TEDxBrussels
Jeremy Howard: The wonderful and terrifying implications of computers that can learn
傑瑞米.霍華德: 有學習能力的電腦帶來的美好但駭人影響
Filmed:
Readability: 4.5
2,532,971 views
當我們知道電腦如何學習會發生什麼事情？科學技術家傑瑞米.霍華德分享了一些深度學習驚人的迅速發展情況，那是一種科技可以讓電腦學中文和確認圖片和思考一些醫學診斷。（一項深度學習儀器看了數小時 Youtube 之後，學會了貓這個概念）。你身邊的電腦獲得某一領域專業知識的方法將改變，改變的速度比想像的還要快。
Jeremy Howard - Data scientist
Jeremy Howard imagines how advanced machine learning can improve our lives. Full bio
Jeremy Howard imagines how advanced machine learning can improve our lives. Full bio
Double-click the English transcript below to play the video.
00:12
It used to be that if you wanted
to get a computer to do something new,
to get a computer to do something new,
0
880
4013
過去如果想用電腦來作點新東西，
00:16
you would have to program it.
1
4893
1554
你需要設計程式。
00:18
Now, programming, for those of you here
that haven't done it yourself,
that haven't done it yourself,
2
6447
3411
而現在，
你們可能沒做過程式設計這件事，
你們可能沒做過程式設計這件事，
00:21
requires laying out in excruciating detail
3
9858
3502
它需要規劃相當詳細的細節
00:25
every single step that you want
the computer to do
the computer to do
4
13360
3367
那些你想讓電腦執行的每一個步驟
00:28
in order to achieve your goal.
5
16727
2362
以達到你的目的。
00:31
Now, if you want to do something
that you don't know how to do yourself,
that you don't know how to do yourself,
6
19089
3496
如果你沒有概念要怎麼做的話
00:34
then this is going
to be a great challenge.
to be a great challenge.
7
22585
2063
那會是個很大的挑戰。
00:36
So this was the challenge faced
by this man, Arthur Samuel.
by this man, Arthur Samuel.
8
24648
3483
亞瑟·撒姆爾也曾面對這種挑戰。
00:40
In 1956, he wanted to get this computer
9
28131
4077
他在 1956 年便想到用這台電腦
00:44
to be able to beat him at checkers.
10
32208
2340
能夠在西洋跳棋棋賽打敗他。
00:46
How can you write a program,
11
34548
2040
要如何設計這樣的程式？
00:48
lay out in excruciating detail,
how to be better than you at checkers?
how to be better than you at checkers?
12
36588
3806
把細節通通寫出來，
如何讓電腦比你還會下棋？
如何讓電腦比你還會下棋？
00:52
So he came up with an idea:
13
40394
1722
於是他想出了一個點子：
00:54
he had the computer play
against itself thousands of times
against itself thousands of times
14
42116
3724
他讓電腦與電腦本身對弈數千次
00:57
and learn how to play checkers.
15
45840
2524
以學習如何玩西洋棋。
01:00
And indeed it worked,
and in fact, by 1962,
and in fact, by 1962,
16
48364
3180
然而，在 1962 年做到了，
01:03
this computer had beaten
the Connecticut state champion.
the Connecticut state champion.
17
51544
4017
電腦打敗了康乃狄克州的冠軍。
01:07
So Arthur Samuel was
the father of machine learning,
the father of machine learning,
18
55561
2973
於是亞瑟·撒姆爾
成為了機器學習之父，
成為了機器學習之父，
01:10
and I have a great debt to him,
19
58534
1717
我尊敬他，
01:12
because I am a machine
learning practitioner.
learning practitioner.
20
60251
2763
因為我也是個機器學習實踐者，
01:15
I was the president of Kaggle,
21
63014
1465
我曾是 Kaggle 的會長，
01:16
a community of over 200,000
machine learning practictioners.
machine learning practictioners.
22
64479
3388
Kaggle 是個超過 20 萬人的
機器學習實踐者的社群。
機器學習實踐者的社群。
01:19
Kaggle puts up competitions
23
67867
2058
Kaggle 設立了一些比賽
01:21
to try and get them to solve
previously unsolved problems,
previously unsolved problems,
24
69925
3708
讓他們參與解決
過去無法解決的問題，
過去無法解決的問題，
01:25
and it's been successful
hundreds of times.
hundreds of times.
25
73633
3837
而有上百的成功個案。
01:29
So from this vantage point,
I was able to find out
I was able to find out
26
77470
2470
從這有利的環境中，
我發現
我發現
01:31
a lot about what machine learning
can do in the past, can do today,
can do in the past, can do today,
27
79940
3950
很多機器學習在
過去和現在可以做到的事情，
過去和現在可以做到的事情，
01:35
and what it could do in the future.
28
83890
2362
還有未來可以做到的事。
01:38
Perhaps the first big success of
machine learning commercially was Google.
machine learning commercially was Google.
29
86252
4423
第一個機器學習的
商業成功案例是谷歌。
商業成功案例是谷歌。
01:42
Google showed that it is
possible to find information
possible to find information
30
90675
3109
谷歌展示找尋資料的方法
01:45
by using a computer algorithm,
31
93784
1752
是使用計算機演算法，
01:47
and this algorithm is based
on machine learning.
on machine learning.
32
95536
2901
而這演算法是以機器學習為基礎。
01:50
Since that time, there have been many
commercial successes of machine learning.
commercial successes of machine learning.
33
98437
3886
自此，機器學習
有很多的商業成功例子，
有很多的商業成功例子，
01:54
Companies like Amazon and Netflix
34
102323
1837
譬如亞馬遜和奈飛公司
01:56
use machine learning to suggest
products that you might like to buy,
products that you might like to buy,
35
104160
3716
用機器學習會向你推薦
你可能想買的商品，
你可能想買的商品，
01:59
movies that you might like to watch.
36
107876
2020
你可能想看的影片。
02:01
Sometimes, it's almost creepy.
37
109896
1807
有時，你可能會很訝異。
02:03
Companies like LinkedIn and Facebook
38
111703
1954
像領英和臉書等公司
02:05
sometimes will tell you about
who your friends might be
who your friends might be
39
113657
2594
有些時候會告訴你
誰會是你的朋友
誰會是你的朋友
02:08
and you have no idea how it did it,
40
116251
1977
而你根本不知道他們是如何做到的，
02:10
and this is because it's using
the power of machine learning.
the power of machine learning.
