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譯者: Marssi Draw
審譯者: Willy Feng
00:12
I work with children with autism.
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我服務有自閉症的孩子。
00:15
Specifically, I make technologies
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更確切來說,我發明科技
00:17
to help them communicate.
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幫助他們溝通。
00:19
Now, many of the problems that children
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許多自閉症孩童面臨的問題
00:21
with autism face, they have a common source,
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出自於同樣的因素,
00:24
and that source is that they find it difficult
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那就是他們很難
00:26
to understand abstraction, symbolism.
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了解抽象概念與象徵性的符號。
00:32
And because of this, they have
a lot of difficulty with language.
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因此,他們在面對語言時
會有很大的困難。
00:36
Let me tell you a little bit about why this is.
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讓我告訴你一些原因。
00:39
You see that this is a picture of a bowl of soup.
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你可以看到這張圖片是一碗湯。
00:43
All of us can see it. All of us understand this.
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我們每個人都看得見,也都了解這是什麼。
00:46
These are two other pictures of soup,
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這是另外兩張湯的圖片,
00:48
but you can see that these are more abstract
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但是你會發現它們比較抽象,
00:50
These are not quite as concrete.
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不太具體。
00:52
And when you get to language,
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當你使用語言時,
00:54
you see that it becomes a word
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會發現那個字詞
00:56
whose look, the way it looks and the way it sounds,
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看起來、聽起來
00:59
has absolutely nothing to do
with what it started with,
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和它以什麼開頭
01:02
or what it represents, which is the bowl of soup.
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或是和它代表的意義「那碗湯」完全無關。
01:05
So it's essentially a completely abstract,
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因此,基本上那是一個完全抽象、
01:08
a completely arbitrary representation of something
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存在真實世界中某種事物的
01:10
which is in the real world,
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一種任意的表述,
01:12
and this is something that children with autism
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自閉症的孩子在這方面
01:13
have an incredible amount of difficulty with.
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有很大的困難。
01:17
Now that's why most of the people
that work with children with autism --
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那就是為什麼許多
協助自閉症孩童的人們
01:19
speech therapists, educators --
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——語言治療師、教育人士——
01:21
what they do is, they try to help children with autism
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他們協助自閉症孩童
01:24
communicate not with words, but with pictures.
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不是用文字溝通,而是用圖片溝通。
01:27
So if a child with autism wanted to say,
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因此如果有個自閉症孩童想說:「我想喝湯。」
01:29
"I want soup," that child would pick
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這孩子會拿起
01:31
three different pictures, "I," "want," and "soup,"
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三張不同的圖片「我」、「想喝」、「湯」,
01:34
and they would put these together,
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然後把圖排在一起,
01:35
and then the therapist or the parent would
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那麼治療師或家長就能理解
01:37
understand that this is what the kid wants to say.
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這是孩子想說的話。
01:39
And this has been incredibly effective;
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三四十年來
01:41
for the last 30, 40 years
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這方法一直都很有效,
01:43
people have been doing this.
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大家都這麼做。
01:45
In fact, a few years back,
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事實上,幾年前
01:46
I developed an app for the iPad
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我開發了一個 iPad 的應用程式,
01:49
which does exactly this. It's called Avaz,
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名為「阿維思」(Avaz),就是採用此法。
01:51
and the way it works is that kids select
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操作方式是讓孩子選擇
01:53
different pictures.
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不同的圖片,
01:55
These pictures are sequenced
together to form sentences,
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將圖片排列成句子,
01:57
and these sentences are spoken out.
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然後這些句子會被唸出。
01:59
So Avaz is essentially converting pictures,
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因此基本上「阿維思」會轉換圖片,
02:02
it's a translator, it converts pictures into speech.
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它是翻譯機,能將圖片轉換成言語。
02:06
Now, this was very effective.
