Ajit Narayanan: A word game to communicate in any language

114,745 views ・ 2014-03-10

TED


請雙擊下方英文字幕播放視頻。

譯者: Marssi Draw 審譯者: Willy Feng
00:12
I work with children with autism.
0
12721
2670
我服務有自閉症的孩子。
00:15
Specifically, I make technologies
1
15391
1914
更確切來說,我發明科技
00:17
to help them communicate.
2
17305
2171
幫助他們溝通。
00:19
Now, many of the problems that children
3
19476
1539
許多自閉症孩童面臨的問題
00:21
with autism face, they have a common source,
4
21015
3763
出自於同樣的因素,
00:24
and that source is that they find it difficult
5
24778
2094
那就是他們很難
00:26
to understand abstraction, symbolism.
6
26872
5260
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.
7
32132
4652
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.
8
36784
3015
讓我告訴你一些原因。
00:39
You see that this is a picture of a bowl of soup.
9
39799
3934
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.
10
43733
2485
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,
11
46218
2312
這是另外兩張湯的圖片,
00:48
but you can see that these are more abstract
12
48530
2067
但是你會發現它們比較抽象,
00:50
These are not quite as concrete.
13
50597
1856
不太具體。
00:52
And when you get to language,
14
52453
2174
當你使用語言時,
00:54
you see that it becomes a word
15
54627
1868
會發現那個字詞
00:56
whose look, the way it looks and the way it sounds,
16
56495
3261
看起來、聽起來
00:59
has absolutely nothing to do with what it started with,
17
59756
2912
和它以什麼開頭
01:02
or what it represents, which is the bowl of soup.
18
62668
2830
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,
19
65498
2900
因此,基本上那是一個完全抽象、
01:08
a completely arbitrary representation of something
20
68398
2576
存在真實世界中某種事物的
01:10
which is in the real world,
21
70974
1163
一種任意的表述,
01:12
and this is something that children with autism
22
72137
1791
自閉症的孩子在這方面
01:13
have an incredible amount of difficulty with.
23
73928
3164
有很大的困難。
01:17
Now that's why most of the people that work with children with autism --
24
77092
2751
那就是為什麼許多 協助自閉症孩童的人們
01:19
speech therapists, educators --
25
79843
1878
——語言治療師、教育人士——
01:21
what they do is, they try to help children with autism
26
81721
2633
他們協助自閉症孩童
01:24
communicate not with words, but with pictures.
27
84354
3229
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,
28
87583
1930
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
29
89513
2458
這孩子會拿起
01:31
three different pictures, "I," "want," and "soup,"
30
91971
2260
三張不同的圖片「我」、「想喝」、「湯」,
01:34
and they would put these together,
31
94231
1609
然後把圖排在一起,
01:35
and then the therapist or the parent would
32
95840
1867
那麼治療師或家長就能理解
01:37
understand that this is what the kid wants to say.
33
97707
1887
這是孩子想說的話。
01:39
And this has been incredibly effective;
34
99594
1778
三四十年來
01:41
for the last 30, 40 years
35
101372
2141
這方法一直都很有效,
01:43
people have been doing this.
36
103513
1613
大家都這麼做。
01:45
In fact, a few years back,
37
105126
1349
事實上,幾年前
01:46
I developed an app for the iPad
38
106475
2675
我開發了一個 iPad 的應用程式,
01:49
which does exactly this. It's called Avaz,
39
109150
2255
名為「阿維思」(Avaz),就是採用此法。
01:51
and the way it works is that kids select
40
111405
2279
操作方式是讓孩子選擇
01:53
different pictures.
41
113684
1321
不同的圖片,
01:55
These pictures are sequenced together to form sentences,
42
115005
2570
將圖片排列成句子,
01:57
and these sentences are spoken out.
43
117575
1719
然後這些句子會被唸出。
01:59
So Avaz is essentially converting pictures,
44
119294
3025
因此基本上「阿維思」會轉換圖片,
02:02
it's a translator, it converts pictures into speech.
45
122319
3960
它是翻譯機,能將圖片轉換成言語。
02:06
Now, this was very effective.
