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Ajit Narayanan 談能用任何語言交流的文字遊戲

Ajit Narayanan: A word game to communicate in any language

 

Photo of three lions hunting on the Serengeti.

講者:Ajit Narayanan

2013年2月演講,2014年3月在TED2013上線

 

翻譯:洪曉慧

編輯:朱學恒

簡繁轉換:洪曉慧

後制:洪曉慧

字幕影片後制:謝旻均

 

影片請按此下載

MAC及手持裝置版本請按此下載

閱讀中文字幕純文字版本

 

關於這場演講

從事幫助語言障礙自閉症兒童相關工作時,Ajit Narayanan建構出一種以圖片思考語言的方法,將文字和想法以「地圖」連結。這個想法衍生出一個協助語言障礙者進行溝通的應用程式,其中蘊含一個遠大的構想-名為FreeSpeech的語言概念-擁有相當大的潛力。

 

關於Ajit Narayanan

Ajit Narayanan是Avaz的發明者,這是專為語言障礙者打造、價格實惠的平板電腦式通訊設備。

 

為什麼要聽他演講

Ajit Narayanan是Invention Labs創始人兼執行長,也是Avaz AAC(Avaz進階音訊編碼)發明者,這是第一部針對印度市場的輔助裝置,幫助語言障礙者進行溝通-如腦性麻痺、自閉症、智力障礙、失語症及學習障礙。Avaz也可作為針對自閉症兒童的iPad應用程式。2010年,Avaz榮獲印度總統頒發的「身障者賦權國家獎」,2011年,Narayanan被《MIT Technology Review》列為35位35歲以下創新者之一。

 

Narayanan是一位多產的發明家,擁有超過20項專利申請。他是電機工程師,擁有印度理工學院馬德拉斯分校學位。他的研究興趣包括嵌入式系統、訊號處理及瞭解大腦如何感知語言和進行溝通。

 

Ajit Narayanan的英語網上資料

avazapp.com/freespeech

@ajitq

 

[TED科技‧娛樂‧設計]

已有中譯字幕的TED影片目錄(繁體)(簡體)。請注意繁簡目錄是不一樣的。

 

Ajit Narayanan 談能用任何語言交流的文字遊戲

 

我從事與自閉症孩童有關的工作。具體來說,我研發幫助他們交流的技術。

 

目前,自閉症孩童面臨的許多問題有一個共同根源,那就是他們很難理解抽象概念、象徵符號,因此他們在語言方面遇上很多困難。

 

我稍微說明一下其中原因。你看見這是一碗湯的圖片,我們都能看見、我們都能理解。這是另外兩張湯的圖片,但你可以看見這些較為抽象,不是那麼具體。當化為語言時,你看見它變成一個單字。它的外觀,它看起來、聽起來或呈現的型態與最初那碗湯的圖片毫無關聯,對嗎?因此它本質上是完全抽象、對現實生活中某項事物完全隨機的呈現,這正是對自閉症孩童來說困難重重的地方。好,這就是為什麼大多數從事自閉症孩童相關工作的人-語言治療師、教育工作者,他們幫助自閉症孩童交流的方法並非藉由文字,而是藉由圖片。因此如果一位自閉症孩童想表達「我要湯」,那個孩子會拿起三張不同的圖片:「我」、「要」、「湯」,他們會將這些拼湊在一起,治療師或家長就能理解這是孩子想說的。這相當有效,過去3、40年,人們一直採用這種方法。事實上,幾年前我開發了一個iPad應用程式,正是以這種方式運作,名稱為Avaz。它的運作方法是:孩子選擇不同圖片,這些圖片排列成句子,然後被讀出來,因此基本上Avaz的作用是轉譯圖片。它是翻譯機,將圖片翻譯成語言。

 

