TED2013
Ajit Narayanan: A word game to communicate in any language
阿吉特：纳拉亚南: 一个能用任何语言交流的文字游戏
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在帮助有语言障碍的自闭症儿童的工作中，阿吉特-纳拉亚南勾画出一种用图片来思考语言的方法,将文字和概念用“地图”联系在一起。这个想法现在成就了一个应用，它帮助有语言缺陷的人们交流，而在这背后的大构想，一个叫做“Free Speech”的语言概念，拥有激动人心的潜力。
Ajit Narayanan - Visual grammar engine inventor
Ajit Narayanan is the inventor of Avaz, an affordable, tablet-based communication device for people who are speech-impaired. Full bio
Ajit Narayanan is the inventor of Avaz, an affordable, tablet-based communication device for people who are speech-impaired. Full bio
Double-click the English transcript below to play the video.
00:12
I work with children with autism.
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我从事关于自闭症儿童的工作。
00:15
Specifically, I make technologies
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具体而言，我发明技术
00:17
to help them communicate.
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来帮助他们交流。
00:19
Now, many of the problems that children
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许多自闭症儿童所面临的问题，
00:21
with autism face, they have a common source,
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它们有一个共同的根源，
00:24
and that source is that they find it difficult
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而这个根源就是他们觉得
00:26
to understand abstraction, symbolism.
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很难理解抽象概念和符号。
00:32
And because of this, they have
a lot of difficulty with language.
a lot of difficulty with language.
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因此，他们在语言上存在很大的困难。
00:36
Let me tell you a little bit about why this is.
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让我来告诉你们一些会这样的原因
00:39
You see that this is a picture of a bowl of soup.
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你看这是一碗汤的图片。
00:43
All of us can see it. All of us understand this.
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我们都可以看见它。我们都能理解这个。
00:46
These are two other pictures of soup,
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这里是另外两张汤的图片。
00:48
but you can see that these are more abstract
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但是你可以看到，这些更加抽象。
00:50
These are not quite as concrete.
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这些并不那么具体。
00:52
And when you get to language,
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而当你使用语言的时候，
00:54
you see that it becomes a word
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你看它变成了一个单词
00:56
whose look, the way it looks and the way it sounds,
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它看起来，听起来
00:59
has absolutely nothing to do
with what it started with,
with what it started with,
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都跟刚开始的或代表的一碗汤，
01:02
or what it represents, which is the bowl of soup.
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没有半点关系。
01:05
So it's essentially a completely abstract,
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所以它本质上是完全抽象的，
01:08
a completely arbitrary representation of something
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一个对现实生活中事物
01:10
which is in the real world,
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很随意的陈述，
01:12
and this is something that children with autism
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这才是自闭症儿童
01:13
have an incredible amount of difficulty with.
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有极大困难的地方。
01:17
Now that's why most of the people
that work with children with autism --
that work with children with autism --
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这就是为什么如今大多数从事自闭症儿童工作的人
01:19
speech therapists, educators --
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——语言治疗师们，教育者们
01:21
what they do is, they try to help children with autism
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——他们所做的是帮助自闭症儿童交流，
01:24
communicate not with words, but with pictures.
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不是通过文字，而是通过图片。
01:27
So if a child with autism wanted to say,
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于是如果一个自闭症儿童想说，
01:29
"I want soup," that child would pick
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"I want soup"(我要汤)，那个孩子将会拿起
01:31
three different pictures, "I," "want," and "soup,"
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三张不同的图片，"I"(我)，"want"(要)和"soup"(汤)
01:34
and they would put these together,
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他们会将这些拼凑在一起，
01:35
and then the therapist or the parent would
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然后治疗师或者家长就能理解
01:37
understand that this is what the kid wants to say.
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这就是孩子想要说的。
01:39
And this has been incredibly effective;
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而且这已经卓有成效了；
01:41
for the last 30, 40 years
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在过去的30，40年间
01:43
people have been doing this.
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人们一直在这样做。
01:45
In fact, a few years back,
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事实上，几年前，
01:46
I developed an app for the iPad
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我在iPad上开发了一个app
01:49
which does exactly this. It's called Avaz,
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就是这样的运作的。它叫做Avaz，
01:51
and the way it works is that kids select
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它是这样运行的：孩子们选择
01:53
different pictures.
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不同的图片。
01:55
These pictures are sequenced
together to form sentences,
together to form sentences,
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这些图片排列成句子，
01:57
and these sentences are spoken out.