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這很有用。
02:07
There are thousands of children using this,
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有成千上萬的孩子使用它,
02:09
you know, all over the world,
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遍及全世界,
02:10
and I started thinking about
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於是我開始思考
02:12
what it does and what it doesn't do.
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它做了什麼,又漏了什麼。
02:15
And I realized something interesting:
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我發現某件很有趣的事:
02:17
Avaz helps children with autism learn words.
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「阿維思」協助有自閉症的孩子學習文字。
02:21
What it doesn't help them do is to learn
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但沒有教他們
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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讓我說明一些細節。
02:29
Take this sentence: "I want soup tonight."
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以此句為例:「我今晚想喝湯。」
02:32
Now it's not just the words
here that convey the meaning.
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這不只是文字傳達了意義,
02:36
It's also the way in which these words are arranged,
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這些文字排列的方式、
02:39
the way these words are modified and arranged.
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這些文字修飾與排列的方式也有意義。
02:41
And that's why a sentence like "I want soup tonight"
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那就是為什麼像是「我今晚想喝湯」這句話
02:44
is different from a sentence like
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會完全不同於
02:46
"Soup want I tonight," which
is completely meaningless.
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「湯想喝我今晚」這樣無意義的句子。
02:49
So there is another hidden abstraction here
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這裡有另一種隱藏的抽象概念,
02:52
which children with autism find
a lot of difficulty coping with,
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讓自閉症孩童難以處理,
02:55
and that's the fact that you can modify words
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那就是你能透過修飾文字、
02:58
and you can arrange them to have
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排列文字,
03:00
different meanings, to convey different ideas.
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讓它有不同的意義,傳達不同的想法。
03:03
Now, this is what we call grammar.
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我們稱之為文法。
03:07
And grammar is incredibly powerful,
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而文法的力量十分強大,
03:09
because grammar is this one component of language
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因為文法是語言的其中一項要素,
03:12
which takes this finite vocabulary that all of us have
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讓我們使用所擁有的有限字彙
03:15
and allows us to convey an
infinite amount of information,
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傳達無限種資訊、
03:20
an infinite amount of ideas.
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無限種想法。
03:22
It's the way in which you can put things together
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這種方式能讓你把東西組合在一起
03:24
in order to convey anything you want to.
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來傳達所有你想表達的事。
03:26
And so after I developed Avaz,
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因此在我開發「阿維思」之後,
03:28
I worried for a very long time
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有件事讓我擔心很久,
03:30
about how I could give grammar
to children with autism.
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那就是我要怎麼教自閉症孩童文法。
03:34
The solution came to me from
a very interesting perspective.
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解決方式來自一種非常有趣的觀點。
03:36
I happened to chance upon a child with autism
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我巧遇自閉症的孩童
03:39
conversing with her mom,
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和她的母親對話,
03:41
and this is what happened.
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事情就這樣發生了。
03:44
Completely out of the blue, very spontaneously,
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事發非常突然、不期而遇,
03:46
the child got up and said, "Eat."
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那孩子站起來說:「吃。」
03:48
Now what was interesting was
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有趣的是
03:50
the way in which the mom was trying to tease out
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那位媽媽誘導小孩的方式,
03:54
the meaning of what the child wanted to say
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她讓小孩透過回答她的問題
03:56
by talking to her in questions.
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表達出想說的話。
03:59
So she asked, "Eat what? Do
you want to eat ice cream?
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因此她問:「吃什麼?」
「你想吃冰淇淋?」
04:01
You want to eat? Somebody else wants to eat?
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「你想吃?」
「其他人想吃?」
04:03
You want to eat cream now? You
want to eat ice cream in the evening?"
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「你想現在吃冰淇淋?」
「你想晚上吃冰淇淋?」
04:07
And then it struck me that
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我突然意識到
04:08
what the mother had done was something incredible.
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那位母親做了一件非常棒的事。
04:10
She had been able to get that child to communicate
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她已經能讓那個孩子
04:12
an idea to her without grammar.