好,這相當有用,數千名孩子正使用這個,你知道,遍及全世界。因此我開始思考它能做到及無法做到的部分。我發現一些令人感興趣的事:Avaz可幫助自閉症孩童學習單字,它無法提供的幫助是學習文字模式。我稍微詳細解釋一下。以這個句子為例:「我今晚要湯。」好,傳達意義的不僅是這些文字,還有文字排列的方式,文字的修飾及排列方式。這就是為什麼一個句子,例如:「I want soup tonight(我今晚要湯)」,不同於這句「Soup want I tonight(湯要我今晚)」。這句話毫無意義。因此其中隱藏另一個抽象概念,自閉症孩童很難理解,那就是你可修飾文字,可將它們排列成不同含義,表達不同的意思。好,這就是所謂的語法。語法相當重要,因為語法是語言的組成部分,它使用我們擁有的有限辭彙,使我們得以表達無限的資訊、無限的想法,這是使你將事物連結、以表達任何想法的方式。

 

因此開發Avaz後,我擔心了很長一段時間,關於如何讓自閉症孩童學習語法。解決方案來自一個十分有趣的觀點。我碰巧看見一位自閉症孩童與母親交談,情況如下:出乎意料地,那個孩子主動站起來說:「吃。」令人感興趣的是,那位母親藉由提問試著瞭解孩子想表達的意思。因此她問道:「吃什麼?你想吃冰淇淋嗎?你想吃?還是別人想吃?你現在想吃冰淇淋?還是晚上想吃?」這讓我意識到這位母親所做的是相當了不起的事,她能使孩子在不使用語法的情況下表達想法。我突然意識到,這或許就是我一直尋找的答案:並非將文字依序排列成句子,而是將它們安置在這張圖中。它們彼此相連,並非將它們依序排列,而是以「問題-答案」的組合排列。因此如果這麼做,你表達的並非英文句子,而是英文句子的真正意義。好,以某種程度來說,意義是語言的關鍵,它介於思想和語言之間。因此這個想法是,這種呈現方式或許能以最原始的形式表達意義。

 

因此這令我相當興奮。你知道,手舞足蹈,試著瞭解我是否能將所有聽過的句子轉換成這種形式。但我發現這並不足夠,為什麼不夠?因為如果你想表達某些想法,例如否定,你想表達:「I don't want soup(我不要湯)」,你無法藉由提問做到這一點,只能藉由改變「want(要)」這個字。同樣地,如果你想表達「I wanted soup yesterday(我昨天想要湯)」,需將「want」變成「wanted」,使用過去時態。因此這是我添加的功能,使這個系統更完善。這是藉由問題及答案使詞彙連接的地圖,藉由上方的篩檢應用進行修飾,使它們得以表達細微差異。讓我以不同例子展示這項功能。

 

以這句話為例:「I told the carpenter I could not pay him(我跟木匠說無法付錢給他)」,這是相當複雜的句子。這個系統運作的方法是,你可從句子任何部分開始。我從「tell(說)」這個字開始,因此這是「tell(說)」這個字。好,這發生在過去,因此我把它變成「told」,現在我要做的是提出問題。因此-誰說的?我說的。我跟誰說?我跟木匠說。現在我們從句子另一部分開始,我們從「pay(支付)」這個字開始。我們加入能力篩檢模式,將它變成「can pay(能支付)」,將它變成「can't pay(不能支付)」,也可將它變成「couldn't pay(過去不能支付)」,藉由將它變成過去時態。因此誰無法付錢?我無法付錢。無法付錢給誰?我無法付錢給木匠。然後將兩者相連,藉由提出這個問題:我跟木匠說什麼?我跟木匠說無法付錢給他。

 

好,想想這些,這是-(掌聲)這是句子的呈現,不需使用語言,其中有兩、三個令人感興趣的特性。首先,我可從任何地方開始。我不必從「tell(說)」這個字開始,我可從句子任何一處開始,產生完整架構。第二點,如果我並非使用英語的人,如果我使用某種其他語言,這張地圖適用於任何語言,只要問題能標準化。事實上,這張地圖獨立於語言,因此我稱之為FreeSpeech(自由語言)。我試用了好幾個月,我嘗試過許多不同組合。

 

我注意到FreeSpeech某些令人感興趣的特性。我試著進行語言轉換,將英文句子轉換成FreeSpeech句子,然後反向轉換,反覆嘗試。我意識到這種特別的結構,這種特別的語言表達方式,使我得以創造相當簡明的規則,作為使FreeSpeech和英語雙向轉換的橋樑,因此我確實能寫出將這種特殊表達方式轉換成英語的規則。因此我開發了這個東西,我開發了名為FreeSpeech引擎的東西。它能輸入任何FreeSpeech句子,產生語法完美的英文詞句。藉由將兩者結合,表達部分和引擎部分,我得以創造一個應用程式,專為自閉症孩童打造的技術。不僅教導他們文字,也教導他們語法。