46
126279
1718
這很有用。
02:07
There are thousands of children using this,
47
127997
1384
有成千上萬的孩子使用它,
02:09
you know, all over the world,
48
129381
1430
遍及全世界,
02:10
and I started thinking about
49
130811
2175
於是我開始思考
02:12
what it does and what it doesn't do.
50
132986
2654
它做了什麼,又漏了什麼。
02:15
And I realized something interesting:
51
135640
1684
我發現某件很有趣的事:
02:17
Avaz helps children with autism learn words.
52
137324
4203
「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
53
141527
2405
但沒有教他們
02:23
word patterns.
54
143932
2748
文字模式。
02:26
Let me explain this in a little more detail.
55
146680
2472
讓我說明一些細節。
02:29
Take this sentence: "I want soup tonight."
56
149152
3057
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.
57
152209
4080
這不只是文字傳達了意義,
02:36
It's also the way in which these words are arranged,
58
156289
3140
這些文字排列的方式、
02:39
the way these words are modified and arranged.
59
159429
2515
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"
60
161959
2306
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
61
164265
1984
會完全不同於
02:46
"Soup want I tonight," which is completely meaningless.
62
166249
3312
「湯想喝我今晚」這樣無意義的句子。
02:49
So there is another hidden abstraction here
63
169561
2619
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,
64
172180
3557
讓自閉症孩童難以處理,
02:55
and that's the fact that you can modify words
65
175737
2840
那就是你能透過修飾文字、
02:58
and you can arrange them to have
66
178577
2101
排列文字,
03:00
different meanings, to convey different ideas.
67
180678
2895
讓它有不同的意義,傳達不同的想法。
03:03
Now, this is what we call grammar.
68
183573
3459
我們稱之為文法。
03:07
And grammar is incredibly powerful,
69
187032
2036
而文法的力量十分強大,
03:09
because grammar is this one component of language
70
189068
3157
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
71
192225
3489
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,
72
195714
4531
傳達無限種資訊、
03:20
an infinite amount of ideas.
73
200245
2134
無限種想法。
03:22
It's the way in which you can put things together
74
202379
2002
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.
75
204381
2168
來傳達所有你想表達的事。
03:26
And so after I developed Avaz,
76
206549
2127
因此在我開發「阿維思」之後,
03:28
I worried for a very long time
77
208676
1568
有件事讓我擔心很久,
03:30
about how I could give grammar to children with autism.
78
210244
3910
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.
79
214154
2275
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
80
216429
3449
我巧遇自閉症的孩童
03:39
conversing with her mom,
81
219878
2109
和她的母親對話,
03:41
and this is what happened.
82
221987
2094
事情就這樣發生了。
03:44
Completely out of the blue, very spontaneously,
83
224081
2186
事發非常突然、不期而遇,
03:46
the child got up and said, "Eat."
84
226267
2463
那孩子站起來說:「吃。」
03:48
Now what was interesting was
85
228730
1770
有趣的是
03:50
the way in which the mom was trying to tease out
86
230500
4244
那位媽媽誘導小孩的方式,
03:54
the meaning of what the child wanted to say
87
234744
2213
她讓小孩透過回答她的問題
03:56
by talking to her in questions.
88
236957
2260
表達出想說的話。
03:59
So she asked, "Eat what? Do you want to eat ice cream?
89
239217
2593
因此她問:「吃什麼?」 「你想吃冰淇淋?」
04:01
You want to eat? Somebody else wants to eat?
90
241810
2112
「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?"
91
243922
3313
「你想現在吃冰淇淋?」 「你想晚上吃冰淇淋?」
04:07
And then it struck me that
92
247235
1514
我突然意識到
04:08
what the mother had done was something incredible.
93
248749
2028
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
94
250777
1994
她已經能讓那個孩子
04:12
an idea to her without grammar.
95
252771
4138
不用文法就能傳達想法。
04:16
And it struck me that maybe this is what
96
256909
2696
我突然想到也許這就是
04:19
I was looking for.
97
259605
1385
我在找的方式。
04:20
Instead of arranging words in an order, in sequence,
98
260990
4142
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
99
265132
2172
不如將文字排列在這張圖中,
04:27
in this map, where they're all linked together
100
267304
3811
文字連結在一起的方式
04:31
not by placing them one after the other
101
271115
2143
不是透過將它們一個接一個排列,
04:33
but in questions, in question-answer pairs.