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不用文法就能傳達想法。
04:16
And it struck me that maybe this is what
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我突然想到也許這就是
04:19
I was looking for.
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我在找的方式。
04:20
Instead of arranging words in an order, in sequence,
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與其透過按照規則、順序
將文字排列成句子,
04:25
as a sentence, you arrange them
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不如將文字排列在這張圖中,
04:27
in this map, where they're all linked together
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文字連結在一起的方式
04:31
not by placing them one after the other
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不是透過將它們一個接一個排列,
04:33
but in questions, in question-answer pairs.
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而是透過問題,多組問答題。
04:36
And so if you do this, then what you're conveying
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因此如果你這麼做,那你傳達的
04:38
is not a sentence in English,
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不是一個英文句子,
04:40
but what you're conveying is really a meaning,
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你傳達的是一個意義,
04:43
the meaning of a sentence in English.
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一個英文句子的意義。
04:45
Now, meaning is really the underbelly,
in some sense, of language.
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從某個層面來說,
意義在語言中屬於較深層的部分。
04:48
It's what comes after thought but before language.
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意義出現在想法之後,但是在語言之前。
04:52
And the idea was that this particular representation
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而此想法是這種特殊的表述
04:54
might convey meaning in its raw form.
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可能是用它的根本樣貌來傳達意義。
04:57
So I was very excited by this, you know,
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這件事讓我很興奮,
04:59
hopping around all over the place,
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開心得手舞足蹈,
05:01
trying to figure out if I can convert
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試著確認我是否能
05:02
all possible sentences that I hear into this.
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將所有聽見的詞句轉換成這樣。
05:05
And I found that this is not enough.
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我發現這還不夠。
05:07
Why is this not enough?
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為什麼不夠呢?
05:08
This is not enough because if you wanted to convey
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不夠是因為如果你想要傳達
05:10
something like negation,
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否定的句子,
05:12
you want to say, "I don't want soup,"
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比如說:「我不想喝湯。」
05:14
then you can't do that by asking a question.
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那麼你就不能用問句完成。
05:16
You do that by changing the word "want."
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你會改變「想」這個字。
05:18
Again, if you wanted to say,
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同樣地,如果你想說:
05:20
"I wanted soup yesterday,"
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「我昨天本來 想喝湯。」
05:22
you do that by converting
the word "want" into "wanted."
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你把「想」轉換成「本來想」。
05:25
It's a past tense.
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那是過去式。
05:26
So this is a flourish which I added
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因此我加了這個功能
05:28
to make the system complete.
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讓系統更完善。
05:30
This is a map of words joined together
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這是許多單字的連結圖,
05:32
as questions and answers,
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以問句和答案組合而成,
05:34
and with these filters applied on top of them
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有了這些篩選功能在上面,
05:36
in order to modify them to represent
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就能做修改,呈現出
05:38
certain nuances.
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較細微的差異。
05:39
Let me show you this with a different example.
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讓我舉個不同的例子來說明。
05:41
Let's take this sentence:
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以這個句子來說:
05:43
"I told the carpenter I could not pay him."
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「我告訴了木工我不能付錢。」
05:45
It's a fairly complicated sentence.
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這是個蠻複雜的句子。
05:46
The way that this particular system works,
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這個特殊系統運作的方式是
05:48
you can start with any part of this sentence.
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你可以從句子的任何一處開始。
05:51
I'm going to start with the word "tell."
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我用「告訴」開頭來做說明。
05:53
So this is the word "tell."
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這個字是「告訴」,
05:54
Now this happened in the past,
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但這是以前發生的事,
05:56
so I'm going to make that "told."
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所以我要說「告訴了」。
05:58
Now, what I'm going to do is,
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現在我想做的是,
06:00
I'm going to ask questions.
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我開始問問題。
06:01
So, who told? I told.
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是誰「告訴」?
是我。
06:04
I told whom? I told the carpenter.
