if you type the correct word
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如果你能拼写正确
04:33
for the one for which the system already knows the answer,
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系统已认知的那个单词
04:35
it assumes you are human,
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就判断你为真人
04:37
and it also gets some confidence that you typed the other word correctly.
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这样对你输入的另一个单词就有所把握
04:39
And if we repeat this process to like 10 different people
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我们把这个过程让十个人重复进行
04:42
and all of them agree on what the new word is,
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如果他们对不识别单词的答案一致
04:44
then we get one more word digitized accurately.
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我们就得到了一个准确电子化的新单词
04:46
So this is how the system works.
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这就是这个系统的工作原理
04:48
And basically, since we released it about three or four years ago,
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大约三四年前我们导入这个系统
04:51
a lot of websites have started switching
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许多网站已经从旧的验证码
04:53
from the old CAPTCHA where people wasted their time
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换成新的来帮助书籍电子化
04:55
to the new CAPTCHA where people are helping to digitize books.
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而不是浪费人们的时间
04:57
So for example, Ticketmaster.
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比如“票务大全”
04:59
So every time you buy tickets on Ticketmaster, you help to digitize a book.
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每次你在它的网站上购票 就在帮助把书籍电子化
05:02
Facebook: Every time you add a friend or poke somebody,
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脸书：每次你加好友或者打招呼
05:04
you help to digitize a book.
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你就帮忙在把书籍电子化
05:06
Twitter and about 350,000 other sites are all using reCAPTCHA.
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推特和其他350,000个网站都在用reCAPTCHA
05:09
And in fact, the number of sites that are using reCAPTCHA is so high
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现在使用reCAPTCHA的网站是如此之多
05:11
that the number of words that we're digitizing per day is really, really large.
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每天我们电子化的单词数量惊人
05:14
It's about 100 million a day,
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大概是每天一亿
05:16
which is the equivalent of about two and a half million books a year.
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这就是每年大概250万本书
05:20
And this is all being done one word at a time
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而这一切仅仅都是通过人们在网上
05:22
by just people typing CAPTCHAs on the Internet.
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输入验证码来做到的
05:24
(Applause)
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（掌声）
05:32
Now of course,
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当然
05:34
since we're doing so many words per day,
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因为每天处理的词是如此之多
05:36
funny things can happen.
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难免有搞笑的状况
05:38
And this is especially true because now we're giving people
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特别是现在我们给出的单词是
05:40
two randomly chosen English words next to each other.
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两个随机组合的英语单词
05:42
So funny things can happen.
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就出现了有意思的事
05:44
For example, we presented this word.
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比如 我们给出了这个词
05:46
It's the word "Christians"; there's nothing wrong with it.
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“基督徒” 这没什么问题
05:48
But if you present it along with another randomly chosen word,
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问题是另外一个随机抽取的词
05:51
bad things can happen.
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就把事情搞糟了
05:53
So we get this. (Text: bad christians)
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比如这个 （恶基督徒）
05:55
But it's even worse, because the particular website where we showed this
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更糟的是 出现这个的网站
05:58
actually happened to be called The Embassy of the Kingdom of God.
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正好是“神之国度大使馆”
06:01
(Laughter)
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（笑声）
06:03
Oops.
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糟了
06:05
(Laughter)
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（笑声）
06:08
Here's another really bad one.
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这儿还有一个
06:10
JohnEdwards.com
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JohnEdwards.com
06:12
(Text: Damn liberal)
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（该死的自由主义者）
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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我们就这么每天不停地羞辱别人
06:20
Now, of course, we're not just insulting people.
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当然 不仅是人
06:22
See here's the thing, since we're presenting two randomly chosen words,
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其他东西也难逃厄运 因为我们是随机选取的单词
06:25
interesting things can happen.
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就有了很有趣的结果
06:27
So this actually has given rise
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这个正在成为
06:29
to a really big Internet meme
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互联网上一个流行趋势
06:32
that tens of thousands of people have participated in,
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无数的人参与这个
06:34
which is called CAPTCHA art.