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然后这些句子被读出来。
01:59
So Avaz is essentially converting pictures,
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所以 Avaz 基本上在转译图片，
02:02
it's a translator, it converts pictures into speech.
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他是个翻译器，它将图片翻译成语音。
02:06
Now, this was very effective.
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现在，这已经很有效了。
02:07
There are thousands of children using this,
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有数千个孩子正在使用这个，
02:09
you know, all over the world,
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你知道的，遍布全世界，
02:10
and I started thinking about
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于是我开始思考
02:12
what it does and what it doesn't do.
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它可以做的和不能做的地方。
02:15
And I realized something interesting:
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我发现了一些有趣的地方：
02:17
Avaz helps children with autism learn words.
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Avaz 可以帮助自闭症儿童学习单词。
02:21
What it doesn't help them do is to learn
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它没做到的是帮助他们学习
02:23
word patterns.
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文字模式。
02:26
Let me explain this in a little more detail.
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让我更具体地解释下这个。
02:29
Take this sentence: "I want soup tonight."
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比如这句话："I want soup tonight"(我要汤今晚)。
02:32
Now it's not just the words
here that convey the meaning.
here that convey the meaning.
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它不仅仅是文字传达了意思。
02:36
It's also the way in which these words are arranged,
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这些文字的排列方式也起到了作用，
02:39
the way these words are modified and arranged.
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文字被修饰和排列的方式。
02:41
And that's why a sentence like "I want soup tonight"
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那就是为什么一句话比如"I want soup tonight"(我要汤今晚)
02:44
is different from a sentence like
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不同于这句
02:46
"Soup want I tonight," which
is completely meaningless.
is completely meaningless.
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"Soup want I tonight"(汤要我今晚)，后者是没意义 的。
02:49
So there is another hidden abstraction here
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所以这里有另一个隐藏的抽象概念
02:52
which children with autism find
a lot of difficulty coping with,
a lot of difficulty coping with,
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自闭症儿童很难应对，
02:55
and that's the fact that you can modify words
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那就是，你可以修辞文字
02:58
and you can arrange them to have
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你可以对他们进行排序
03:00
different meanings, to convey different ideas.
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来得到不同的含义，来表达不同的思想。
03:03
Now, this is what we call grammar.
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这就是我们所说的语法。
03:07
And grammar is incredibly powerful,
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而且语法极其有用的，
03:09
because grammar is this one component of language
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因为语法是语言的一个这样的组成部分。
03:12
which takes this finite vocabulary that all of us have
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它用我们所拥有的有限的词汇
03:15
and allows us to convey an
infinite amount of information,
infinite amount of information,
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使我们表达无限的信息，
03:20
an infinite amount of ideas.
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无限的思想。
03:22
It's the way in which you can put things together
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这是一种你可以将事物连在一起
03:24
in order to convey anything you want to.
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来表达任何你所要表达的。
03:26
And so after I developed Avaz,
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于是在我开发了 Avaz 之后，
03:28
I worried for a very long time
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我担心过很长一段时间
03:30
about how I could give grammar
to children with autism.
to children with autism.
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关于我该怎样将语法传递给患了自闭症的孩子们。
03:34
The solution came to me from
a very interesting perspective.
a very interesting perspective.
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解决方案来自于一个很有意思的观点。
03:36
I happened to chance upon a child with autism
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我碰巧遇到一个自闭症孩子
03:39
conversing with her mom,
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正在和他的妈妈交谈，
03:41
and this is what happened.
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然后发生了这样的事。
03:44
Completely out of the blue, very spontaneously,
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完全出乎意料的，很自然地，
03:46
the child got up and said, "Eat."
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那个孩子站起来并说道，"Eat"(吃)
03:48
Now what was interesting was
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非常有意思的是
03:50
the way in which the mom was trying to tease out
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这位妈妈试图梳理出
03:54
the meaning of what the child wanted to say
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孩子所以表达的意思
03:56
by talking to her in questions.
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通过提问来与她交谈。
03:59
So she asked, "Eat what? Do
you want to eat ice cream?
you want to eat ice cream?
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于是她问道，“吃什么？你想吃冰淇淋吗？
04:01
You want to eat? Somebody else wants to eat?
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你想吃？还是别人想吃？
04:03
You want to eat cream now? You
want to eat ice cream in the evening?"
want to eat ice cream in the evening?"