41
118228
2967
因為他們用了
機器學習這強大的功能。
機器學習這強大的功能。
02:13
These are algorithms that have
learned how to do this from data
learned how to do this from data
42
121195
2957
演算法從資料去學習這類事情
02:16
rather than being programmed by hand.
43
124152
3247
不需要動手去編寫程式。
02:19
This is also how IBM was successful
44
127399
2478
這也是 IBM 過去能成功的原因
02:21
in getting Watson to beat
the two world champions at "Jeopardy,"
the two world champions at "Jeopardy,"
45
129877
3862
讓超級電腦「華生」在「危機遊戲」中
打敗兩屆世界冠軍。
打敗兩屆世界冠軍。
02:25
answering incredibly subtle
and complex questions like this one.
and complex questions like this one.
46
133739
3225
回答一些細碎和複雜的問題，像是
02:28
["The ancient 'Lion of Nimrud' went missing
from this city's national museum in 2003
(along with a lot of other stuff)"]
from this city's national museum in 2003
(along with a lot of other stuff)"]
47
136964
2835
「2003年，古獅像在這城市的
國家博物館消失了（連同其他物品）」
國家博物館消失了（連同其他物品）」
02:31
This is also why we are now able
to see the first self-driving cars.
to see the first self-driving cars.
48
139799
3235
這也是我們現在能看到第一部
自行駕駛汽車的原因。
自行駕駛汽車的原因。
02:35
If you want to be able to tell
the difference between, say,
the difference between, say,
49
143034
2822
如果你能說出不同點，像是
02:37
a tree and a pedestrian,
well, that's pretty important.
well, that's pretty important.
50
145856
2632
一棵樹和一條行人道，
那顯得非常重要。
那顯得非常重要。
02:40
We don't know how to write
those programs by hand,
those programs by hand,
51
148488
2587
我們不知道如何設計這樣的程式，
02:43
but with machine learning,
this is now possible.
this is now possible.
52
151075
2997
不過通過機器，這就成為可能。
02:46
And in fact, this car has driven
over a million miles
over a million miles
53
154072
2608
事實上，
這部汽車已經行駛一百萬英哩
這部汽車已經行駛一百萬英哩
02:48
without any accidents on regular roads.
54
156680
3506
在正常路面沒有發生事故。
02:52
So we now know that computers can learn,
55
160196
3914
我們現在都知道電腦能夠學習，
02:56
and computers can learn to do things
56
164110
1900
學習做一些
02:58
that we actually sometimes
don't know how to do ourselves,
don't know how to do ourselves,
57
166010
2838
有時我們自己也不知道怎麼做的事，
03:00
or maybe can do them better than us.
58
168848
2885
還可能比我們做得更好。
03:03
One of the most amazing examples
I've seen of machine learning
I've seen of machine learning
59
171733
4195
其中一個機器學習的經典例子
03:07
happened on a project that I ran at Kaggle
60
175928
2392
是我在 Kaggle 所做的一個專案
03:10
where a team run by a guy
called Geoffrey Hinton
called Geoffrey Hinton
61
178320
3591
由傑佛里·辛頓帶領的團隊
03:13
from the University of Toronto
62
181911
1552
他是多倫多大學的教授
03:15
won a competition for
automatic drug discovery.
automatic drug discovery.
63
183463
2677
他們贏了新藥研發的比賽。
03:18
Now, what was extraordinary here
is not just that they beat
is not just that they beat
64
186140
2847
他們出色地方
不只打敗了
不只打敗了
03:20
all of the algorithms developed by Merck
or the international academic community,
or the international academic community,
65
188987
4013
默克藥廠或國際學術社群
所研發的演算法，
所研發的演算法，
03:25
but nobody on the team had any background
in chemistry or biology or life sciences,
in chemistry or biology or life sciences,
66
193000
5061
他們的團隊沒有化學
生物或生命科學的背景，
生物或生命科學的背景，
03:30
and they did it in two weeks.
67
198061
2169
而且只花了兩個星期就完成。
03:32
How did they do this?
68
200230
1381
他們怎麼做到的？
03:34
They used an extraordinary algorithm
called deep learning.
called deep learning.
69
202421
2921
他們用了一個很出色的演算法
叫做「深度學習」。
叫做「深度學習」。
03:37
So important was this that in fact
the success was covered
the success was covered
70
205342
2949
這是重要且成功的事情
03:40
in The New York Times in a front page
article a few weeks later.
article a few weeks later.
71
208291
3121
在數星期後
被刊登在紐約時報頭版。
被刊登在紐約時報頭版。
03:43
This is Geoffrey Hinton
here on the left-hand side.
here on the left-hand side.
72
211412
2735
左手邊那位是傑佛里·辛頓。
03:46
Deep learning is an algorithm
inspired by how the human brain works,
inspired by how the human brain works,
73
214147
4341
深度學習是一種
受到人類大腦啟發的演算法，
受到人類大腦啟發的演算法，
03:50
and as a result it's an algorithm
74
218488
1812
它是一種演算法
03:52
which has no theoretical limitations
on what it can do.
on what it can do.
75
220300
3841
做法不受理論限制的演算法。
03:56
The more data you give it and the more
computation time you give it,
computation time you give it,
76
224141
2823
你給它越多的資料和
運算時間，
運算時間，
03:58
the better it gets.
77
226964
1312
會得到更好的結果。
04:00
The New York Times also
showed in this article
showed in this article
78
228276
2339
紐約時報的文章裡
04:02
another extraordinary
result of deep learning
result of deep learning
79
230615
2242
也介紹到深度學習的非凡成就
04:04
which I'm going to show you now.
80
232857
2712
我現在要展示給你們看。
04:07
It shows that computers
can listen and understand.
can listen and understand.
81
235569
4941
它顯示電腦能聽懂和理解資料的能力。
04:12
(Video) Richard Rashid: Now, the last step
82
240510
2711
（影片）理察·拉希德:
現在，最後一步是
現在，最後一步是
04:15
that I want to be able
to take in this process
to take in this process
83
243221
3025
我能夠理解這個程序
04:18
is to actually speak to you in Chinese.