102
273258
3284
而是透過問題,多組問答題。
04:36
And so if you do this, then what you're conveying
103
276542
2358
因此如果你這麼做,那你傳達的
04:38
is not a sentence in English,
104
278900
1986
不是一個英文句子,
04:40
but what you're conveying is really a meaning,
105
280886
2966
你傳達的是一個意義,
04:43
the meaning of a sentence in English.
106
283852
1511
一個英文句子的意義。
04:45
Now, meaning is really the underbelly, in some sense, of language.
107
285363
2932
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.
108
288295
3821
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
109
292116
2503
而此想法是這種特殊的表述
04:54
might convey meaning in its raw form.
110
294619
3261
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,
111
297880
1771
這件事讓我很興奮,
04:59
hopping around all over the place,
112
299651
1493
開心得手舞足蹈,
05:01
trying to figure out if I can convert
113
301144
1771
試著確認我是否能
05:02
all possible sentences that I hear into this.
114
302915
2524
將所有聽見的詞句轉換成這樣。
05:05
And I found that this is not enough.
115
305439
1773
我發現這還不夠。
05:07
Why is this not enough?
116
307212
1385
為什麼不夠呢?
05:08
This is not enough because if you wanted to convey
117
308597
1711
不夠是因為如果你想要傳達
05:10
something like negation,
118
310308
2250
否定的句子,
05:12
you want to say, "I don't want soup,"
119
312558
1736
比如說:「我不想喝湯。」
05:14
then you can't do that by asking a question.
120
314294
2220
那麼你就不能用問句完成。
05:16
You do that by changing the word "want."
121
316514
2285
你會改變「想」這個字。
05:18
Again, if you wanted to say,
122
318799
1637
同樣地,如果你想說:
05:20
"I wanted soup yesterday,"
123
320436
1980
「我昨天本來 想喝湯。」
05:22
you do that by converting the word "want" into "wanted."
124
322416
2737
你把「想」轉換成「本來想」。
05:25
It's a past tense.
125
325153
1666
那是過去式。
05:26
So this is a flourish which I added
126
326819
2103
因此我加了這個功能
05:28
to make the system complete.
127
328922
1576
讓系統更完善。
05:30
This is a map of words joined together
128
330498
1977
這是許多單字的連結圖,
05:32
as questions and answers,
129
332475
1656
以問句和答案組合而成,
05:34
and with these filters applied on top of them
130
334131
2264
有了這些篩選功能在上面,
05:36
in order to modify them to represent
131
336395
1817
就能做修改,呈現出
05:38
certain nuances.
132
338212
1709
較細微的差異。
05:39
Let me show you this with a different example.
133
339921
1951
讓我舉個不同的例子來說明。
05:41
Let's take this sentence:
134
341872
1254
以這個句子來說:
05:43
"I told the carpenter I could not pay him."
135
343126
1980
「我告訴了木工我不能付錢。」
05:45
It's a fairly complicated sentence.
136
345106
1792
這是個蠻複雜的句子。
05:46
The way that this particular system works,
137
346898
1893
這個特殊系統運作的方式是
05:48
you can start with any part of this sentence.
138
348791
2578
你可以從句子的任何一處開始。
05:51
I'm going to start with the word "tell."
139
351369
1698
我用「告訴」開頭來做說明。
05:53
So this is the word "tell."
140
353067
1462
這個字是「告訴」,
05:54
Now this happened in the past,
141
354529
1600
但這是以前發生的事,
05:56
so I'm going to make that "told."
142
356129
2223
所以我要說「告訴了」。
05:58
Now, what I'm going to do is,
143
358352
1708
現在我想做的是,
06:00
I'm going to ask questions.
144
360060
1756
我開始問問題。
06:01
So, who told? I told.
145
361816
2364
是誰「告訴」? 是我。
06:04
I told whom? I told the carpenter.
146
364180
1927
我告訴了誰? 我告訴了木工。
06:06
Now we start with a different part of the sentence.