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所谓的验证码艺术
06:36
I'm sure some of you have heard about it.
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肯定有人听说过
06:38
Here's how it works.
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是这样
06:40
Imagine you're using the Internet and you see a CAPTCHA
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假设你在上网看到一个验证码
06:42
that you think is somewhat peculiar,
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你觉得很特别
06:44
like this CAPTCHA. (Text: invisible toaster)
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比如这个 （隐形的烤面包机）
06:46
Then what you're supposed to do is you take a screen shot of it.
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你要做的就是截图
06:48
Then of course, you fill out the CAPTCHA
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然后当然就是输入验证码
06:50
because you help us digitize a book.
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因为你在帮我们电子化书籍
06:52
But then, first you take a screen shot,
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接下来 你截了图
06:54
and then you draw something that is related to it.
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就画出与它相关的图像
06:56
(Laughter)
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（笑声）
06:58
That's how it works.
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就是这样
07:01
There are tens of thousands of these.
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这样作品大概有一万个
07:04
Some of them are very cute. (Text: clenched it)
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有些很可爱 （握紧它）
07:06
(Laughter)
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（笑声）
07:08
Some of them are funnier.
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有些很好玩
07:10
(Text: stoned founders)
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（大醉的创始人）
07:13
(Laughter)
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（笑声）
07:16
And some of them,
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还有一些
07:18
like paleontological shvisle,
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比如 “古生物学的史维凿”
07:21
they contain Snoop Dogg.
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说不定那儿有史诺谱･道格（美国说唱歌手）
07:23
(Laughter)
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（笑声）
07:26
Okay, so this is my favorite number of reCAPTCHA.
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这是我最喜欢的reCAPTCHA数字
07:28
So this is the favorite thing that I like about this whole project.
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这是我最喜欢的这个项目的部分
07:31
This is the number of distinct people
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这个数字是
07:33
that have helped us digitize at least one word out of a book through reCAPTCHA:
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通过reCAPTCHA帮助我们电子化书籍中单词的人数
07:36
750 million,
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7.5亿
07:38
which is a little over 10 percent of the world's population,
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多于世界总人口的十分之一的人们
07:40
has helped us digitize human knowledge.
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帮助我们电子化人类的知识
07:42
And it is numbers like these that motivate my research agenda.
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正是这样的数字激励我的研究进程
07:45
So the question that motivates my research is the following:
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那激励我研究进程的问题如下：
07:48
If you look at humanity's large-scale achievements,
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试想人类的大型成就
07:50
these really big things
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人类共同
07:52
that humanity has gotten together and done historically --
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创造的那些大型历史性事物－
07:55
like for example, building the pyramids of Egypt
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比如 建造埃及金字塔
07:57
or the Panama Canal
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开凿巴拿马运河
07:59
or putting a man on the Moon --
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或者把人类送上月球－
08:01
there is a curious fact about them,
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这些工程都有个奇怪的事实
08:03
and it is that they were all done with about the same number off people.
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就是它们基本都是由一样数量的人们完成的
08:05
It's weird; they were all done with about 100,000 people.
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这很奇怪 这些工程都是由大概十万人完成
08:08
And the reason for that is because, before the Internet,
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因为在互联网出现之前
08:11
coordinating more than 100,000 people,
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整合十万人
08:13
let alone paying them, was essentially impossible.
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这十万人的巨大酬劳基本上是无法支付的
08:16
But now with the Internet, I've just shown you a project
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但是有了互联网 刚刚展示的这个项目
08:18
where we've gotten 750 million people
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就找到了7.5亿人
08:20
to help us digitize human knowledge.
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来帮助我们电子化人类知识
08:22
So the question that motivates my research is,
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那么 激励我的研究的问题就是
08:24
if we can put a man on the Moon with 100,000,
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如果十万人能把一个人送上月球
08:27
what can we do with 100 million?
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一亿人能做到什么呢？
08:29
So based on this question,
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基于这个问题
08:31
we've had a lot of different projects that we've been working on.
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我们有很多项目在进行中
08:33
Let me tell you about one that I'm most excited about.