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我告訴了誰?
我告訴了木工。
06:06
Now we start with a different part of the sentence.
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現在我們從句子的另一處開始,
06:07
We start with the word "pay,"
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以「付錢」開始,
06:09
and we add the ability filter to it to make it "can pay."
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我們加上使役動詞,讓它變成「能付錢」,
06:14
Then we make it "can't pay,"
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接著我們就能改成「不能付錢」,
06:16
and we can make it "couldn't pay"
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接著就能更改時態,
06:18
by making it the past tense.
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將它改為過去式。
06:19
So who couldn't pay? I couldn't pay.
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那是誰不能付錢?
我不能付錢。
06:21
Couldn't pay whom? I couldn't pay the carpenter.
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不能付錢給誰?
我不能付錢給木工。
06:24
And then you join these two together
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接著你透過問這個問題
06:25
by asking this question:
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把這兩個部分連在一起:
06:27
What did I tell the carpenter?
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我告訴了木工什麼?
06:29
I told the carpenter I could not pay him.
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我告訴了木工我不能付錢。
06:33
Now think about this. This is
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想想看這個問題,
06:35
—(Applause)—
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(掌聲)
06:38
this is a representation of this sentence
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這是這個句子要表達的內容,
06:42
without language.
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沒有語言。
06:44
And there are two or three
interesting things about this.
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這裡有兩到三件有趣的事。
06:46
First of all, I could have started anywhere.
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首先,我能從任何一個單字開始,
06:50
I didn't have to start with the word "tell."
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我不一定要從「告訴」開始。
06:52
I could have started anywhere in the sentence,
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我能從句子的任何一部分開始,
06:53
and I could have made this entire thing.
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還是能完成整件事。
06:55
The second thing is, if I wasn't an English speaker,
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第二點是,如果我不是說英語的人,
06:57
if I was speaking in some other language,
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如果我說的是別的語言,
07:00
this map would actually hold true in any language.
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這個地圖真的在任何語言都管用。
07:03
So long as the questions are standardized,
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只要這個問題符合標準,
07:05
the map is actually independent of language.
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這個地圖就能獨立於語言使用。
07:09
So I call this FreeSpeech,
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因此我稱它為「輕鬆講」 (FreeSpeech),
07:11
and I was playing with this for many, many months.
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我已經玩了好幾個月,
07:14
I was trying out so many
different combinations of this.
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並試著使用許多不同的組合。
07:17
And then I noticed something very
interesting about FreeSpeech.
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後來,我注意到「輕鬆講」有個有趣的部分。
07:19
I was trying to convert language,
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我試著轉換語言,
07:22
convert sentences in English
into sentences in FreeSpeech,
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轉換英語句子和「輕鬆講」的句子,
07:25
and vice versa, and back and forth.
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來回反覆不斷嘗試。
07:27
And I realized that this particular configuration,
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我理解這種特殊的結構,
07:29
this particular way of representing language,
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這種表現語言的特殊方式
07:31
it allowed me to actually create very concise rules
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讓我能夠真正地建立很簡要的規則,
07:35
that go between FreeSpeech on one side
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在「輕鬆講」
07:38
and English on the other.
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以及英語之間的規則。
07:39
So I could actually write this set of rules
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我確實能寫下這組規則,
07:42
that translates from this particular
representation into English.
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讓這個特殊的表述轉換成英語。
07:45
And so I developed this thing.
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因此我發明了這項產品,
07:47
I developed this thing called
the FreeSpeech Engine
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稱為「輕鬆講引擎」,
07:49
which takes any FreeSpeech sentence as the input
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能把任何「輕鬆講」的句子輸入,
07:52
and gives out perfectly grammatical English text.
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然後產出有完美文法的英語。
07:56
And by putting these two pieces together,
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透過組合
07:57
the representation and the engine,
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表述與引擎,
07:59
I was able to create an app, a
technology for children with autism,
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我就能建立一個應用程式,
一個供自閉症孩童用的科技,
08:03
that not only gives them words
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不只是提供他們文字,
08:05
but also gives them grammar.