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你现在想吃奶油？还是你晚上想吃冰淇淋？“
04:07
And then it struck me that
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这件事让我突然想到
04:08
what the mother had done was something incredible.
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这位妈妈做的事情是不可思议的。
04:10
She had been able to get that child to communicate
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她已经能够让孩子和她交流想法
04:12
an idea to her without grammar.
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却不需要语法。
04:16
And it struck me that maybe this is what
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我突然想到也许这就是
04:19
I was looking for.
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我一直在寻找的。
04:20
Instead of arranging words in an order, in sequence,
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与其将文字 按顺序，按词组，按句子排列，
04:25
as a sentence, you arrange them
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不如将他们安放在
04:27
in this map, where they're all linked together
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这个地图中，它们都是联系在一起
04:31
not by placing them one after the other
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不是通过将他们一个接着一个地排放
04:33
but in questions, in question-answer pairs.
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而是按问题，按 [问题-答案] 成对排列。
04:36
And so if you do this, then what you're conveying
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如果你这样做，那么你所表达的
04:38
is not a sentence in English,
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就不是英文中的一个句子，
04:40
but what you're conveying is really a meaning,
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你所表达的是真实的"意义"，
04:43
the meaning of a sentence in English.
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英文句子的"意义"。
04:45
Now, meaning is really the underbelly,
in some sense, of language.
in some sense, of language.
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"意义"在某种程度上是语言的软肋。
04:48
It's what comes after thought but before language.
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它是一种在想法之后语言之前的东西。
04:52
And the idea was that this particular representation
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所以这个想法是，这种特别的表达方式
04:54
might convey meaning in its raw form.
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可能能够用原始形式传递"意义"。
04:57
So I was very excited by this, you know,
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我对此非常兴奋，你知道的，
04:59
hopping around all over the place,
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手舞足蹈地，
05:01
trying to figure out if I can convert
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试图确定我是否能够将
05:02
all possible sentences that I hear into this.
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所有我听到的句子转换成这样。
05:05
And I found that this is not enough.
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然后我发现这样是不够的。
05:07
Why is this not enough?
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为什么不够呢？
05:08
This is not enough because if you wanted to convey
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不够是因为如果你想要表达
05:10
something like negation,
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某些东西，比如否认，
05:12
you want to say, "I don't want soup,"
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你会想说，"I don't want soup"(我不想要汤)，
05:14
then you can't do that by asking a question.
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然而你不能通过提问来做到这个，
05:16
You do that by changing the word "want."
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你通过改变“want”（要）这个词来做到的。
05:18
Again, if you wanted to say,
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再者，如果你想要说，
05:20
"I wanted soup yesterday,"
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"I wanted soup yesterday"(我昨天想要汤)
05:22
you do that by converting
the word "want" into "wanted."
the word "want" into "wanted."
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你是通过将“want”转换成“wanted”做到的
05:25
It's a past tense.
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它是个过去时态。
05:26
So this is a flourish which I added
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所以这是一个重要的进度
05:28
to make the system complete.
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使我完善这系统。
05:30
This is a map of words joined together
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这是单词彼此联系在一起的一张图
05:32
as questions and answers,
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通过问题和答案的方式，
05:34
and with these filters applied on top of them
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通过顶上这些过滤器的应用
05:36
in order to modify them to represent
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来修改他们来表达
05:38
certain nuances.
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某种细微差别。
05:39
Let me show you this with a different example.
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让我通过一个不同的例子来展示给大家。
05:41
Let's take this sentence:
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就拿这个句子来说吧：
05:43
"I told the carpenter I could not pay him."
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"I told the carpenter I could not pay him."(我跟木匠说过了我不能付他钱)
05:45
It's a fairly complicated sentence.
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这是一个相当复杂的句子。
05:46
The way that this particular system works,
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这个特别的系统工作的方式就是，
05:48
you can start with any part of this sentence.
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你可以以句子的任意部分开始。
05:51
I'm going to start with the word "tell."
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我将从"tell"(说)这个词开始。
05:53
So this is the word "tell."
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这就是“说‘（tell）这个单词。
05:54
Now this happened in the past,
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这发生在过去，
05:56
so I'm going to make that "told."
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所以我将把它变成"说过"(told)。
05:58
Now, what I'm going to do is,
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现在，我将这样做，
06:00
I'm going to ask questions.
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我将提问，
06:01
So, who told? I told.
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那么，谁说(told)了？我说(told)了。
06:04
I told whom? I told the carpenter.