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下面介绍一个最令我兴奋的项目
08:36
This is something that we've been semi-quietly working on
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这是过去一年半里
08:38
for the last year and a half or so.
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我们低调进行的一个项目
08:40
It hasn't yet been launched. It's called Duolingo.
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还没有真正投入使用 它叫做Duolingo
08:42
Since it hasn't been launched, shhhhh!
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因为我们还没有投入使用 嘘！
08:44
(Laughter)
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（笑声）
08:46
Yeah, I can trust you'll do that.
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我相信你们都会保密的
08:48
So this is the project. Here's how it started.
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这个项目是这样开始的
08:50
It started with me posing a question to my graduate student,
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它始于我向我的一名研究生提的问题
08:52
Severin Hacker.
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塞韦林･骇客
08:54
Okay, that's Severin Hacker.
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这就是他
08:56
So I posed the question to my graduate student.
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我向他提了一个问题
08:58
By the way, you did hear me correctly;
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另外你确实没听错
09:00
his last name is Hacker.
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他姓骇客
09:02
So I posed this question to him:
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我向他提了个问题：
09:04
How can we get 100 million people
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怎么才能找到一亿人
09:06
translating the Web into every major language for free?
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把互联网上的内容免费翻译成所有的主要语言？
09:09
Okay, so there's a lot of things to say about this question.
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这个问题有好几个方面
09:11
First of all, translating the Web.
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首先是翻译网页
09:13
So right now the Web is partitioned into multiple languages.
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现在的网页内容主要分为几大语言
09:16
A large fraction of it is in English.
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其中一个大的分支是英语
09:18
If you don't know any English, you can't access it.
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如果你不会英语就无法使用
09:20
But there's large fractions in other different languages,
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但是还有其他几种不同的语言
09:22
and if you don't know those languages, you can't access it.
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如果你不会那几种也无法使用
09:25
So I would like to translate all of the Web, or at least most of the Web,
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我打算把所有网页 大部分网页
09:28
into every major language.
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翻译成主要语言
09:30
So that's what I would like to do.
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这是我想做的
09:32
Now some of you may say, why can't we use computers to translate?
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也许有人会说 怎么不用电脑翻译？
09:35
Why can't we use machine translation?
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为什么我们不用机器翻译？
09:37
Machine translation nowadays is starting to translate some sentences here and there.
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机器翻译已经在应用中
09:39
Why can't we use it to translate the whole Web?
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为什么不用它来翻译所有网页呢？
09:41
Well the problem with that is that it's not yet good enough
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问题就是机器翻译还不够好
09:43
and it probably won't be for the next 15 to 20 years.
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也许将来的15到20年内都不行
09:45
It makes a lot of mistakes.
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机器翻译有很多错误
09:47
Even when it doesn't make a mistake,
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甚至就算它翻对的时候
09:49
since it makes so many mistakes, you don't know whether to trust it or not.
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因为它的错误率太高 你也不敢相信它
09:52
So let me show you an example
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比如这个例子
09:54
of something that was translated with a machine.
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是由机器翻译的
09:56
Actually it was a forum post.
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这是个论坛帖子
09:58
It was somebody who was trying to ask a question about JavaScript.
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有人提了关于Java语言的一个问题
10:01
It was translated from Japanese into English.
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是从日语翻译成英语
10:04
So I'll just let you read.
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你可以读一下
10:06
This person starts apologizing
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他首先道歉
10:08
for the fact that it's translated with a computer.
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这是机器翻译的内容
10:10
So the next sentence is is going to be the preamble to the question.
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下一个句子开始涉及问题
10:13
So he's just explaining something.
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他开始说明
10:15
Remember, it's a question about JavaScript.
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记住 这个问题是关于Java语言的
10:19
(Text: At often, the goat-time install a error is vomit.)
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（文字：常常，山羊时间启动错误被吐出来）
10:23
(Laughter)
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（笑声）
10:27
Then comes the first part of the question.
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接下来是问题的第一部分
10:30
(Text: How many times like the wind, a pole, and the dragon?)