84
246246
4715
我能夠跟你說中文。
04:22
Now the key thing there is,
85
250961
2635
現在關鍵的是，
04:25
we've been able to take a large amount
of information from many Chinese speakers
of information from many Chinese speakers
86
253596
5002
我們從很多講中文的人士中
收集大量的資訊
收集大量的資訊
04:30
and produce a text-to-speech system
87
258598
2530
然後產生文字轉化語言的系統
04:33
that takes Chinese text
and converts it into Chinese language,
and converts it into Chinese language,
88
261128
4673
將中文文字轉化成中文語言，
04:37
and then we've taken
an hour or so of my own voice
an hour or so of my own voice
89
265801
4128
然後錄一個小時我自己的聲音
04:41
and we've used that to modulate
90
269929
1891
我們使用它去調變
04:43
the standard text-to-speech system
so that it would sound like me.
so that it would sound like me.
91
271820
4544
使標準文字轉化語音系統的聲音
聽起來像我的聲音。
聽起來像我的聲音。
04:48
Again, the result's not perfect.
92
276364
2540
再一次，雖然結果沒有很完美，
04:50
There are in fact quite a few errors.
93
278904
2648
裡面還有一些錯誤。
04:53
(In Chinese)
94
281552
2484
（中文）
04:56
(Applause)
95
284036
3367
（掌聲）
05:01
There's much work to be done in this area.
96
289446
3576
在這個領域還有很多工作要做。
05:05
(In Chinese)
97
293022
3645
（中文）
05:08
(Applause)
98
296667
3433
（掌聲）
05:13
Jeremy Howard: Well, that was at
a machine learning conference in China.
a machine learning conference in China.
99
301345
3399
傑里米·霍華德：那是在中國舉行的
機器學習研討會。
機器學習研討會。
05:16
It's not often, actually,
at academic conferences
at academic conferences
100
304744
2370
那不常有，事實上，
在學術會議上
在學術會議上
05:19
that you do hear spontaneous applause,
101
307114
1897
聽到熱烈的掌聲，
05:21
although of course sometimes
at TEDx conferences, feel free.
at TEDx conferences, feel free.
102
309011
3676
雖然有些時候
TEDx 講座不拘泥形式。
TEDx 講座不拘泥形式。
05:24
Everything you saw there
was happening with deep learning.
was happening with deep learning.
103
312687
2795
你所看到的都是出於深度學習
05:27
(Applause) Thank you.
104
315482
1525
（掌聲）謝謝。
05:29
The transcription in English
was deep learning.
was deep learning.
105
317007
2282
英文文字翻譯由深度學習完成的。
05:31
The translation to Chinese and the text
in the top right, deep learning,
in the top right, deep learning,
106
319289
3412
翻譯成中文和右上角的文稿
也是出於深度學習，
也是出於深度學習，
05:34
and the construction of the voice
was deep learning as well.
was deep learning as well.
107
322701
3307
連創建聲音也都是深度學習。
05:38
So deep learning is
this extraordinary thing.
this extraordinary thing.
108
326008
3234
深度學習是如此的神奇。
05:41
It's a single algorithm that
can seem to do almost anything,
can seem to do almost anything,
109
329242
3099
它是個單一的演算法
似乎可以完成任何事情，
似乎可以完成任何事情，
05:44
and I discovered that a year earlier,
it had also learned to see.
it had also learned to see.
110
332341
3111
我一年前還發現它可以學會看
05:47
In this obscure competition from Germany
111
335452
2176
這個德國遊戲的比賽
05:49
called the German Traffic Sign
Recognition Benchmark,
Recognition Benchmark,
112
337628
2597
叫德國交通標誌確認基準，
05:52
deep learning had learned
to recognize traffic signs like this one.
to recognize traffic signs like this one.
113
340225
3393
深度學習能認出這個交通標誌。
05:55
Not only could it
recognize the traffic signs
recognize the traffic signs
114
343618
2094
它不只確認交通標誌的能力
05:57
better than any other algorithm,
115
345712
1758
比其他的演算法好，
05:59
the leaderboard actually showed
it was better than people,
it was better than people,
116
347470
2719
在排行榜上更顯示它做得比人類好，
06:02
about twice as good as people.
117
350189
1852
正確性是人類的兩倍。
06:04
So by 2011, we had the first example
118
352041
1996
2011 以前，我們有了第一個例子
06:06
of computers that can see
better than people.
better than people.
119
354037
3405
視力高於人類的電腦。
06:09
Since that time, a lot has happened.
120
357442
2049
從那時開始，許多電腦也可以做到。
06:11
In 2012, Google announced that
they had a deep learning algorithm
they had a deep learning algorithm
121
359491
3514
2012 年谷歌宣佈
使用深度學習演算法
使用深度學習演算法
06:15
watch YouTube videos
122
363005
1415
來監看 Youtube 影片
06:16
and crunched the data
on 16,000 computers for a month,
on 16,000 computers for a month,
123
364420
3437
收集一個月 1,600 台電電腦的資料，
06:19
and the computer independently learned
about concepts such as people and cats
about concepts such as people and cats
124
367857
4361
電腦獨立識別
人或貓的概念
人或貓的概念
06:24
just by watching the videos.
125
372218
1809
僅透過觀看影片。
06:26
This is much like the way
that humans learn.
that humans learn.
126
374027
2352
這樣更像人類的學習方式。
06:28
Humans don't learn
by being told what they see,
by being told what they see,
127
376379
2740
人類並非通過別人的指示來學習，
06:31
but by learning for themselves
what these things are.
what these things are.
128
379119
3331
而是從自己搞懂事情來學習。
06:34
Also in 2012, Geoffrey Hinton,
who we saw earlier,
who we saw earlier,
129
382450
3369
在 2012 年傑佛里·辛頓
我們之前看到的人，
我們之前看到的人，
06:37
won the very popular ImageNet competition,
130
385819
2858
贏了很有名的映像網路比賽，
06:40
looking to try to figure out
from one and a half million images
from one and a half million images
131
388677
4141
嘗試從 150 萬的圖像中找出
06:44
what they're pictures of.