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我跟谁说(told)了？我跟木匠说(told)了。
06:06
Now we start with a different part of the sentence.
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现在让我们从句子的另一个部分开始。
06:07
We start with the word "pay,"
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我们从“付钱”（pay）这个词开始，
06:09
and we add the ability filter to it to make it "can pay."
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我们加入了能力过滤器，将它变成了“can pay”(能够付钱)。
06:14
Then we make it "can't pay,"
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然后我们将它变成了“can't pay”(不能付钱)
06:16
and we can make it "couldn't pay"
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而且我们将它变成了“couldn't pay”((过去)不能付钱)
06:18
by making it the past tense.
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通过将它变成过去时态。
06:19
So who couldn't pay? I couldn't pay.
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那么谁"couldn't pay"((过去)不能付钱)？我"couldn't pay"((过去)不能付钱)。
06:21
Couldn't pay whom? I couldn't pay the carpenter.
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不能付钱给谁？我不能付钱付给木匠。
06:24
And then you join these two together
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然后你讲这两个关联在一起
06:25
by asking this question:
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通过问这样的一个问题：
06:27
What did I tell the carpenter?
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我跟木匠说什么了？
06:29
I told the carpenter I could not pay him.
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我跟木匠说我不能付钱给他。
06:33
Now think about this. This is
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现在想想这个问题。这是
06:35
—(Applause)—
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—（掌声）—
06:38
this is a representation of this sentence
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这是对这个句子的一个描述
06:42
without language.
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不需要语言。
06:44
And there are two or three
interesting things about this.
interesting things about this.
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这里有两三个很有意思的现象。
06:46
First of all, I could have started anywhere.
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首先，我可以从任何地方开始。
06:50
I didn't have to start with the word "tell."
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我没有必要从"tell"（说）这个单词开始。
06:52
I could have started anywhere in the sentence,
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我本可以从句子的任何一个地方开始，
06:53
and I could have made this entire thing.
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也能弄出这整个东西。
06:55
The second thing is, if I wasn't an English speaker,
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第二点是，如果我不是一个说英语的人，
06:57
if I was speaking in some other language,
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如果我说某种其他的语言，
07:00
this map would actually hold true in any language.
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这个地图能够在任何语言中适用。
07:03
So long as the questions are standardized,
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只要问题能够被标准化，
07:05
the map is actually independent of language.
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这个地图事实上是独立于语言的。
07:09
So I call this FreeSpeech,
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我管这个叫 Free Speech（自由说话），
07:11
and I was playing with this for many, many months.
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我玩这个玩了好多好多个月。
07:14
I was trying out so many
different combinations of this.
different combinations of this.
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我试过关于这个的好多个不同的组合。
07:17
And then I noticed something very
interesting about FreeSpeech.
interesting about FreeSpeech.
176
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然后我注意到 Free Speech 有一些非常有趣的现象。
07:19
I was trying to convert language,
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我尝试过将语言进行转换，
07:22
convert sentences in English
into sentences in FreeSpeech,
into sentences in FreeSpeech,
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将英文句子转换成 Free Speech的句子，
07:25
and vice versa, and back and forth.
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然后反向转换，然后反复转换。
07:27
And I realized that this particular configuration,
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我意识到这种特别的结构，
07:29
this particular way of representing language,
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2026
这种特别的语言表示方式，
07:31
it allowed me to actually create very concise rules
182
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它竟然能让我创造非常简明的规则，
07:35
that go between FreeSpeech on one side
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这种规则能够在 Free Speech 和
07:38
and English on the other.
184
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英语之间转换。
07:39
So I could actually write this set of rules
185
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因此我事实上能够写下这些
07:42
that translates from this particular
representation into English.
representation into English.
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将特别表示法转换成英语的规则。
07:45
And so I developed this thing.
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于是我发明了这个东西。
07:47
I developed this thing called
the FreeSpeech Engine
the FreeSpeech Engine
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我发明了这个，叫做 Free Speech 引擎
07:49
which takes any FreeSpeech sentence as the input
189
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它将任何 Free Speech 句子作为输入
07:52
and gives out perfectly grammatical English text.
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然后输出语法完美的英语文本。
07:56
And by putting these two pieces together,
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通过将这两部分结合在一起，
07:57
the representation and the engine,
192
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表示部分和引擎部分，
07:59
I was able to create an app, a
technology for children with autism,
technology for children with autism,
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我得以创造一个 app， 一种专为自闭症儿童而生的技术，
08:03
that not only gives them words
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它不仅教他们文字
08:05
but also gives them grammar.