 

因此我讓自閉症孩童試用,獲得莫大肯定。他們能藉由FreeSpeech創造詞句,比類似的英文詞句更加複雜、更具表達力。我開始思考為何會產生這種結果。我有一個想法,接下來我想和各位分享這個想法。大約在1997年,15年前左右,一群科學家試圖瞭解大腦如何處理語言,他們發現一些十分令人感興趣的現象。他們發現年幼時學習一門語言,例如2歲時,你藉由大腦特定部分學習。當你成年後學習語言-例如-假設我現在想學日語,使用的是大腦中完全不同的部位。好,我不知道為何如此,但我猜測的原因是,當你成年後學習語言,幾乎總是藉由母語或第一語言學習。因此FreeSpeech令人感興趣的是,當你創造句子或語言時,自閉症孩童藉由FreeSpeech創造語言,他們並未使用這種輔助語言,並未使用這種橋樑語言,他們直接建構句子。

 

因此這給了我一個想法:是否有這個可能,不僅將FreeSpeech用於自閉症孩童,也讓一般人藉此學習語言?因此我做了一些實驗,首先是創造一個拼圖遊戲,其中的問題和答案以形狀和顏色表示,讓人們將它們拼湊在一起,試著理解它如何運作。我藉此開發了一個應用程式,一個遊戲,孩子可藉由文字玩遊戲,使用音效、加強視覺結構,藉此學習語言。這具有很大的潛力及展望,我們最近將這項技術授權給印度政府,他們打算讓數百萬名孩童試用,藉此教導他們英語。這個夢想、這個希望、這個願景,事實上是,當他們藉由這種方式學習英語,他們能學習得和母語一樣熟練。

 

好,我們談點別的話題。我們談談語言。這是語言,因此語言是我們進行交流的主要模式。好,關於語言令人感興趣的是,它是一維的。為何是一維的?因為它是聲音。它是一維的原因也在於我們口部的構造正是如此,我們的口部被建構成發出一維聲音。但如果你想想大腦,我們腦海中的想法並非一維。我是指,我們擁有豐富、複雜、多維的思想。好,在我看來,語言確實是大腦的發明,將這種豐富、多維的想法轉換成語言。好,令人感興趣的是,目前我們進行大量資訊相關工作,幾乎所有都屬於語言領域。以Google為例,Google囊括了無數網站,全使用英語。當你想使用Google時,你進入Google搜尋,你輸入英語,它將英語與英語匹配。如果以FreeSpeech代替會如何?我有一個設想:如果我們這麼做,將發現例如搜尋、檢索等所有演算法將更加簡單、更加有效率,因為它們並非處理語言資訊結構,而是處理思想資訊結構。思想資訊結構,這是個刺激的想法。

 

但我們不妨仔細探討一下。因此這是FreeSpeech生態體系,其中包含FreeSpeech表達法及FreeSpeech引擎,它可生成英文。好,如果你思考一下FreeSpeech,我說過,完全獨立於語言,其中沒有任何關於英語的特定資訊,因此這個系統中所有關於英語的資訊事實上都被寫入引擎編碼中。這本身就是相當有趣的概念,你已將所有人類語言編入一個軟體程式中。但如果你觀察引擎內部,事實上並不是很複雜,這並非相當複雜的編碼。更令人感興趣的是,引擎裡絕大多數編碼都不是英文特有的,這衍生出一個有趣的想法:或許對我們來說,以多種不同語言創造這些引擎十分容易;以印地語、法語、德語、斯瓦希利語。這給了我們另一個有趣的想法。例如,假設我是一名作家,為報社或雜誌工作,我可用一種語言進行創作-FreeSpeech-觀看內容的人,閱讀這些資訊的人可選擇任何引擎。他們能用母語閱讀、用當地方言閱讀。我是指,這是相當誘人的想法,尤其對印度來說,我們有太多不同的語言。有一首關於印度的歌,其中有一段對這個國家的描述:(梵語)意思是說:「美妙語言永遠微笑的述說者。」