132
392818
1438
想要的圖像。
06:46
As of 2014, we're now down
to a six percent error rate
to a six percent error rate
133
394256
3533
2014 年, 我們現在
圖像辨識的錯誤率
圖像辨識的錯誤率
06:49
in image recognition.
134
397789
1453
降到 6％ 以下。
06:51
This is better than people, again.
135
399242
2026
這再次證明它比人類優秀。
06:53
So machines really are doing
an extraordinarily good job of this,
an extraordinarily good job of this,
136
401268
3769
可見機器
真可以做到如此非凡的成就，
真可以做到如此非凡的成就，
06:57
and it is now being used in industry.
137
405037
2269
它現在已經用在產業上了。
06:59
For example, Google announced last year
138
407306
3042
比如說，谷歌去年宣佈
07:02
that they had mapped every single
location in France in two hours,
location in France in two hours,
139
410348
4585
他們可以在兩小時内把
法國每一個位置繪成地圖，
法國每一個位置繪成地圖，
07:06
and the way they did it was
that they fed street view images
that they fed street view images
140
414933
3447
他們用的方式是
把街景圖像
把街景圖像
07:10
into a deep learning algorithm
to recognize and read street numbers.
to recognize and read street numbers.
141
418380
4319
輸入深度學習演算法
來辨認和讀取街道號碼。
來辨認和讀取街道號碼。
07:14
Imagine how long
it would have taken before:
it would have taken before:
142
422699
2220
想想我們以前需要花多少時間？
07:16
dozens of people, many years.
143
424919
3355
至少好幾十人加上好幾年呢。
07:20
This is also happening in China.
144
428274
1911
同樣的情況也發生在中國。
07:22
Baidu is kind of
the Chinese Google, I guess,
the Chinese Google, I guess,
145
430185
4036
我想「百度」類似中國的谷歌，
07:26
and what you see here in the top left
146
434221
2283
在左上角你會看見
07:28
is an example of a picture that I uploaded
to Baidu's deep learning system,
to Baidu's deep learning system,
147
436504
3974
一張我上傳到
百度深度學習系統的圖片，
百度深度學習系統的圖片，
07:32
and underneath you can see that the system
has understood what that picture is
has understood what that picture is
148
440478
3769
下方你可以看到
系統可以理解這張圖片
系統可以理解這張圖片
07:36
and found similar images.
149
444247
2236
而且能找到相似的圖像。
07:38
The similar images actually
have similar backgrounds,
have similar backgrounds,
150
446483
2736
類似的圖像
也就是有相似的背景，
也就是有相似的背景，
07:41
similar directions of the faces,
151
449219
1658
相似面孔的角度，
07:42
even some with their tongue out.
152
450877
1788
有的圖像甚至有伸出舌頭。
07:44
This is not clearly looking
at the text of a web page.
at the text of a web page.
153
452665
3030
這個網頁的文字看不大清楚，
07:47
All I uploaded was an image.
154
455695
1412
因為我上傳的都是圖像。
07:49
So we now have computers which
really understand what they see
really understand what they see
155
457107
4021
這顯示了電腦能明白他們所看到的
07:53
and can therefore search databases
156
461128
1624
電腦能夠搜尋資料庫
07:54
of hundreds of millions
of images in real time.
of images in real time.
157
462752
3554
以即時的方式從億萬張圖片中搜尋。
07:58
So what does it mean
now that computers can see?
now that computers can see?
158
466306
3230
現在的電腦能夠去看
是表示什麼意思呢？
是表示什麼意思呢？
08:01
Well, it's not just
that computers can see.
that computers can see.
159
469536
2017
其實電腦不只能看見。
08:03
In fact, deep learning
has done more than that.
has done more than that.
160
471553
2069
事實上深度學習可以做得更多。
08:05
Complex, nuanced sentences like this one
161
473622
2948
像這個樣複雜，僅有小小差別的句子
08:08
are now understandable
with deep learning algorithms.
with deep learning algorithms.
162
476570
2824
現在的深度學習演算法能夠理解。
08:11
As you can see here,
163
479394
1303
你可以看到，
08:12
this Stanford-based system
showing the red dot at the top
showing the red dot at the top
164
480697
2768
這以史丹福為基礎的系統
顯示上面的紅點
顯示上面的紅點
08:15
has figured out that this sentence
is expressing negative sentiment.
is expressing negative sentiment.
165
483465
3919
指這句子是在表達負面的情緒。
08:19
Deep learning now in fact
is near human performance
is near human performance
166
487384
3406
深度學習現在已經接近人類的行為
08:22
at understanding what sentences are about
and what it is saying about those things.
and what it is saying about those things.
167
490802
5121
能理解句子是要表達什麼。
08:27
Also, deep learning has
been used to read Chinese,
been used to read Chinese,
168
495923
2728
同時，深度學習也能用以閱讀中文，
08:30
again at about native
Chinese speaker level.
Chinese speaker level.
169
498651
3156
程度相當於以中文為母語的水平。
08:33
This algorithm developed
out of Switzerland
out of Switzerland
170
501807
2168
這演算法發展於瑞士
08:35
by people, none of whom speak
or understand any Chinese.
or understand any Chinese.
171
503975
3356
沒有一個會說中文的團隊。
08:39
As I say, using deep learning
172
507331
2051
像我說的，深度學習
08:41
is about the best system
in the world for this,
in the world for this,
173
509382
2219
是一個最好的系統
對完成這任務來說，
對完成這任務來說，
08:43
even compared to native
human understanding.
human understanding.
174
511601
5117
甚至比人類還要好。
08:48
This is a system that we
put together at my company
put together at my company
175
516718
2964
這個系統是我公司建立的
08:51
which shows putting
all this stuff together.
all this stuff together.
176
519682
2046
要把這些東西都集中在一起。
08:53
These are pictures which
have no text attached,
have no text attached,
177
521728
2461
這是一些沒有文字描述的圖片，
08:56
and as I'm typing in here sentences,
178
524189
2352
我在這裡輸入句子，
08:58
in real time it's understanding
these pictures
these pictures
179
526541
2969
它在同步理解這些照片
09:01
and figuring out what they're about
180
529510
1679
找出它們是有關什麼的照片
09:03
and finding pictures that are similar
to the text that I'm writing.
to the text that I'm writing.