195
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它也教他们语法。
08:09
So I tried this out with kids with autism,
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我给患了自闭症的孩子试过这个，
08:12
and I found that there was an
incredible amount of identification.
incredible amount of identification.
197
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我获得了莫大的肯定。
08:17
They were able to create sentences in FreeSpeech
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2720
他们能够使用 Free Speech 来创造那些
08:19
which were much more complicated
but much more effective
but much more effective
199
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比起类似英文语句
08:22
than equivalent sentences in English,
200
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2899
更复杂却有效多了的语句，
08:25
and I started thinking about
201
493333
1682
然后我开始思考
08:27
why that might be the case.
202
495015
1969
为什么是这样的。
08:28
And I had an idea, and I want to
talk to you about this idea next.
talk to you about this idea next.
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我有一个想法，我后面想要和大家说说这个想法。
08:33
In about 1997, about 15 years back,
204
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3142
大概在1997年，大约15年前，
08:36
there were a group of scientists that were trying
205
504413
2011
有一群科学家他们试图
08:38
to understand how the brain processes language,
206
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2389
弄清楚大脑是如何处理语言的，
08:40
and they found something very interesting.
207
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1779
然后他们发现了一些有很意思的现象。
08:42
They found that when you learn a language
208
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1872
他们发现当你还说小孩时学习一门语言
08:44
as a child, as a two-year-old,
209
512464
2912
比如2岁大，
08:47
you learn it with a certain part of your brain,
210
515376
2366
你使用大脑的特定的部分来学习它，
08:49
and when you learn a language as an adult --
211
517742
1600
当你作为一名成年人来学习它的时候——
08:51
for example, if I wanted to
learn Japanese right now —
learn Japanese right now —
212
519342
3911
比如，我现在想学习日语——
08:55
a completely different part of my brain is used.
213
523253
2707
我的大脑的一个完全不同的部分被使用了。
08:57
Now I don't know why that's the case,
214
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1831
现在我不知道为什么是这样的，
08:59
but my guess is that that's because
215
527791
1991
但是我猜测那是因为
09:01
when you learn a language as an adult,
216
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2437
当你作为成年人学习语言的时候，
09:04
you almost invariably learn it
217
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1616
你几乎总是在
09:05
through your native language, or
through your first language.
through your first language.
218
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4266
通过你的母语或者通过你的第一语言在学习它。
09:10
So what's interesting about FreeSpeech
219
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3252
所以对于 Free Speech 而言有意思的是
09:13
is that when you create a sentence
220
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1802
当你创造一个句子
09:15
or when you create language,
221
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1695
或者当你创造语言时，
09:16
a child with autism creates
language with FreeSpeech,
language with FreeSpeech,
222
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3070
患了自闭症的孩子通过 Free Speech 创造语言，
09:19
they're not using this support language,
223
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1833
他们没有使用这种语言中介，
09:21
they're not using this bridge language.
224
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2211
他们没有使用桥梁语言。
09:23
They're directly constructing the sentence.
225
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他们直接在构造句子。
09:26
And so this gave me this idea.
226
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2193
这给了我这样一个想法。
09:28
Is it possible to use FreeSpeech
227
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2024
这样是可能的吗？不仅仅将 Free Speech
09:30
not for children with autism
228
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2510
给自闭症儿童使用
09:33
but to teach language to people without disabilities?
229
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6262
也给没有缺陷的人学习语言用？
09:39
And so I tried a number of experiments.
230
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1978
于是我尝试了一些实验。
09:41
The first thing I did was I built a jigsaw puzzle
231
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2948
首先我做的时建造一个拼图游戏
09:44
in which these questions and answers
232
572536
1970
它的问题和答案
09:46
are coded in the form of shapes,
233
574506
1835
以图形和颜色
09:48
in the form of colors,
234
576341
1138
的形式编码，
09:49
and you have people putting these together
235
577479
1849
然后你让人们把他们拼凑在一起
09:51
and trying to understand how this works.
236
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1773
然后试图理解这是怎么运作的。
09:53
And I built an app out of it, a game out of it,
237
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2376
我通过它制作了一个应用，是一个游戏
09:55
in which children can play with words
238
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2661
孩子们可以用文字游戏
09:58
and with a reinforcement,
239
586138
1704
然后巩固，
09:59
a sound reinforcement of visual structures,
240
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2585
用声音强化视觉结构，
10:02
they're able to learn language.