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（文字：有多少次像风，像杆子，像龙？）
10:34
(Laughter)
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（笑声）
10:36
Then comes my favorite part of the question.
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接下来是最好玩的部分
10:39
(Text: This insult to father's stones?)
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（文字：这是对父亲的石头的侮辱？）
10:42
(Laughter)
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（笑声）
10:44
And then comes the ending, which is my favorite part of the whole thing.
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接下来是结尾 我最喜欢的部分
10:47
(Text: Please apologize for your stupidity. There are a many thank you.)
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（文字：请为你的愚蠢道歉，很多谢谢）
10:51
(Laughter)
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（笑声）
10:53
Okay, so computer translation, not yet good enough.
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可见 机器翻译 还不够好
10:55
So back to the question.
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回到问题上去
10:57
So we need people to translate the whole Web.
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我们需要人来翻译所有网页
11:00
So now the next question you may have is,
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下一个问题是
11:02
well why can't we just pay people to do this?
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为什么不付钱找人做呢？
11:04
We could pay professional language translators to translate the whole Web.
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我们可以找专业翻译人员来翻译整个网页
11:07
We could do that.
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可以这么做
11:09
Unfortunately, it would be extremely expensive.
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但是 这会无比昂贵
11:11
For example, translating a tiny, tiny fraction of the whole Web, Wikipedia,
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比如 翻译互联网很小很小的一个部分 维基百科
11:14
into one other language, Spanish.
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英语翻译成西班牙语
11:17
Wikipedia exists in Spanish,
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维基百科有西班牙语
11:19
but it's very small compared to the size of English.
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但是相比英语部分小多了
11:21
It's about 20 percent of the size of English.
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大概是英语内容的百分之二十
11:23
If we wanted to translate the other 80 percent into Spanish,
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如果我们把剩下的百分之八十翻译成英语
11:26
it would cost at least 50 million dollars --
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就得至少五千万美元－
11:28
and this is at even the most exploited, outsourcing country out there.
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这还是在最便宜的服务外包国家
11:31
So it would be very expensive.
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所以这个方法很昂贵
11:33
So what we want to do is we want to get 100 million people
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我们要的是一亿人
11:35
translating the Web into every major language
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免费把网页内容翻译成
11:37
for free.
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所有主要语言
11:39
Now if this is what you want to do,
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如果你要这么做的话
11:41
you pretty quickly realize you're going to run into two pretty big hurdles,
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就会意识到面临两个非常
11:43
two big obstacles.
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巨大的障碍
11:45
The first one is a lack of bilinguals.
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首先是缺乏掌握双语的人
11:48
So I don't even know
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我甚至不知道
11:50
if there exists 100 million people out there using the Web
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是否有一亿个互联网使用者
11:53
who are bilingual enough to help us translate.
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掌握双语来进行翻译
11:55
That's a big problem.
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这是个大问题
11:57
The other problem you're going to run into is a lack of motivation.
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另一个问题是缺少鼓励机制
11:59
How are we going to motivate people
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怎么才能让人们
12:01
to actually translate the Web for free?
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甘愿免费翻译网页？
12:03
Normally, you have to pay people to do this.
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通常你得付钱请人干活儿
12:06
So how are we going to motivate them to do it for free?
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那么怎么才能让他们无偿劳动呢？
12:08
Now when we were starting to think about this, we were blocked by these two things.
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当我们着手考虑这个项目的时候这是拦在面前的两大问题
12:11
But then we realized, there's actually a way
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后来我们意识到
12:13
to solve both these problems with the same solution.
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有一个方法可以一举解决这两个问题
12:15
There's a way to kill two birds with one stone.
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一箭双雕
12:17
And that is to transform language translation
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这就是把翻译转化成
12:20
into something that millions of people want to do,
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无数人想做的事情
12:23
and that also helps with the problem of lack of bilinguals,
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同时解决双语人员人手不够的问题
12:26
and that is language education.
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这就是语言学习
12:29
So it turns out that today,
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当今
12:31
there are over 1.2 billion people learning a foreign language.