181
531189
3163
也找出跟我句子相關類似的圖片。
09:06
So you can see, it's actually
understanding my sentences
understanding my sentences
182
534352
2756
所以你看，
它真的能理解我的句子。
它真的能理解我的句子。
09:09
and actually understanding these pictures.
183
537108
2224
也完全的理解這些圖片。
09:11
I know that you've seen
something like this on Google,
something like this on Google,
184
539332
2559
你在谷歌上也看過類似的，
09:13
where you can type in things
and it will show you pictures,
and it will show you pictures,
185
541891
2775
你可以輸入文字
而它會顯示圖片，
而它會顯示圖片，
09:16
but actually what it's doing is it's
searching the webpage for the text.
searching the webpage for the text.
186
544666
3424
但事實上，它在尋索網頁上的文字。
09:20
This is very different from actually
understanding the images.
understanding the images.
187
548090
3001
這跟理解圖片有很大的不同。
09:23
This is something that computers
have only been able to do
have only been able to do
188
551091
2752
理解圖片只有電腦可以做
09:25
for the first time in the last few months.
189
553843
3248
電腦在過去幾個月才會做的事。
09:29
So we can see now that computers
can not only see but they can also read,
can not only see but they can also read,
190
557091
4091
電腦不單能看見
也能閱讀，
也能閱讀，
09:33
and, of course, we've shown that they
can understand what they hear.
can understand what they hear.
191
561182
3765
而且我們顯示了電腦能理解所聽到的。
09:36
Perhaps not surprising now that
I'm going to tell you they can write.
I'm going to tell you they can write.
192
564947
3442
或許不意外地，
我要告訴你們電腦也能書寫。
我要告訴你們電腦也能書寫。
09:40
Here is some text that I generated
using a deep learning algorithm yesterday.
using a deep learning algorithm yesterday.
193
568389
4783
這是我昨天用深度學習演算法
所產生的文字。
所產生的文字。
09:45
And here is some text that an algorithm
out of Stanford generated.
out of Stanford generated.
194
573172
3924
這裡有一些非史丹佛演算法
所產生的文字。
所產生的文字。
09:49
Each of these sentences was generated
195
577096
1764
這些句子的產生
09:50
by a deep learning algorithm
to describe each of those pictures.
to describe each of those pictures.
196
578860
4249
是透過深度學習演算法
對圖片進行描述。
對圖片進行描述。
09:55
This algorithm before has never seen
a man in a black shirt playing a guitar.
a man in a black shirt playing a guitar.
197
583109
4472
這演算法是電腦從來沒有看見過
一個穿黑襯衫的男子彈吉他。
一個穿黑襯衫的男子彈吉他。
09:59
It's seen a man before,
it's seen black before,
it's seen black before,
198
587581
2220
電腦見過男人，
看過黑色，
看過黑色，
10:01
it's seen a guitar before,
199
589801
1599
見過吉他，
10:03
but it has independently generated
this novel description of this picture.
this novel description of this picture.
200
591400
4294
它自己便對圖片做出描述。
10:07
We're still not quite at human
performance here, but we're close.
performance here, but we're close.
201
595694
3502
雖然還沒有超越人類，
不過很接近了。
不過很接近了。
10:11
In tests, humans prefer
the computer-generated caption
the computer-generated caption
202
599196
4068
依據統計，人們較喜歡
電腦的圖片說明
電腦的圖片說明
10:15
one out of four times.
203
603264
1527
有四分之一的人會做這樣的選擇。
10:16
Now this system is now only two weeks old,
204
604791
2064
這系統在兩個星期前開發完成，
10:18
so probably within the next year,
205
606855
1846
估計在明年，
10:20
the computer algorithm will be
well past human performance
well past human performance
206
608701
2801
電腦演算法將會超越人類
10:23
at the rate things are going.
207
611502
1862
如果依照這樣的速度發展下的話。
10:25
So computers can also write.
208
613364
3049
到時候電腦也會書寫了。
10:28
So we put all this together and it leads
to very exciting opportunities.
to very exciting opportunities.
209
616413
3475
我們把這些都放在一起，
讓它來引導到一個令人振奮的時機。
讓它來引導到一個令人振奮的時機。
10:31
For example, in medicine,
210
619888
1492
像在藥物方面，
10:33
a team in Boston announced
that they had discovered
that they had discovered
211
621380
2525
一個波士頓的團隊
宣佈他們發現了
宣佈他們發現了
10:35
dozens of new clinically relevant features
212
623905
2949
數十種腫瘤的臨床特徵
10:38
of tumors which help doctors
make a prognosis of a cancer.
make a prognosis of a cancer.
213
626854
4266
幫助醫生預測癌症。
10:44
Very similarly, in Stanford,
214
632220
2296
同樣的，在史丹佛，
10:46
a group there announced that,
looking at tissues under magnification,
looking at tissues under magnification,
215
634516
3663
一個組織宣佈
在放大鏡下觀察組織，
在放大鏡下觀察組織，
10:50
they've developed
a machine learning-based system
a machine learning-based system
216
638179
2381
他們開發
一個以機器學習為基礎的系統
一個以機器學習為基礎的系統
10:52
which in fact is better
than human pathologists
than human pathologists
217
640560
2582
比人類病理學家更有效地
10:55
at predicting survival rates
for cancer sufferers.
for cancer sufferers.
218
643142
4377
預測癌症病患的生存率。
10:59
In both of these cases, not only
were the predictions more accurate,
were the predictions more accurate,
219
647519
3245
這些例子，
不但能更準確地預測，
不但能更準確地預測，
11:02
but they generated new insightful science.
220
650764
2502
而且也能帶來更多科技上的洞見。
11:05
In the radiology case,
221
653276
1505
在放射學的個案中，
11:06
they were new clinical indicators
that humans can understand.
that humans can understand.
222
654781
3095
他們是人類所能理解的新臨床指標。
11:09
In this pathology case,
223
657876
1792
在這病理學個案，
11:11
the computer system actually discovered
that the cells around the cancer
that the cells around the cancer
224
659668
4500
電腦系統發現癌症周圍的細胞
11:16
are as important as
the cancer cells themselves
the cancer cells themselves
225
664168
3340
在診斷的時候
11:19
in making a diagnosis.
226
667508
1752
是跟癌細胞一樣重要。
11:21
This is the opposite of what pathologists
had been taught for decades.
had been taught for decades.