241
590427
2013
让他们学习语言。
10:04
And this, this has a lot of potential, a lot of promise,
242
592440
2736
然后这个，这个有很大的潜力，很多承诺，
10:07
and the government of India recently
243
595176
1975
而且印度政府最近
10:09
licensed this technology from us,
244
597151
1404
从我们这项技术授权，
10:10
and they're going to try it out
with millions of different children
with millions of different children
245
598555
2074
他们将在上百万不同的儿童身上使用
10:12
trying to teach them English.
246
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2605
尝试教他们英语。
10:15
And the dream, the hope, the vision, really,
247
603234
2614
而这个梦想，希望，远景，实际上，
10:17
is that when they learn English this way,
248
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3082
就是他们能够用这种方式学习英语，
10:20
they learn it with the same proficiency
249
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2643
他们想能够将它学得熟练得
10:23
as their mother tongue.
250
611573
3718
跟他们的母语一样。
10:27
All right, let's talk about something else.
251
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3816
好吧，让我们来谈点别的东西。
10:31
Let's talk about speech.
252
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1997
让我们谈谈语言。
10:33
This is speech.
253
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1271
这个是语言，
10:34
So speech is the primary mode of communication
254
622375
1962
语言是我们之间
10:36
delivered between all of us.
255
624337
1613
交流主要模式。
10:37
Now what's interesting about speech is that
256
625950
1855
关于语言有意思的是
10:39
speech is one-dimensional.
257
627805
1245
语言是一维的。
10:41
Why is it one-dimensional?
258
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1359
为什么是一维的呢？
10:42
It's one-dimensional because it's sound.
259
630409
1568
它是一维的因为它是声音。
10:43
It's also one-dimensional because
260
631977
1539
它是一维的也是因为
10:45
our mouths are built that way.
261
633516
1205
我们的嘴是那样构造的。
10:46
Our mouths are built to create
one-dimensional sound.
one-dimensional sound.
262
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3512
我们的嘴被构造得生产一维的声音。
10:50
But if you think about the brain,
263
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2866
但是如果你想想大脑，
10:53
the thoughts that we have in our heads
264
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1764
在我们脑子中的思法
10:54
are not one-dimensional.
265
642863
2102
不是一维的。
10:56
I mean, we have these rich,
266
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1459
我的意思是，我们拥有这些丰富的，
10:58
complicated, multi-dimensional ideas.
267
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3028
复杂的，多维的思想。
11:01
Now, it seems to me that language
268
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1690
现在，对我而言好像是这样的
11:03
is really the brain's invention
269
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2332
语言确实是大脑的发明
11:05
to convert this rich, multi-dimensional thought
270
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3096
将这种丰富的，多维的想法
11:08
on one hand
271
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1587
进行转换，一方面，
11:10
into speech on the other hand.
272
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1923
转换成语言，另一方面。
11:12
Now what's interesting is that
273
660080
1762
现在有趣的是
11:13
we do a lot of work in information nowadays,
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2568
我们如今在做大量的信息化工作，
11:16
and almost all of that is done
in the language domain.
in the language domain.
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3079
而且几乎所有的工作都是在语言领域的。
11:19
Take Google, for example.
276
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1939
比如Google，打个比方吧。
11:21
Google trawls all these
countless billions of websites,
countless billions of websites,
277
669428
2677
Google 网罗了数十亿的网站，
11:24
all of which are in English,
and when you want to use Google,
and when you want to use Google,
278
672105
2725
都是英文网站，当你想要使用 Google 时，
11:26
you go into Google search, and you type in English,
279
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2450
你进入 Google 搜索，然后你输入英文，
11:29
and it matches the English with the English.
280
677280
4163
然后它将英文与英文匹配。
11:33
What if we could do this in FreeSpeech instead?
281
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3583
如果我们能够用 Free Speech 来替代这件事会怎样呢？
11:37
I have a suspicion that if we did this,
282
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2301
我有一个猜想就是如果我们做到了这个，
11:39
we'd find that algorithms like searching,
283
687327
2068
我们就会发现类似于搜索，
11:41
like retrieval, all of these things,
284
689395
2325
类似于检索，所有这样的算法，
11:43
are much simpler and also more effective,
285
691720
3075
简单得多同时也有效得多了，
11:46
because they don't process
the data structure of speech.
the data structure of speech.
286
694795
4417
因为他们不是处理语言的信息结构，
11:51
Instead they're processing
the data structure of thought.
the data structure of thought.