 

語言相當美妙,我認為它是人類發明中最美妙的事物,我認為它是大腦所創造最可愛的事物。它具有娛樂性、教育性、啟發性。但關於語言,我最喜愛的是它賦予人類的力量。

 

我想以這個故事作為結束。這是我合作夥伴的照片,我最早的合作夥伴,當我開始研究語言、自閉症及許多其他項目時。這位女孩名叫Pavna,這是她的母親Kalpana。Pavna是一位創業家,但她的故事比我的更精彩。因為Pavna大約23歲,罹患四肢痙孿型腦性麻痺,因此自出生以來,她無法行動也無法說話。她目前所有的成就:完成學業、就讀大學、創立公司、與我合作開發Avaz,她所做的一切都是藉由眼睛的移動完成。

 

Daniel Webster曾說:「如果我擁有的一切都被剝奪,只能留下一樣,我會選擇留下交流能力。因為藉由它,我能重建所有其他的一切。」這就是為何FreeSpeech所有不可思議的應用中,最深得我心的,仍是它使有所障礙的孩童得以交流的能力。交流的力量可重建所有其他的一切。

 

謝謝。(掌聲)。謝謝。(掌聲)。謝謝、謝謝、謝謝。(掌聲)。謝謝、謝謝。(掌聲)

 

以下為系統擷取之英文原文

About this Talk

While working with kids who have trouble speaking, Ajit Narayanan sketched out a way to think about language in pictures, to relate words and concepts in "maps." The idea now powers an app that helps nonverbal people communicate, and the big idea behind it, a language concept called FreeSpeech, has exciting potential.;

About the Speaker

Ajit Narayanan is the inventor of Avaz, an affordable, tablet-based communication device for people who are speech-impaired. Full bio.

Transcript

Now, many of the problems that children with autism face, they have a common source, and that source is that they find it difficult to understand abstraction, symbolism. And because of this, they have a lot of difficulty with language.

Let me tell you a little bit about why this is. You see that this is a picture of a bowl of soup. All of us can see it. All of us understand this. These are two other pictures of soup, but you can see that these are more abstract These are not quite as concrete. And when you get to language, you see that it becomes a word whose look, the way it looks and the way it sounds, has absolutely nothing to do with what it started with, or what it represents, which is the bowl of soup. So it's essentially a completely abstract, a completely arbitrary representation of something which is in the real world, and this is something that children with autism have an incredible amount of difficulty with. Now that's why most of the people that work with children with autism -- speech therapists, educators -- what they do is, they try to help children with autism communicate not with words, but with pictures. So if a child with autism wanted to say, "I want soup," that child would pick three different pictures, "I," "want," and "soup," and they would put these together, and then the therapist or the parent would understand that this is what the kid wants to say. And this has been incredibly effective; for the last 30, 40 years people have been doing this. In fact, a few years back, I developed an app for the iPad which does exactly this. It's called Avaz, and the way it works is that kids select different pictures. These pictures are sequenced together to form sentences, and these sentences are spoken out. So Avaz is essentially converting pictures, it's a translator, it converts pictures into speech.

Now, this was very effective. There are thousands of children using this, you know, all over the world, and I started thinking about what it does and what it doesn't do. And I realized something interesting: Avaz helps children with autism learn words. What it doesn't help them do is to learn word patterns. Let me explain this in a little more detail. Take this sentence: "I want soup tonight." Now it's not just the words here that convey the meaning. It's also the way in which these words are arranged, the way these words are modified and arranged. And that's why a sentence like "I want soup tonight" is different from a sentence like "Soup want I tonight," which is completely meaningless. So there is another hidden abstraction here which children with autism find a lot of difficulty coping with, and that's the fact that you can modify words and you can arrange them to have different meanings, to convey different ideas. Now, this is what we call grammar. And grammar is incredibly powerful, because grammar is this one component of language which takes this finite vocabulary that all of us have and allows us to convey an infinite amount of information, an infinite amount of ideas. It's the way in which you can put things together in order to convey anything you want to.