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也提供他們文法。
08:09
So I tried this out with kids with autism,
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我在自閉症孩童身上測試,
08:12
and I found that there was an
incredible amount of identification.
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發現了很驚人的成效。
08:17
They were able to create sentences in FreeSpeech
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他們用「輕鬆講」建立的句子
08:19
which were much more complicated
but much more effective
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複雜程度和效用都遠高於
08:22
than equivalent sentences in English,
200
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用英語講同一句話,
08:25
and I started thinking about
201
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我開始思考
08:27
why that might be the case.
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為什麼會成功。
08:28
And I had an idea, and I want to
talk to you about this idea next.
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因此,接下來我想與大家分享一個想法。
08:33
In about 1997, about 15 years back,
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大約在 1997 年時,大約 15 年前,
08:36
there were a group of scientists that were trying
205
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2011
有一群科學家嘗試
08:38
to understand how the brain processes language,
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2389
理解大腦處理語言的方式,
08:40
and they found something very interesting.
207
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1779
他們發現一件很有趣的事情。
08:42
They found that when you learn a language
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1872
就是當你學習一種語言,
08:44
as a child, as a two-year-old,
209
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2912
身為一個兩歲小孩,
08:47
you learn it with a certain part of your brain,
210
527376
2366
你用大腦的特定部位在學習;
08:49
and when you learn a language as an adult --
211
529742
1600
而當你身為一名成人
08:51
for example, if I wanted to
learn Japanese right now —
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──舉例來說,如果我現在想學日語──
08:55
a completely different part of my brain is used.
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2707
就會運用完全不同部位的大腦。
08:57
Now I don't know why that's the case,
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1831
我不了解為什麼會這樣,
08:59
but my guess is that that's because
215
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1991
但我猜是因為
09:01
when you learn a language as an adult,
216
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2437
成年時學習語言
09:04
you almost invariably learn it
217
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1616
幾乎無可避免會
09:05
through your native language, or
through your first language.
218
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4266
透過你的母語、習慣語言來學習。
09:10
So what's interesting about FreeSpeech
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3252
「輕鬆講」有趣的是
09:13
is that when you create a sentence
220
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1802
當你建立一個句子,
09:15
or when you create language,
221
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1695
或是建立一種語言,
09:16
a child with autism creates
language with FreeSpeech,
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自閉症孩童用「輕鬆講」建立語言,
09:19
they're not using this support language,
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他們不是用它來支援語言,
09:21
they're not using this bridge language.
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2211
他們不是用它來連結語言,
09:23
They're directly constructing the sentence.
225
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他們是直接建立句子。
09:26
And so this gave me this idea.
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這讓我有個想法。
09:28
Is it possible to use FreeSpeech
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2024
有可能讓「輕鬆講」
09:30
not for children with autism
228
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教自閉症孩童語言之外,
09:33
but to teach language to people without disabilities?
229
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也教非身障的孩童嗎?
09:39
And so I tried a number of experiments.
230
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1978
因此我嘗試許多實驗。
09:41
The first thing I did was I built a jigsaw puzzle
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2948
首先我設計了一個拼圖,
09:44
in which these questions and answers
232
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1970
這些問題和答案
09:46
are coded in the form of shapes,
233
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1835
都編碼成各種形狀,
09:48
in the form of colors,
234
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1138
各種顏色,
09:49
and you have people putting these together
235
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1849
操作人把這些放在一起,
09:51
and trying to understand how this works.
236
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1773
試著了解這是如何運作。
09:53
And I built an app out of it, a game out of it,
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我設計了一個應用程式,以此為基礎的遊戲,
09:55
in which children can play with words
238
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孩童可以玩文字遊戲,
09:58
and with a reinforcement,
239
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1704
並且有強化的功能,
09:59
a sound reinforcement of visual structures,
240
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以聽覺強化視覺,
10:02
they're able to learn language.