147
366107
1751
現在我們從句子的另一處開始,
06:07
We start with the word "pay,"
148
367858
1867
以「付錢」開始,
06:09
and we add the ability filter to it to make it "can pay."
149
369725
4577
我們加上使役動詞,讓它變成「能付錢」,
06:14
Then we make it "can't pay,"
150
374302
2101
接著我們就能改成「不能付錢」,
06:16
and we can make it "couldn't pay"
151
376403
1599
接著就能更改時態,
06:18
by making it the past tense.
152
378002
1663
將它改為過去式。
06:19
So who couldn't pay? I couldn't pay.
153
379665
1923
那是誰不能付錢? 我不能付錢。
06:21
Couldn't pay whom? I couldn't pay the carpenter.
154
381588
2676
不能付錢給誰? 我不能付錢給木工。
06:24
And then you join these two together
155
384264
1731
接著你透過問這個問題
06:25
by asking this question:
156
385995
1350
把這兩個部分連在一起:
06:27
What did I tell the carpenter?
157
387345
1737
我告訴了木工什麼?
06:29
I told the carpenter I could not pay him.
158
389082
4049
我告訴了木工我不能付錢。
06:33
Now think about this. This is
159
393131
1937
想想看這個問題,
06:35
—(Applause)—
160
395068
3542
(掌聲)
06:38
this is a representation of this sentence
161
398610
3672
這是這個句子要表達的內容,
06:42
without language.
162
402282
2435
沒有語言。
06:44
And there are two or three interesting things about this.
163
404717
2192
這裡有兩到三件有趣的事。
06:46
First of all, I could have started anywhere.
164
406909
3131
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."
165
410040
2243
我不一定要從「告訴」開始。
06:52
I could have started anywhere in the sentence,
166
412283
1416
我能從句子的任何一部分開始,
06:53
and I could have made this entire thing.
167
413699
1507
還是能完成整件事。
06:55
The second thing is, if I wasn't an English speaker,
168
415206
2776
第二點是,如果我不是說英語的人,
06:57
if I was speaking in some other language,
169
417982
2175
如果我說的是別的語言,
07:00
this map would actually hold true in any language.
170
420157
3156
這個地圖真的在任何語言都管用。
07:03
So long as the questions are standardized,
171
423313
1990
只要這個問題符合標準,
07:05
the map is actually independent of language.
172
425303
4287
這個地圖就能獨立於語言使用。
07:09
So I call this FreeSpeech,
173
429590
2115
因此我稱它為「輕鬆講」 (FreeSpeech),
07:11
and I was playing with this for many, many months.
174
431705
2935
我已經玩了好幾個月,
07:14
I was trying out so many different combinations of this.
175
434640
2726
並試著使用許多不同的組合。
07:17
And then I noticed something very interesting about FreeSpeech.
176
437366
2289
後來,我注意到「輕鬆講」有個有趣的部分。
07:19
I was trying to convert language,
177
439655
3243
我試著轉換語言,
07:22
convert sentences in English into sentences in FreeSpeech,
178
442898
2384
轉換英語句子和「輕鬆講」的句子,
07:25
and vice versa, and back and forth.
179
445282
1752
來回反覆不斷嘗試。
07:27
And I realized that this particular configuration,
180
447034
2255
我理解這種特殊的結構,
07:29
this particular way of representing language,
181
449289
2026
這種表現語言的特殊方式
07:31
it allowed me to actually create very concise rules
182
451315
4395
讓我能夠真正地建立很簡要的規則,
07:35
that go between FreeSpeech on one side
183
455710
2734
在「輕鬆講」
07:38
and English on the other.
184
458444
1488
以及英語之間的規則。
07:39
So I could actually write this set of rules
185
459932
2180
我確實能寫下這組規則,
07:42
that translates from this particular representation into English.
186
462112
3395
讓這個特殊的表述轉換成英語。
07:45
And so I developed this thing.
187
465507
1831
因此我發明了這項產品,
07:47
I developed this thing called the FreeSpeech Engine
188
467338
2232
稱為「輕鬆講引擎」,
07:49
which takes any FreeSpeech sentence as the input
189
469570
2561
能把任何「輕鬆講」的句子輸入,
07:52
and gives out perfectly grammatical English text.