227
669260
5361
這跟病理學家
10 年來的說法相反。
10 年來的說法相反。
11:26
In each of those two cases,
they were systems developed
they were systems developed
228
674621
3292
在這兩個個案，
系統的開發人員
系統的開發人員
11:29
by a combination of medical experts
and machine learning experts,
and machine learning experts,
229
677913
3621
是由醫學專家
和機器學習專家所組成，
和機器學習專家所組成，
11:33
but as of last year,
we're now beyond that too.
we're now beyond that too.
230
681534
2741
但自去年開始，
我們也超越了這些。
我們也超越了這些。
11:36
This is an example of
identifying cancerous areas
identifying cancerous areas
231
684275
3549
這是確認癌症範圍的例子
11:39
of human tissue under a microscope.
232
687824
2530
是在顯微鏡下的人類組織。
11:42
The system being shown here
can identify those areas more accurately,
can identify those areas more accurately,
233
690354
4613
系統顯示可以更準確地確認範圍，
11:46
or about as accurately,
as human pathologists,
as human pathologists,
234
694967
2775
如病理學家般準確，
11:49
but was built entirely with deep learning
using no medical expertise
using no medical expertise
235
697742
3392
不過沒有藥物專家
來建構整套深度學習系統
來建構整套深度學習系統
11:53
by people who have
no background in the field.
no background in the field.
236
701134
2526
系統是由一些
沒有專業背景的人完成。
沒有專業背景的人完成。
11:56
Similarly, here, this neuron segmentation.
237
704730
2555
同樣地，從是細胞分裂。
11:59
We can now segment neurons
about as accurately as humans can,
about as accurately as humans can,
238
707285
3668
我們的系統可以像人類般
精確地分裂神經細胞，
精確地分裂神經細胞，
12:02
but this system was developed
with deep learning
with deep learning
239
710953
2717
不過開發這套深度學習系統
12:05
using people with no previous
background in medicine.
background in medicine.
240
713670
3251
沒有一個人來自醫學背景。
12:08
So myself, as somebody with
no previous background in medicine,
no previous background in medicine,
241
716921
3227
就是我和一些沒有醫學背景的人，
12:12
I seem to be entirely well qualified
to start a new medical company,
to start a new medical company,
242
720148
3727
看來我頗有資格開一家醫藥公司。
12:15
which I did.
243
723875
2146
我確實這麼做了。
12:18
I was kind of terrified of doing it,
244
726021
1740
我是以戒慎恐懼的心情開始做，
12:19
but the theory seemed to suggest
that it ought to be possible
that it ought to be possible
245
727761
2889
不過理論顯示
這是可行的
這是可行的
12:22
to do very useful medicine
using just these data analytic techniques.
using just these data analytic techniques.
246
730650
5492
用這些資料分析技術來
製作有效的藥物。
製作有效的藥物。
12:28
And thankfully, the feedback
has been fantastic,
has been fantastic,
247
736142
2480
感恩的是
回應也挺不錯，
回應也挺不錯，
12:30
not just from the media
but from the medical community,
but from the medical community,
248
738622
2356
這回應不只是來自媒體，
而且還有醫藥社群，
而且還有醫藥社群，
12:32
who have been very supportive.
249
740978
2344
他們都很支持。
12:35
The theory is that we can take
the middle part of the medical process
the middle part of the medical process
250
743322
4149
理論上我們能在醫務過程中
12:39
and turn that into data analysis
as much as possible,
as much as possible,
251
747471
2893
盡量轉換成資料分析，
12:42
leaving doctors to do
what they're best at.
what they're best at.
252
750364
3065
讓醫生去做他們擅長的。
12:45
I want to give you an example.
253
753429
1602
我舉一個例子。
12:47
It now takes us about 15 minutes
to generate a new medical diagnostic test
to generate a new medical diagnostic test
254
755031
4944
我們現在花 15 分鐘
來創造一項新的醫學診斷測試
來創造一項新的醫學診斷測試
12:51
and I'll show you that in real time now,
255
759975
1954
我會讓你同步看到過程，
12:53
but I've compressed it down to
three minutes by cutting some pieces out.
three minutes by cutting some pieces out.
256
761929
3487
不過我已刪除部分資料
壓縮成三分鐘。
壓縮成三分鐘。
12:57
Rather than showing you
creating a medical diagnostic test,
creating a medical diagnostic test,
257
765416
3061
我不會向你們展示
創造出來的醫學診斷測試，
創造出來的醫學診斷測試，
13:00
I'm going to show you
a diagnostic test of car images,
a diagnostic test of car images,
258
768477
3369
我要向你們展示
一項汽車圖片的診斷測試，
一項汽車圖片的診斷測試，
13:03
because that's something
we can all understand.
we can all understand.
259
771846
2222
因為這個我們都能理解。
13:06
So here we're starting with
about 1.5 million car images,
about 1.5 million car images,
260
774068
3201
我們從 150 萬張
的汽車圖片開始，
的汽車圖片開始，
13:09
and I want to create something
that can split them into the angle
that can split them into the angle
261
777269
3206
我希望創造一些東西
把圖片分類
把圖片分類
13:12
of the photo that's being taken.
262
780475
2223
而且依圖片拍攝的角度來分類。
13:14
So these images are entirely unlabeled,
so I have to start from scratch.
so I have to start from scratch.
263
782698
3888
這些圖片完全沒有標題，
我必需從零開始。
我必需從零開始。
13:18
With our deep learning algorithm,
264
786586
1865
深度學習演算法，
13:20
it can automatically identify
areas of structure in these images.
areas of structure in these images.
265
788451
3707
它能自動確認
這些圖片的結構。
這些圖片的結構。
13:24
So the nice thing is that the human
and the computer can now work together.
and the computer can now work together.
266
792158
3620
美好的是
人和電腦可以合作
人和電腦可以合作
13:27
So the human, as you can see here,
267
795778
2178
看看這裡，這個人，
13:29
is telling the computer
about areas of interest
about areas of interest
268
797956
2675
正在告訴電腦
關於感興趣的範圍
關於感興趣的範圍
13:32
which it wants the computer then
to try and use to improve its algorithm.
to try and use to improve its algorithm.