241
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2013
他們就能學習語言。
10:04
And this, this has a lot of potential, a lot of promise,
242
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2736
這有很大的潛力和前景,
10:07
and the government of India recently
243
607176
1975
而最近印度政府
10:09
licensed this technology from us,
244
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1404
向我們取得這項科技的授權,
10:10
and they're going to try it out
with millions of different children
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他們打算讓上百萬名孩童嘗試,
10:12
trying to teach them English.
246
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2605
試著教他們英語。
10:15
And the dream, the hope, the vision, really,
247
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2614
而這個夢想、希望、願景
10:17
is that when they learn English this way,
248
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3082
即是當他們以此學習英語,
10:20
they learn it with the same proficiency
249
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他們能夠表達流利,
10:23
as their mother tongue.
250
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就像母語一樣。
10:27
All right, let's talk about something else.
251
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接下來,我們來討論另一點。
10:31
Let's talk about speech.
252
631107
1997
談談說話。
10:33
This is speech.
253
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1271
這是說話。
10:34
So speech is the primary mode of communication
254
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1962
因此說話是溝通的基礎,
10:36
delivered between all of us.
255
636337
1613
在我們之間傳遞訊息。
10:37
Now what's interesting about speech is that
256
637950
1855
關於說話,有趣的是
10:39
speech is one-dimensional.
257
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1245
說話是單面的。
10:41
Why is it one-dimensional?
258
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1359
為什麼是單面的?
10:42
It's one-dimensional because it's sound.
259
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1568
因為說話是聲音,所以它是單面的。
10:43
It's also one-dimensional because
260
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1539
也因為
10:45
our mouths are built that way.
261
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1205
那是嘴巴的功能。
10:46
Our mouths are built to create
one-dimensional sound.
262
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3512
嘴巴的功能即是創造單面的聲音。
10:50
But if you think about the brain,
263
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但是如果你想想大腦,
10:53
the thoughts that we have in our heads
264
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1764
在我們頭腦裡的思想
10:54
are not one-dimensional.
265
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2102
並非一面向的。
10:56
I mean, we have these rich,
266
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我的意思是,我們有這些豐富、
10:58
complicated, multi-dimensional ideas.
267
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複雜和多面向的想法。
11:01
Now, it seems to me that language
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對我來說,語言
11:03
is really the brain's invention
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2332
就是大腦的發明,
11:05
to convert this rich, multi-dimensional thought
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3096
一方面轉換這豐富、
11:08
on one hand
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多面向的思想,
11:10
into speech on the other hand.
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另一方面轉換成話語。
11:12
Now what's interesting is that
273
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有趣的是
11:13
we do a lot of work in information nowadays,
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現在我們以資訊做許多事,
11:16
and almost all of that is done
in the language domain.
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3079
幾乎所有的事情都是在語言的領域中完成。
11:19
Take Google, for example.
276
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以 Google 為例,
11:21
Google trawls all these
countless billions of websites,
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2677
Google 網羅千百萬個網站,
11:24
all of which are in English,
and when you want to use Google,
278
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2725
全都是英語網站,
而當你想要用 Google,
11:26
you go into Google search, and you type in English,
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2450
進入 Google 搜尋功能列,輸入英語,
11:29
and it matches the English with the English.
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會出現符合你要的英語。
11:33
What if we could do this in FreeSpeech instead?
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有沒有可能我們改用「輕鬆講」這樣做呢?
11:37
I have a suspicion that if we did this,
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我推測如果我們這麼做,
11:39
we'd find that algorithms like searching,
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2068
我們會發現一些規則系統,像是搜尋、
11:41
like retrieval, all of these things,
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2325
像是擷取,所有的這些功能
11:43
are much simpler and also more effective,
285
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3075
都更簡單也更有效,
11:46
because they don't process
the data structure of speech.