190
472131
3930
然後產出有完美文法的英語。
07:56
And by putting these two pieces together,
191
476061
1605
透過組合
07:57
the representation and the engine,
192
477666
1881
表述與引擎,
07:59
I was able to create an app, a technology for children with autism,
193
479547
3796
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
194
483343
2499
不只是提供他們文字,
08:05
but also gives them grammar.
195
485842
3941
也提供他們文法。
08:09
So I tried this out with kids with autism,
196
489783
2360
我在自閉症孩童身上測試,
08:12
and I found that there was an incredible amount of identification.
197
492143
5013
發現了很驚人的成效。
08:17
They were able to create sentences in FreeSpeech
198
497156
2720
他們用「輕鬆講」建立的句子
08:19
which were much more complicated but much more effective
199
499876
2558
複雜程度和效用都遠高於
08:22
than equivalent sentences in English,
200
502434
2899
用英語講同一句話,
08:25
and I started thinking about
201
505333
1682
我開始思考
08:27
why that might be the case.
202
507015
1969
為什麼會成功。
08:28
And I had an idea, and I want to talk to you about this idea next.
203
508984
4287
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,
204
513271
3142
大約在 1997 年時,大約 15 年前,
08:36
there were a group of scientists that were trying
205
516413
2011
有一群科學家嘗試
08:38
to understand how the brain processes language,
206
518424
2389
理解大腦處理語言的方式,
08:40
and they found something very interesting.
207
520813
1779
他們發現一件很有趣的事情。
08:42
They found that when you learn a language
208
522592
1872
就是當你學習一種語言,
08:44
as a child, as a two-year-old,
209
524464
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 —
212
531342
3911
──舉例來說,如果我現在想學日語──
08:55
a completely different part of my brain is used.
213
535253
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,
214
537960
1831
我不了解為什麼會這樣,
08:59
but my guess is that that's because
215
539791
1991
但我猜是因為
09:01
when you learn a language as an adult,
216
541782
2437
成年時學習語言
09:04
you almost invariably learn it
217
544219
1616
幾乎無可避免會
09:05
through your native language, or through your first language.
218
545835
4266
透過你的母語、習慣語言來學習。
09:10
So what's interesting about FreeSpeech
219
550101
3252
「輕鬆講」有趣的是
09:13
is that when you create a sentence
220
553353
1802
當你建立一個句子,
09:15
or when you create language,
221
555155
1695
或是建立一種語言,
09:16
a child with autism creates language with FreeSpeech,
222
556850
3070
自閉症孩童用「輕鬆講」建立語言,
09:19
they're not using this support language,
223
559920
1833
他們不是用它來支援語言,
09:21
they're not using this bridge language.
224
561753
2211
他們不是用它來連結語言,
09:23
They're directly constructing the sentence.
225
563964
2657
他們是直接建立句子。
09:26
And so this gave me this idea.
226
566621
2193
這讓我有個想法。
09:28
Is it possible to use FreeSpeech
227
568814
2024
有可能讓「輕鬆講」
09:30
not for children with autism
228
570838
2510
教自閉症孩童語言之外,
09:33
but to teach language to people without disabilities?
229
573348
6262
也教非身障的孩童嗎?
09:39
And so I tried a number of experiments.
230
579610
1978
因此我嘗試許多實驗。
09:41
The first thing I did was I built a jigsaw puzzle
231
581588
2948
首先我設計了一個拼圖,
09:44
in which these questions and answers
232
584536
1970
這些問題和答案
09:46
are coded in the form of shapes,
233
586506
1835
都編碼成各種形狀,
09:48
in the form of colors,
234
588341
1138
各種顏色,
09:49
and you have people putting these together
235
589479
1849
操作人把這些放在一起,
09:51
and trying to understand how this works.
236
591328
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,
237
593101
2376
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
595477
2661
孩童可以玩文字遊戲,
09:58
and with a reinforcement,
239
598138
1704
並且有強化的功能,
09:59
a sound reinforcement of visual structures,
240
599842
2585
以聽覺強化視覺,
10:02
they're able to learn language.