287
699212
5976
而是他们处理想法的信息结构。
11:57
The data structure of thought.
288
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2808
想法的信息结构。
11:59
That's a provocative idea.
289
707996
2076
这是个令人振奋的主意。
12:02
But let's look at this in a little more detail.
290
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2142
但是让我们更仔细的想想这个问题。
12:04
So this is the FreeSpeech ecosystem.
291
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2366
这就是 Free Speech 生态体系。
12:06
We have the Free Speech
representation on one side,
representation on one side,
292
714580
2884
我们把 Free Speech 的表示法放置在一个站点上，
12:09
and we have the FreeSpeech
Engine, which generates English.
Engine, which generates English.
293
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2228
我们有 Free Speech 引擎， 它生成英文。
12:11
Now if you think about it,
294
719694
1725
现在大家思考一下这个，
12:13
FreeSpeech, I told you, is completely
language-independent.
language-independent.
295
721419
2544
Free Speech，我已经跟大家讲过，是完全语言独立的。
12:15
It doesn't have any specific information in it
296
723963
2087
他没有任何特定的信息在里面
12:18
which is about English.
297
726050
1228
关于英语的。
12:19
So everything that this system knows about English
298
727278
2800
于是这个系统所知的所有关于英语的
12:22
is actually encoded into the engine.
299
730078
4620
事实上都被编码写入了引擎。
12:26
That's a pretty interesting concept in itself.
300
734698
2237
它本身就是一个非常有趣的概念。
12:28
You've encoded an entire human language
301
736935
3604
你已经将整个人类的的语种编码
12:32
into a software program.
302
740539
2645
写入了一个软件程序中。
12:35
But if you look at what's inside the engine,
303
743184
2531
但是如果你看看引擎里面有什么，
12:37
it's actually not very complicated.
304
745715
2358
他实际上并不是很复杂。
12:40
It's not very complicated code.
305
748073
2105
不是非常复杂的代码。
12:42
And what's more interesting is the fact that
306
750178
2672
更有趣的情况是
12:44
the vast majority of the code in that engine
307
752850
2203
引擎里绝大多数的代码
12:47
is not really English-specific.
308
755053
2412
都不是英文特有的。
12:49
And that gives this interesting idea.
309
757465
1895
这给出的这样一个有趣的想法。
12:51
It might be very easy for us to actually
310
759360
2038
或许对我们来说
12:53
create these engines in many,
many different languages,
many different languages,
311
761398
3826
用许多不同的语言来创造这些引擎是很容易的，
12:57
in Hindi, in French, in German, in Swahili.
312
765224
6354
比如用印地语，法语，德语，斯瓦希里语。
13:03
And that gives another interesting idea.
313
771578
2799
这给了我们另一个有趣的想法。
13:06
For example, supposing I was a writer,
314
774377
2654
比如，假设我是一名作家，
13:09
say, for a newspaper or for a magazine.
315
777031
2122
比方说，为报社或者为杂志工作。
13:11
I could create content in one language, FreeSpeech,
316
779153
5011
我可以用一种语言创作内容，Free Speech，
13:16
and the person who's consuming that content,
317
784164
2056
看内容的人，
13:18
the person who's reading that particular information
318
786220
3061
阅览特定信息的人，
13:21
could choose any engine,
319
789281
2495
能够选择任意引擎，
13:23
and they could read it in their own mother tongue,
320
791776
2736
他们能够用他们的母语来阅读，
13:26
in their native language.
321
794512
3939
用他们的当地语言。
13:30
I mean, this is an incredibly attractive idea,
322
798451
2722
我的意思是，这是一个极其诱人的想法，
13:33
especially for India.
323
801173
1999
特别是对印度而言。
13:35
We have so many different languages.
324
803172
1690
我们有这么多不同的语言。
13:36
There's a song about India, and there's a description
325
804862
2142
有一首关于印度的歌，其中有一段描述
13:39
of the country as, it says,
326
807004
2344
是关于这个国家的，它是这样唱的，
13:41
(in Sanskrit).
327
809348
2360
（梵文）
13:43
That means "ever-smiling speaker
328
811708
2773
意思是说“永远微笑着的
13:46
of beautiful languages."
329
814481
4519
美好的语言的述说者”
13:51
Language is beautiful.
330
819000
1964
语言是美好的。
13:52
I think it's the most beautiful of human creations.