286
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4417
因為他們不是處理說話的資料結構。
11:51
Instead they're processing
the data structure of thought.
287
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5976
相反地,他們處理思想的資料結構。
11:57
The data structure of thought.
288
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2808
思想的資料結構。
11:59
That's a provocative idea.
289
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那是個令人興奮的概念。
12:02
But let's look at this in a little more detail.
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讓我們多深入看一點細節。
12:04
So this is the FreeSpeech ecosystem.
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2366
這是「輕鬆講」的生態系統。
12:06
We have the Free Speech
representation on one side,
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2884
我們一邊有「輕鬆講」的畫面,
12:09
and we have the FreeSpeech
Engine, which generates English.
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2228
另一邊也有「輕鬆講」的引擎產生英語。
12:11
Now if you think about it,
294
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1725
請想像
12:13
FreeSpeech, I told you, is completely
language-independent.
295
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2544
「輕鬆講」是完全獨立的語言。
12:15
It doesn't have any specific information in it
296
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2087
裡面沒有任何關於英語的
12:18
which is about English.
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1228
特定資訊。
12:19
So everything that this system knows about English
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2800
因此對這個系統來說,
英語都已在引擎中編碼。
12:22
is actually encoded into the engine.
299
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4620
12:26
That's a pretty interesting concept in itself.
300
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2237
這之中有個很有趣的概念。
12:28
You've encoded an entire human language
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3604
你已經將所有的人類語言編碼入
12:32
into a software program.
302
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2645
一套軟體中。
12:35
But if you look at what's inside the engine,
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但是如果你看這個引擎的內部,
12:37
it's actually not very complicated.
304
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2358
會發現其實不複雜,
12:40
It's not very complicated code.
305
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2105
不是很複雜的編碼。
12:42
And what's more interesting is the fact that
306
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2672
更有趣的是,
12:44
the vast majority of the code in that engine
307
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2203
在那個引擎中大多數的編碼
12:47
is not really English-specific.
308
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2412
其實都不是只針對英語。
12:49
And that gives this interesting idea.
309
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1895
因此有了這個有趣的想法,
12:51
It might be very easy for us to actually
310
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2038
我們也許可以因此輕易地
12:53
create these engines in many,
many different languages,
311
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3826
建立很多很多不同語言的引擎,
12:57
in Hindi, in French, in German, in Swahili.
312
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6354
印度語、法語、德語、斯瓦希里語。
(註:斯瓦希里語是非洲使用人數最多的語言之一)
13:03
And that gives another interesting idea.
313
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2799
這引起了另一個有趣的想法。
13:06
For example, supposing I was a writer,
314
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2654
舉例來說,假設我是作家,
13:09
say, for a newspaper or for a magazine.
315
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2122
在報社或雜誌社工作。
13:11
I could create content in one language, FreeSpeech,
316
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5011
我的文章可以用一種語言「輕鬆講」來寫,
13:16
and the person who's consuming that content,
317
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2056
然後有個人買了那則報導,
13:18
the person who's reading that particular information
318
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3061
閱讀資訊的那個人
13:21
could choose any engine,
319
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2495
可以選擇任何引擎,
13:23
and they could read it in their own mother tongue,
320
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2736
他們可以用自己的母語閱讀,
13:26
in their native language.
321
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3939
用他們當地的語言閱讀。
13:30
I mean, this is an incredibly attractive idea,
322
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2722
我的意思是,這是非常吸引人的想法,
13:33
especially for India.
323
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1999
尤其是在印度。
13:35
We have so many different languages.
324
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1690
我們有好多種語言。
13:36
There's a song about India, and there's a description
325
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2142
有首關於印度的歌,其中有一段描述
13:39
of the country as, it says,
326
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2344
將國家比喻為
13:41
(in Sanskrit).