And so after I developed Avaz, I worried for a very long time about how I could give grammar to children with autism. The solution came to me from a very interesting perspective. I happened to chance upon a child with autism conversing with her mom, and this is what happened. Completely out of the blue, very spontaneously, the child got up and said, "Eat." Now what was interesting was the way in which the mom was trying to tease out the meaning of what the child wanted to say by talking to her in questions. So she asked, "Eat what? Do you want to eat ice cream? You want to eat? Somebody else wants to eat? You want to eat cream now? You want to eat ice cream in the evening?" And then it struck me that what the mother had done was something incredible. She had been able to get that child to communicate an idea to her without grammar. And it struck me that maybe this is what I was looking for. Instead of arranging words in an order, in sequence, as a sentence, you arrange them in this map, where they're all linked together not by placing them one after the other but in questions, in question-answer pairs. And so if you do this, then what you're conveying is not a sentence in English, but what you're conveying is really a meaning, the meaning of a sentence in English. Now, meaning is really the underbelly, in some sense, of language. It's what comes after thought but before language. And the idea was that this particular representation might convey meaning in its raw form.

So I was very excited by this, you know, hopping around all over the place, trying to figure out if I can convert all possible sentences that I hear into this. And I found that this is not enough. Why is this not enough? This is not enough because if you wanted to convey something like negation, you want to say, "I don't want soup," then you can't do that by asking a question. You do that by changing the word "want." Again, if you wanted to say, "I wanted soup yesterday," you do that by converting the word "want" into "wanted." It's a past tense. So this is a flourish which I added to make the system complete. This is a map of words joined together as questions and answers, and with these filters applied on top of them in order to modify them to represent certain nuances. Let me show you this with a different example.

Let's take this sentence: "I told the carpenter I could not pay him." It's a fairly complicated sentence. The way that this particular system works, you can start with any part of this sentence. I'm going to start with the word "tell." So this is the word "tell." Now this happened in the past, so I'm going to make that "told." Now, what I'm going to do is, I'm going to ask questions. So, who told? I told. I told whom? I told the carpenter. Now we start with a different part of the sentence. We start with the word "pay," and we add the ability filter to it to make it "can pay." Then we make it "can't pay," and we can make it "couldn't pay" by making it the past tense. So who couldn't pay? I couldn't pay. Couldn't pay whom? I couldn't pay the carpenter. And then you join these two together by asking this question: What did I tell the carpenter? I told the carpenter I could not pay him.

Now think about this. This is —(Applause)— this is a representation of this sentence without language. And there are two or three interesting things about this. First of all, I could have started anywhere. I didn't have to start with the word "tell." I could have started anywhere in the sentence, and I could have made this entire thing. The second thing is, if I wasn't an English speaker, if I was speaking in some other language, this map would actually hold true in any language. So long as the questions are standardized, the map is actually independent of language. So I call this FreeSpeech, and I was playing with this for many, many months. I was trying out so many different combinations of this.

And then I noticed something very interesting about FreeSpeech. I was trying to convert language, convert sentences in English into sentences in FreeSpeech, and vice versa, and back and forth. And I realized that this particular configuration, this particular way of representing language, it allowed me to actually create very concise rules that go between FreeSpeech on one side and English on the other. So I could actually write this set of rules that translates from this particular representation into English. And so I developed this thing. I developed this thing called the FreeSpeech Engine which takes any FreeSpeech sentence as the input and gives out perfectly grammatical English text. And by putting these two pieces together, the representation and the engine, I was able to create an app, a technology for children with autism, that not only gives them words but also gives them grammar.

So I tried this out with kids with autism, and I found that there was an incredible amount of identification. They were able to create sentences in FreeSpeech which were much more complicated but much more effective than equivalent sentences in English, and I started thinking about why that might be the case. And I had an idea, and I want to talk to you about this idea next. In about 1997, about 15 years back, there were a group of scientists that were trying to understand how the brain processes language, and they found something very interesting. They found that when you learn a language as a child, as a two-year-old, you learn it with a certain part of your brain, and when you learn a language as an adult -- for example, if I wanted to learn Japanese right now — a completely different part of my brain is used. Now I don't know why that's the case, but my guess is that that's because when you learn a language as an adult, you almost invariably learn it through your native language, or through your first language. So what's interesting about FreeSpeech is that when you create a sentence or when you create language, a child with autism creates language with FreeSpeech, they're not using this support language, they're not using this bridge language. They're directly constructing the sentence.