241
602427
2013
他們就能學習語言。
10:04
And this, this has a lot of potential, a lot of promise,
242
604440
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
609151
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
245
610555
2074
他們打算讓上百萬名孩童嘗試,
10:12
trying to teach them English.
246
612629
2605
試著教他們英語。
10:15
And the dream, the hope, the vision, really,
247
615234
2614
而這個夢想、希望、願景
10:17
is that when they learn English this way,
248
617848
3082
即是當他們以此學習英語,
10:20
they learn it with the same proficiency
249
620930
2643
他們能夠表達流利,
10:23
as their mother tongue.
250
623573
3718
就像母語一樣。
10:27
All right, let's talk about something else.
251
627291
3816
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
633104
1271
這是說話。
10:34
So speech is the primary mode of communication
254
634375
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
639805
1245
說話是單面的。
10:41
Why is it one-dimensional?
258
641050
1359
為什麼是單面的?
10:42
It's one-dimensional because it's sound.
259
642409
1568
因為說話是聲音,所以它是單面的。
10:43
It's also one-dimensional because
260
643977
1539
也因為
10:45
our mouths are built that way.
261
645516
1205
那是嘴巴的功能。
10:46
Our mouths are built to create one-dimensional sound.
262
646721
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
650233
2866
但是如果你想想大腦,
10:53
the thoughts that we have in our heads
264
653099
1764
在我們頭腦裡的思想
10:54
are not one-dimensional.
265
654863
2102
並非一面向的。
10:56
I mean, we have these rich,
266
656965
1459
我的意思是,我們有這些豐富、
10:58
complicated, multi-dimensional ideas.
267
658424
3028
複雜和多面向的想法。
11:01
Now, it seems to me that language
268
661452
1690
對我來說,語言
11:03
is really the brain's invention
269
663142
2332
就是大腦的發明,
11:05
to convert this rich, multi-dimensional thought
270
665474
3096
一方面轉換這豐富、
11:08
on one hand
271
668570
1587
多面向的思想,
11:10
into speech on the other hand.
272
670157
1923
另一方面轉換成話語。
11:12
Now what's interesting is that
273
672080
1762
有趣的是
11:13
we do a lot of work in information nowadays,
274
673842
2568
現在我們以資訊做許多事,
11:16
and almost all of that is done in the language domain.
275
676410
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
679489
1939
以 Google 為例,
11:21
Google trawls all these countless billions of websites,
277
681428
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
684105
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,
279
686830
2450
進入 Google 搜尋功能列,輸入英語,
11:29
and it matches the English with the English.
280
689280
4163
會出現符合你要的英語。
11:33
What if we could do this in FreeSpeech instead?
281
693443
3583
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,
282
697026
2301
我推測如果我們這麼做,
11:39
we'd find that algorithms like searching,
283
699327
2068
我們會發現一些規則系統,像是搜尋、
11:41
like retrieval, all of these things,
284
701395
2325
像是擷取,所有的這些功能
11:43
are much simpler and also more effective,
285
703720
3075
都更簡單也更有效,
11:46
because they don't process the data structure of speech.
286
706795
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
711212
5976
相反地,他們處理思想的資料結構。
11:57
The data structure of thought.
288
717188
2808
思想的資料結構。
11:59
That's a provocative idea.
289
719996
2076
那是個令人興奮的概念。
12:02
But let's look at this in a little more detail.
290
722072
2142
讓我們多深入看一點細節。
12:04
So this is the FreeSpeech ecosystem.
291
724214
2366
這是「輕鬆講」的生態系統。
12:06
We have the Free Speech representation on one side,
292
726580
2884
我們一邊有「輕鬆講」的畫面,
12:09
and we have the FreeSpeech Engine, which generates English.
293
729464
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
731694
1725
請想像
12:13
FreeSpeech, I told you, is completely language-independent.
295
733419
2544
「輕鬆講」是完全獨立的語言。
12:15
It doesn't have any specific information in it
296
735963
2087
裡面沒有任何關於英語的
12:18
which is about English.
297
738050
1228
特定資訊。
12:19
So everything that this system knows about English
298
739278
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
742078
4620
12:26
That's a pretty interesting concept in itself.
300
746698
2237
這之中有個很有趣的概念。
12:28
You've encoded an entire human language
301
748935
3604
你已經將所有的人類語言編碼入
12:32
into a software program.