327
821348
2360
(梵語)。
13:43
That means "ever-smiling speaker
328
823708
2773
意謂著「使用美好語言、
13:46
of beautiful languages."
329
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4519
永遠微笑的講者」。
13:51
Language is beautiful.
330
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1964
語言是美好的。
13:52
I think it's the most beautiful of human creations.
331
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2454
我認為語言是人類最美好的創造。
13:55
I think it's the loveliest thing
that our brains have invented.
332
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3978
我認為語言是人腦發明最可愛的東西。
13:59
It entertains, it educates, it enlightens,
333
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3584
語言能娛樂、教育、啟發,
14:02
but what I like the most about language
334
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2044
但是我最愛的一點是
14:05
is that it empowers.
335
845024
1500
語言能賦予力量。
14:06
I want to leave you with this.
336
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1838
我想分享一件事。
14:08
This is a photograph of my collaborators,
337
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2385
這是我合作夥伴的照片,
14:10
my earliest collaborators
338
850747
997
我最初的合作夥伴,
14:11
when I started working on language
339
851744
1462
當我開始研究語言、
14:13
and autism and various other things.
340
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1502
自閉症和各種不同的事。
14:14
The girl's name is Pavna,
341
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1417
這位女孩名為帕芙娜,
14:16
and that's her mother, Kalpana.
342
856125
1902
那是她的母親卡派納,
14:18
And Pavna's an entrepreneur,
343
858027
2138
帕芙娜是企業家,
14:20
but her story is much more remarkable than mine,
344
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2371
但是她的故事比我的更非凡,
14:22
because Pavna is about 23.
345
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2400
因為帕芙娜大概才 23 歲。
14:24
She has quadriplegic cerebral palsy,
346
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2552
她患有四肢型腦性麻庳,
14:27
so ever since she was born,
347
867488
1640
因此從她出生以來,
14:29
she could neither move nor talk.
348
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3600
她就不能動也不能說話。
14:32
And everything that she's accomplished so far,
349
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2403
迄今她所完成的所有事情,
14:35
finishing school, going to college,
350
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2227
完成學業、上大學、
14:37
starting a company,
351
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1416
開公司,
14:38
collaborating with me to develop Avaz,
352
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2140
和我合作開發「阿維思」,
14:40
all of these things she's done
353
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1892
她要做任何事情
14:42
with nothing more than moving her eyes.
354
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5523
都只能移動她的雙眼。
14:48
Daniel Webster said this:
355
888329
2689
丹尼爾.韋伯斯特說:
(註:美國已故政治家)
14:51
He said, "If all of my possessions were taken
356
891018
2940
「如果要拿走我的一切,
14:53
from me with one exception,
357
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2988
只能留下一種,
14:56
I would choose to keep the power of communication,
358
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2981
我會選擇保留溝通的能力,
14:59
for with it, I would regain all the rest."
359
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3903
以此,我就能取回全部。」
15:03
And that's why, of all of these incredible
applications of FreeSpeech,
360
903830
5116
那就是「輕鬆講」的所有美好功能中,
15:08
the one that's closest to my heart
361
908946
2080
最能貼近我心的一種
15:11
still remains the ability for this
362
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2068
還保留這項能力,
15:13
to empower children with disabilities
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2380
賦予身障孩童
15:15
to be able to communicate,
364
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1773
擁能溝通的能力,
15:17
the power of communication,
365
917247
1789
擁有溝通的力量,
15:19
to get back all the rest.
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2240
就能取回一切。
15:21
Thank you.
367
921276
1397
謝謝。
15:22
(Applause)
368
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1332
(掌聲)
15:24
Thank you. (Applause)
369
924005
4199
謝謝。(掌聲)
15:28
Thank you. Thank you. Thank you. (Applause)
370
928204
5323
謝謝。(掌聲)
15:33
Thank you. Thank you. Thank you. (Applause)
371
933527
4000
謝謝。(掌聲)
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