And so this gave me this idea. Is it possible to use FreeSpeech not for children with autism but to teach language to people without disabilities? And so I tried a number of experiments. The first thing I did was I built a jigsaw puzzle in which these questions and answers are coded in the form of shapes, in the form of colors, and you have people putting these together and trying to understand how this works. And I built an app out of it, a game out of it, in which children can play with words and with a reinforcement, a sound reinforcement of visual structures, they're able to learn language. And this, this has a lot of potential, a lot of promise, and the government of India recently licensed this technology from us, and they're going to try it out with millions of different children trying to teach them English. And the dream, the hope, the vision, really, is that when they learn English this way, they learn it with the same proficiency as their mother tongue.

All right, let's talk about something else. Let's talk about speech. This is speech. So speech is the primary mode of communication delivered between all of us. Now what's interesting about speech is that speech is one-dimensional. Why is it one-dimensional? It's one-dimensional because it's sound. It's also one-dimensional because our mouths are built that way. Our mouths are built to create one-dimensional sound. But if you think about the brain, the thoughts that we have in our heads are not one-dimensional. I mean, we have these rich, complicated, multi-dimensional ideas. Now, it seems to me that language is really the brain's invention to convert this rich, multi-dimensional thought on one hand into speech on the other hand. Now what's interesting is that we do a lot of work in information nowadays, and almost all of that is done in the language domain. Take Google, for example. Google trawls all these countless billions of websites, all of which are in English, and when you want to use Google, you go into Google search, and you type in English, and it matches the English with the English. What if we could do this in FreeSpeech instead? I have a suspicion that if we did this, we'd find that algorithms like searching, like retrieval, all of these things, are much simpler and also more effective, because they don't process the data structure of speech. Instead they're processing the data structure of thought. The data structure of thought. That's a provocative idea.

But let's look at this in a little more detail. So this is the FreeSpeech ecosystem. We have the Free Speech representation on one side, and we have the FreeSpeech Engine, which generates English. Now if you think about it, FreeSpeech, I told you, is completely language-independent. It doesn't have any specific information in it which is about English. So everything that this system knows about English is actually encoded into the engine. That's a pretty interesting concept in itself. You've encoded an entire human language into a software program. But if you look at what's inside the engine, it's actually not very complicated. It's not very complicated code. And what's more interesting is the fact that the vast majority of the code in that engine is not really English-specific. And that gives this interesting idea. It might be very easy for us to actually create these engines in many, many different languages, in Hindi, in French, in German, in Swahili. And that gives another interesting idea. For example, supposing I was a writer, say, for a newspaper or for a magazine. I could create content in one language, FreeSpeech, and the person who's consuming that content, the person who's reading that particular information could choose any engine, and they could read it in their own mother tongue, in their native language. I mean, this is an incredibly attractive idea, especially for India. We have so many different languages. There's a song about India, and there's a description of the country as, it says, (in Sanskrit). That means "ever-smiling speaker of beautiful languages."

Language is beautiful. I think it's the most beautiful of human creations. I think it's the loveliest thing that our brains have invented. It entertains, it educates, it enlightens, but what I like the most about language is that it empowers.

I want to leave you with this. This is a photograph of my collaborators, my earliest collaborators when I started working on language and autism and various other things. The girl's name is Pavna, and that's her mother, Kalpana. And Pavna's an entrepreneur, but her story is much more remarkable than mine, because Pavna is about 23. She has quadriplegic cerebral palsy, so ever since she was born, she could neither move nor talk. And everything that she's accomplished so far, finishing school, going to college, starting a company, collaborating with me to develop Avaz, all of these things she's done with nothing more than moving her eyes.

Daniel Webster said this: He said, "If all of my possessions were taken from me with one exception, I would choose to keep the power of communication, for with it, I would regain all the rest." And that's why, of all of these incredible applications of FreeSpeech, the one that's closest to my heart still remains the ability for this to empower children with disabilities to be able to communicate, the power of communication, to get back all the rest.

 

 


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