269
800631
4650
而電腦會嘗試用它
來改善電腦的演算法。
來改善電腦的演算法。
13:37
Now, these deep learning systems actually
are in 16,000-dimensional space,
are in 16,000-dimensional space,
270
805281
4296
這些深度學習系統
有 16,000 個立體空間，
有 16,000 個立體空間，
13:41
so you can see here the computer
rotating this through that space,
rotating this through that space,
271
809577
3432
你可以看見電腦
讓他們在這空間旋轉，
讓他們在這空間旋轉，
13:45
trying to find new areas of structure.
272
813009
1992
嘗試找出新的區域結構。
13:47
And when it does so successfully,
273
815001
1781
當它成功時，
13:48
the human who is driving it can then
point out the areas that are interesting.
point out the areas that are interesting.
274
816782
4004
在開車的人能夠
指出有興趣的地方。
指出有興趣的地方。
13:52
So here, the computer has
successfully found areas,
successfully found areas,
275
820786
2422
這裡，電腦成功的找到了那地區，
13:55
for example, angles.
276
823208
2562
再舉例，角度，
13:57
So as we go through this process,
277
825770
1606
通過這個過程，
13:59
we're gradually telling
the computer more and more
the computer more and more
278
827376
2340
我們漸漸地告訴電腦更多
14:01
about the kinds of structures
we're looking for.
we're looking for.
279
829716
2428
關於我們在找的結構類型。
14:04
You can imagine in a diagnostic test
280
832144
1772
你可以想像一個診斷測試
14:05
this would be a pathologist identifying
areas of pathosis, for example,
areas of pathosis, for example,
281
833916
3350
像是一個病理學家辨認
病症的範圍，
病症的範圍，
14:09
or a radiologist indicating
potentially troublesome nodules.
potentially troublesome nodules.
282
837266
5026
或是放射治療師界定
潛在的腫瘤。
潛在的腫瘤。
14:14
And sometimes it can be
difficult for the algorithm.
difficult for the algorithm.
283
842292
2559
有些時候對演算法來說
是有些困難。
是有些困難。
14:16
In this case, it got kind of confused.
284
844851
1964
在我們這個例子，它會出現混亂。
14:18
The fronts and the backs
of the cars are all mixed up.
of the cars are all mixed up.
285
846815
2550
汽車的正面和背面
都混淆不清了。
都混淆不清了。
14:21
So here we have to be a bit more careful,
286
849365
2072
我們需要更小心，
14:23
manually selecting these fronts
as opposed to the backs,
as opposed to the backs,
287
851437
3232
手動選出正面
跟背面有相反效果的文字，
跟背面有相反效果的文字，
14:26
then telling the computer
that this is a type of group
that this is a type of group
288
854669
5506
然後告知電腦
這是一種
這是一種
14:32
that we're interested in.
289
860175
1348
我們有興趣的一類。
14:33
So we do that for a while,
we skip over a little bit,
we skip over a little bit,
290
861523
2677
這要花了一些時間來做，
所以我們跳過，
所以我們跳過，
14:36
and then we train the
machine learning algorithm
machine learning algorithm
291
864200
2246
然後我們訓練
機器學習演算法
機器學習演算法
14:38
based on these couple of hundred things,
292
866446
1974
以好幾百張圖片去訓練它，
14:40
and we hope that it's gotten a lot better.
293
868420
2025
我們希望它會做得更好。
14:42
You can see, it's now started to fade
some of these pictures out,
some of these pictures out,
294
870445
3073
你可以看見，它開始
刪除一些圖片，
刪除一些圖片，
14:45
showing us that it already is recognizing
how to understand some of these itself.
how to understand some of these itself.
295
873518
4708
顯示它已經知道
可以自己理解這些圖片。
可以自己理解這些圖片。
14:50
We can then use this concept
of similar images,
of similar images,
296
878226
2902
我們運用相似圖片的概念，
14:53
and using similar images, you can now see,
297
881128
2094
用類似的圖片，你可以看到，
14:55
the computer at this point is able to
entirely find just the fronts of cars.
entirely find just the fronts of cars.
298
883222
4019
電腦現在可以
完全找到正面的汽車。
完全找到正面的汽車。
14:59
So at this point, the human
can tell the computer,
can tell the computer,
299
887241
2948
這時，
人類可以告訴電腦，
人類可以告訴電腦，
15:02
okay, yes, you've done
a good job of that.
a good job of that.
300
890189
2293
對，你做的很好。
15:05
Sometimes, of course, even at this point
301
893652
2185
當然，有些時候，即使在這個階段
15:07
it's still difficult
to separate out groups.
to separate out groups.
302
895837
3674
分組仍然是困難的。
15:11
In this case, even after we let the
computer try to rotate this for a while,
computer try to rotate this for a while,
303
899511
3884
在這情況，儘管我們讓
電腦嘗試旋轉圖片一陣子，
電腦嘗試旋轉圖片一陣子，
15:15
we still find that the left sides
and the right sides pictures
and the right sides pictures
304
903399
3345
我們還是發現左邊
和右邊的圖片
和右邊的圖片
15:18
are all mixed up together.
305
906744
1478
是混淆在一起的。
15:20
So we can again give
the computer some hints,
the computer some hints,
306
908222
2140
於是我們再次
給電腦一些提示，
給電腦一些提示，
15:22
and we say, okay, try and find
a projection that separates out
a projection that separates out
307
910362
2976
像是嘗試去發現一個計畫可以
15:25
the left sides and the right sides
as much as possible
as much as possible
308
913338
2607
儘量區分出左邊和右邊的圖片
15:27
using this deep learning algorithm.
309
915945
2122
是透過使用深度學習演算法。
15:30
And giving it that hint --
ah, okay, it's been successful.
ah, okay, it's been successful.
310
918067
2942
給予提示後，
好，它已經完成了。
好，它已經完成了。
15:33
It's managed to find a way
of thinking about these objects
of thinking about these objects
311
921009
2882
它找到一個方法
想像這些目標
想像這些目標
15:35
that's separated out these together.
312
923891
2380
來分別這些分類。
15:38
So you get the idea here.
313
926271
2438
你現在知道了。
15:40
This is a case not where the human
is being replaced by a computer,
is being replaced by a computer,
314
928709
8197
這並不是電腦取代人類，
15:48
but where they're working together.