331
820964
2454
我认为它是人类发明中最美好的。
13:55
I think it's the loveliest thing
that our brains have invented.
that our brains have invented.
332
823418
3978
我认为他是我们的大脑创造的最可爱的东西。
13:59
It entertains, it educates, it enlightens,
333
827396
3584
它使人欢乐，教导众生，启发心灵
14:02
but what I like the most about language
334
830980
2044
但是关于语言我最喜欢的
14:05
is that it empowers.
335
833024
1500
是它带给人力量。
14:06
I want to leave you with this.
336
834524
1838
我想以一下内容作为结束。
14:08
This is a photograph of my collaborators,
337
836362
2385
这是一张我的同事的照片，
14:10
my earliest collaborators
338
838747
997
我最早的同事
14:11
when I started working on language
339
839744
1462
当我开始研究语言
14:13
and autism and various other things.
340
841206
1502
和自闭症和许多其他的东西的时候。
14:14
The girl's name is Pavna,
341
842708
1417
这个女孩的名字叫 Pavna，
14:16
and that's her mother, Kalpana.
342
844125
1902
这个是她的妈妈， Kalpana。
14:18
And Pavna's an entrepreneur,
343
846027
2138
Pavna 是一名创业者，
14:20
but her story is much more remarkable than mine,
344
848165
2371
但她的故事比我的更值得一提，
14:22
because Pavna is about 23.
345
850536
2400
因为 Pavna 大约只有23岁。
14:24
She has quadriplegic cerebral palsy,
346
852936
2552
她患有脑性四肢瘫痪，
14:27
so ever since she was born,
347
855488
1640
所以从她出生以来，
14:29
she could neither move nor talk.
348
857128
3600
她既不能行动也不能说话。
14:32
And everything that she's accomplished so far,
349
860728
2403
她目前的所有成就，
14:35
finishing school, going to college,
350
863131
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完成学业，进入大学，
14:37
starting a company,
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创立公司，
14:38
collaborating with me to develop Avaz,
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与我合作开发 Avaz，
14:40
all of these things she's done
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她所做的所有这些
14:42
with nothing more than moving her eyes.
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都是通过移动她的眼睛来完成的。
14:48
Daniel Webster said this:
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Daniel Webster 曾经这样说过：
14:51
He said, "If all of my possessions were taken
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他说，“如果我所拥有的一切都将被带走
14:53
from me with one exception,
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只能有一个例外，
14:56
I would choose to keep the power of communication,
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我将选择留下交流的力量，
14:59
for with it, I would regain all the rest."
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因为有了它，我将能够重建所有其他的东西。”
15:03
And that's why, of all of these incredible
applications of FreeSpeech,
applications of FreeSpeech,
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这就是为何，在 Free Speech 所有这些不可思议的应用中，
15:08
the one that's closest to my heart
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最接近我的心灵的那一个
15:11
still remains the ability for this
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仍旧是它赋予
15:13
to empower children with disabilities
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自闭症儿童
15:15
to be able to communicate,
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能够交流的力量，
15:17
the power of communication,
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交流的力量，
15:19
to get back all the rest.
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重建所有其他的东西。
15:21
Thank you.
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谢谢大家。
15:22
(Applause)
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（掌声）
15:24
Thank you. (Applause)
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谢谢。（掌声）
15:28
Thank you. Thank you. Thank you. (Applause)
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谢谢大家。谢谢。谢谢。（掌声）
15:33
Thank you. Thank you. Thank you. (Applause)
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谢谢大家。谢谢。谢谢。（掌声）
ABOUT THE SPEAKER
Ajit Narayanan - Visual grammar engine inventorAjit Narayanan is the inventor of Avaz, an affordable, tablet-based communication device for people who are speech-impaired.
Why you should listen
Ajit Narayanan is the founder and CEO of Invention Labs, and the inventor of Avaz AAC, the first assistive device aimed at an Indian market that helps people with speech disabilities -- such as cerebral palsy, autism, intellectual disability, aphasia and learning disabilities -- to communicate. Avaz is also available as an iPad app, aimed at children with autism. In 2010, Avaz won the National Award for Empowerment of People with Disabilities from the president of India, and in 2011, Narayanan was listed in MIT Technology Review 35 under 35.
Narayanan is a prolific inventor with more than 20 patent applications. He is an electrical engineer with degrees from IIT Madras. His research interests are embedded systems, signal processing and understanding how the brain perceives language and communication.
Ajit Narayanan | Speaker | TED.com