302
752539
2645
一套軟體中。
12:35
But if you look at what's inside the engine,
303
755184
2531
但是如果你看這個引擎的內部,
12:37
it's actually not very complicated.
304
757715
2358
會發現其實不複雜,
12:40
It's not very complicated code.
305
760073
2105
不是很複雜的編碼。
12:42
And what's more interesting is the fact that
306
762178
2672
更有趣的是,
12:44
the vast majority of the code in that engine
307
764850
2203
在那個引擎中大多數的編碼
12:47
is not really English-specific.
308
767053
2412
其實都不是只針對英語。
12:49
And that gives this interesting idea.
309
769465
1895
因此有了這個有趣的想法,
12:51
It might be very easy for us to actually
310
771360
2038
我們也許可以因此輕易地
12:53
create these engines in many, many different languages,
311
773398
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
777224
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
783578
2799
這引起了另一個有趣的想法。
13:06
For example, supposing I was a writer,
314
786377
2654
舉例來說,假設我是作家,
13:09
say, for a newspaper or for a magazine.
315
789031
2122
在報社或雜誌社工作。
13:11
I could create content in one language, FreeSpeech,
316
791153
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
796164
2056
然後有個人買了那則報導,
13:18
the person who's reading that particular information
318
798220
3061
閱讀資訊的那個人
13:21
could choose any engine,
319
801281
2495
可以選擇任何引擎,
13:23
and they could read it in their own mother tongue,
320
803776
2736
他們可以用自己的母語閱讀,
13:26
in their native language.
321
806512
3939
用他們當地的語言閱讀。
13:30
I mean, this is an incredibly attractive idea,
322
810451
2722
我的意思是,這是非常吸引人的想法,
13:33
especially for India.
323
813173
1999
尤其是在印度。
13:35
We have so many different languages.
324
815172
1690
我們有好多種語言。
13:36
There's a song about India, and there's a description
325
816862
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
819004
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
826481
4519
永遠微笑的講者」。
13:51
Language is beautiful.
330
831000
1964
語言是美好的。
13:52
I think it's the most beautiful of human creations.
331
832964
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
835418
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
839396
3584
語言能娛樂、教育、啟發,
14:02
but what I like the most about language
334
842980
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
846524
1838
我想分享一件事。
14:08
This is a photograph of my collaborators,
337
848362
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
853206
1502
自閉症和各種不同的事。
14:14
The girl's name is Pavna,
341
854708
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
860165
2371
但是她的故事比我的更非凡,
14:22
because Pavna is about 23.
345
862536
2400
因為帕芙娜大概才 23 歲。
14:24
She has quadriplegic cerebral palsy,
346
864936
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
869128
3600
她就不能動也不能說話。
14:32
And everything that she's accomplished so far,
349
872728
2403
迄今她所完成的所有事情,
14:35
finishing school, going to college,
350
875131
2227
完成學業、上大學、
14:37
starting a company,
351
877358
1416
開公司,
14:38
collaborating with me to develop Avaz,
352
878774
2140
和我合作開發「阿維思」,
14:40
all of these things she's done
353
880914
1892
她要做任何事情
14:42
with nothing more than moving her eyes.
354
882806
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
893958
2988
只能留下一種,
14:56
I would choose to keep the power of communication,
358
896946
2981
我會選擇保留溝通的能力,
14:59
for with it, I would regain all the rest."
359
899927
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
911026
2068
還保留這項能力,
15:13
to empower children with disabilities
363
913094
2380
賦予身障孩童
15:15
to be able to communicate,
364
915474
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.
366
919036
2240
就能取回一切。
15:21
Thank you.
367
921276
1397
謝謝。
15:22
(Applause)
368
922673
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)
關於本網站

本網站將向您介紹對學習英語有用的 YouTube 視頻。 您將看到來自世界各地的一流教師教授的英語課程。 雙擊每個視頻頁面上顯示的英文字幕,從那裡播放視頻。 字幕與視頻播放同步滾動。 如果您有任何意見或要求,請使用此聯繫表與我們聯繫。

https://forms.gle/WvT1wiN1qDtmnspy7