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有超过12亿人口在学习外语
12:34
People really, really want to learn a foreign language.
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人们迫切得想学习外语
12:36
And it's not just because they're being forced to do so in school.
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而且这不是学校里不得不做的功课
12:39
For example, in the United States alone,
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比如在美国
12:41
there are over five million people who have paid over $500
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有超过五百万的人在为外语学习软件
12:43
for software to learn a new language.
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每人支付超过五百美元
12:45
So people really, really want to learn a new language.
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所有人们非常想学外语
12:47
So what we've been working on for the last year and a half is a new website --
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过去一年半里我们建立的新网站
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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就是基于这个让人们免费学习外语
12:55
while simultaneously translating the Web.
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同时翻译网页的想法
12:57
And so basically they're learning by doing.
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就是让他们学以致用
12:59
So the way this works
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使用方法是这样
13:01
is whenever you're a just a beginner, we give you very, very simple sentences.
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如果你是个新手 我们会给出非常非常简单的句子
13:04
There's, of course, a lot of very simple sentences on the Web.
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网页上有很多简单的句子
13:06
We give you very, very simple sentences
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我们给出非常简单的句子
13:08
along with what each word means.
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以及句中单词释义
13:10
And as you translate them, and as you see how other people translate them,
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然后你翻译一下 并且可以看到别人是如何翻译的
13:13
you start learning the language.
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这样学习外语
13:15
And as you get more and more advanced,
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当你级别提高后
13:17
we give you more and more complex sentences to translate.
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我们会给出越来越复杂的句子让你翻译
13:19
But at all times, you're learning by doing.
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这整个过程 你都是边学边用
13:21
Now the crazy thing about this method
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这个方法令人疯狂之处
13:23
is that it actually really works.
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是它居然确实有效
13:25
First of all, people are really, really learning a language.
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首先 人们可以通过它学外语
13:27
We're mostly done building it, and now we're testing it.
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我们建完了网站，它现正在测试中
13:29
People really can learn a language with it.
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人们可以用它学习外语
13:31
And they learn it about as well as the leading language learning software.
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完全可以跟外语学习软件媲美
13:34
So people really do learn a language.
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所以用它确实可以学外语
13:36
And not only do they learn it as well,
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不仅可以学好
13:38
but actually it's way more interesting.
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而且更有趣味性
13:40
Because you see with Duolingo, people are actually learning with real content.
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因为通过Duolingo人们学的是真正的语言使用内容
13:43
As opposed to learning with made-up sentences,
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而不是编造的句子
13:45
people are learning with real content, which is inherently interesting.
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通过学习真正的文本内容，趣味性大大提高
13:48
So people really do learn a language.
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这样人们就实实在在学习外语
13:50
But perhaps more surprisingly,
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最令人惊讶的是
13:52
the translations that we get from people using the site,
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网站使用者的翻译
13:55
even though they're just beginners,
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甚至是初学者的翻译
13:57
the translations that we get are as accurate as those of professional language translators,
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和专业的翻译人员几乎不相上下
14:00
which is very surprising.
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这很让人惊讶
14:02
So let me show you one example.
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让我给你们看一个例子
14:04
This is a sentence that was translated from German into English.
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这是一个从德语翻译成英语的例子
14:06
The top is the German.
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上面是德语
14:08
The middle is an English translation
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中间是一名专业英语翻译人员
14:10
that was done by somebody who was a professional English translator
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翻译的句子
14:12
who we paid 20 cents a word for this translation.
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一个词二十美分的价钱
14:14
And the bottom is a translation by users of Duolingo,
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下面是Duolingo使用者的翻译
14:17
none of whom knew any German
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他们在使用该网站前
14:19
before they started using the site.
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不会任何德语
14:21
You can see, it's pretty much perfect.
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可以看到 几乎很完美
14:23
Now of course, we play a trick here
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当然 为了让翻译达到专业水准
14:25
to make the translations as good as professional language translators.
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我们也想了个办法
14:27
We combine the translations of multiple beginners
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我们把多名翻译者的翻译结合起来
14:30
to get the quality of a single professional translator.
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得到专业人员的水准
14:33
Now even though we're combining the translations,
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即使我们要结合翻译
14:38
the site actually can translate pretty fast.
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这个网站仍然可以迅速翻译
14:40
So let me show you,
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让我展示一下
14:42
this is our estimates of how fast we could translate Wikipedia
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这是我们对维基百科翻译工程的预计
14:44
from English into Spanish.
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从英语翻译成西班牙语
14:46
Remember, this is 50 million dollars-worth of value.
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要记住 这可是价值五千万美元的工程
14:49
So if we wanted to translate Wikipedia into Spanish,
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如果要把维基百科从英文翻译成西班牙语
14:51
we could do it in five weeks with 100,000 active users.
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十万名活跃用户可以在五周内完成
14:54
And we could do it in about 80 hours with a million active users.
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一百万活跃用户可以在八十小时内完成
14:57
Since all the projects that my group has worked on so far have gotten millions of users,
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现在我们的项目小组已经有了上百万使用者
15:00
we're hopeful that we'll be able to translate
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我们希望可以以极快的速度
15:02
extremely fast with this project.
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进行这个翻译工程
15:04
Now the thing that I'm most excited about with Duolingo
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现在我对Duolingo最兴奋的就是
15:07
is I think this provides a fair business model for language education.
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它为外语教育创造了一个公平的商业模式
15:10
So here's the thing:
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是这样：
15:12
The current business model for language education
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目前外语教育的商业模式是
15:14
is the student pays,
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学生付钱
15:16
and in particular, the student pays Rosetta Stone 500 dollars.
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主要就是学生购买罗赛塔石碑五百美元的软件
15:18
(Laughter)
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（笑声）
15:20
That's the current business model.
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这是目前的商业模式
15:22
The problem with this business model
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这个模式的问题是
15:24
is that 95 percent of the world's population doesn't have 500 dollars.
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世界人口的百分之九十五没有五百美元
15:27
So it's extremely unfair towards the poor.
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所以这个模式对穷人极度不公平
15:30
This is totally biased towards the rich.
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这是个面向富人的模式
15:32
Now see, in Duolingo,
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而Duolingo
15:34
because while you learn
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因为你学习的时候
15:36
you're actually creating value, you're translating stuff --
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也创造价值，你在翻译东西－
15:39
which for example, we could charge somebody for translations.
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因为比如我们得付钱给人翻译东西
15:42
So this is how we could monetize this.
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这样你的学习过程就货币化了
15:44
Since people are creating value while they're learning,
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因为人们学习的时候同时创造价值
15:46
they don't have to pay their money, they pay with their time.
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他们就不用付钱 而是付出时间
15:49
But the magical thing here is that they're paying with their time,
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最妙的是 虽然人们得付出时间
15:52
but that is time that would have had to have been spent anyways
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但这个时间是他们学习外语无论如何
15:54
learning the language.
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都会付出的那部分时间
15:56
So the nice thing about Duolingo is I think it provides a fair business model --
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所以Duolingo做的好事就是提供了一个公平的商业模式－
15:59
one that doesn't discriminate against poor people.
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这个模式对穷人一样敞开机会
16:01
So here's the site. Thank you.
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这就是这个网站 谢谢
16:03
(Applause)
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（掌声）
16:11
So here's the site.
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这个网站
16:13
We haven't yet launched,
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我们还没有投入应用
16:15
but if you go there, you can sign up to be part of our private beta,
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但是如果你去我们的页面的话可以注册
16:18
which is probably going to start in about three or four weeks.
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也许三四周后就可以开始了
16:20
We haven't yet launched this Duolingo.
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我们还没有投入使用Duolingo
16:22
By the way, I'm the one talking here,
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另外 虽然是我在这里介绍Duolingo
16:24
but actually Duolingo is the work of a really awesome team, some of whom are here.
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但这个网站是一个优秀的团队的成果 这是其中一些人
16:27
So thank you.
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谢谢你们
16:29
(Applause)
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（掌声）
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