315
936906
2640
而是兩者一起合作。
15:51
What we're doing here is we're replacing
something that used to take a team
something that used to take a team
316
939546
3550
我們在做的事情是
在過去需要
在過去需要
15:55
of five or six people about seven years
317
943096
2002
5 或 6 個人
花 7 年時間完成的事情
花 7 年時間完成的事情
15:57
and replacing it with something
that takes 15 minutes
that takes 15 minutes
318
945098
2605
現在只需一個人
15:59
for one person acting alone.
319
947703
2505
15 分鐘來完成。
16:02
So this process takes about
four or five iterations.
four or five iterations.
320
950208
3950
這個過程需要重覆 4 或 5 次。
16:06
You can see we now have 62 percent
321
954158
1859
你現在可以看到
16:08
of our 1.5 million images
classified correctly.
classified correctly.
322
956017
2959
我們在 150 萬的圖片中
有 62％ 是正確分類。
有 62％ 是正確分類。
16:10
And at this point, we
can start to quite quickly
can start to quite quickly
323
958976
2472
現在，可見我們可以迅速地
16:13
grab whole big sections,
324
961448
1297
掌握整個大部分資料，
16:14
check through them to make sure
that there's no mistakes.
that there's no mistakes.
325
962745
2919
再檢查以確定沒有錯誤。
16:17
Where there are mistakes, we can
let the computer know about them.
let the computer know about them.
326
965664
3952
有錯誤，我們可以
讓電腦知道錯誤的地方。
讓電腦知道錯誤的地方。
16:21
And using this kind of process
for each of the different groups,
for each of the different groups,
327
969616
3045
每一個不同的分類
我們都使用這種程序來做，
我們都使用這種程序來做，
16:24
we are now up to
an 80 percent success rate
an 80 percent success rate
328
972661
2487
我們現在
在分辨 150 萬張的圖片時
在分辨 150 萬張的圖片時
16:27
in classifying the 1.5 million images.
329
975148
2415
有超過 80％ 的成功率，
16:29
And at this point, it's just a case
330
977563
2078
現在，在這個案例
16:31
of finding the small number
that aren't classified correctly,
that aren't classified correctly,
331
979641
3579
找到少數幾個不正確的分類，
16:35
and trying to understand why.
332
983220
2888
讓電腦了解原因。
16:38
And using that approach,
333
986108
1743
用這種方法，
16:39
by 15 minutes we get
to 97 percent classification rates.
to 97 percent classification rates.
334
987851
4121
15 分鐘就有 97％ 的分辨率。
16:43
So this kind of technique
could allow us to fix a major problem,
could allow us to fix a major problem,
335
991972
4600
這種技術可以幫助
解決一個重要的問題，
解決一個重要的問題，
16:48
which is that there's a lack
of medical expertise in the world.
of medical expertise in the world.
336
996578
3036
醫療專家不足的問題。
16:51
The World Economic Forum says
that there's between a 10x and a 20x
that there's between a 10x and a 20x
337
999614
3489
世界經濟論壇表示
16:55
shortage of physicians
in the developing world,
in the developing world,
338
1003103
2624
在發展中國家，內科醫生
有 10 倍到 20 倍的短缺。
有 10 倍到 20 倍的短缺。
16:57
and it would take about 300 years
339
1005727
2113
這要三百年的時間
16:59
to train enough people
to fix that problem.
to fix that problem.
340
1007840
2894
才能訓練足夠的人
來處理這個問題。
來處理這個問題。
17:02
So imagine if we can help
enhance their efficiency
enhance their efficiency
341
1010734
2885
想像一下，
我們是否可以幫助提高效率
我們是否可以幫助提高效率
17:05
using these deep learning approaches?
342
1013619
2839
是使用深度學習這個方法來提升？
17:08
So I'm very excited
about the opportunities.
about the opportunities.
343
1016458
2232
我對這個機會感到很興奮。
17:10
I'm also concerned about the problems.
344
1018690
2589
我也關注這些問題。
17:13
The problem here is that
every area in blue on this map
every area in blue on this map
345
1021279
3124
問題是在這地圖上每個藍色的地方
17:16
is somewhere where services
are over 80 percent of employment.
are over 80 percent of employment.
346
1024403
3769
那裡都有 80％ 的服務人員。
17:20
What are services?
347
1028172
1787
什麼是服務？
17:21
These are services.
348
1029959
1514
這些就是服務。
17:23
These are also the exact things that
computers have just learned how to do.
computers have just learned how to do.
349
1031473
4154
電腦剛學會如何去做是確實的事。
17:27
So 80 percent of the world's employment
in the developed world
in the developed world
350
1035627
3804
發展中國家 80％ 的僱員工作
17:31
is stuff that computers
have just learned how to do.
have just learned how to do.
351
1039431
2532
電腦已開始學習如何做。
17:33
What does that mean?
352
1041963
1440
這意味什麼？
17:35
Well, it'll be fine.
They'll be replaced by other jobs.
They'll be replaced by other jobs.
353
1043403
2583
那可好。
他們將會被其他的職業取代。
他們將會被其他的職業取代。
17:37
For example, there will be
more jobs for data scientists.
more jobs for data scientists.
354
1045986
2707
舉例：需要更多科學家來工作。
17:40
Well, not really.
355
1048693
817
不過，這不完全正確。
17:41
It doesn't take data scientists
very long to build these things.
very long to build these things.
356
1049510
3118
數據科學家
不需要花很久的時間去做這些事情。
不需要花很久的時間去做這些事情。
17:44
For example, these four algorithms
were all built by the same guy.
were all built by the same guy.
357
1052628
3252
例如，這四個演算法是同一個人設計的。
17:47
So if you think, oh,
it's all happened before,
it's all happened before,
358
1055880
2438
若你認為這些
以前都發生過，
以前都發生過，
17:50
we've seen the results in the past
of when new things come along
of when new things come along
359
1058318
3808
過去我們看過
新事物出現的結果
新事物出現的結果
17:54
and they get replaced by new jobs,
360
1062126
2252
他們被新的職務所取替，
17:56
what are these new jobs going to be?
361
1064378
2116
那些新的職業會是什麼呢？
17:58
It's very hard for us to estimate this,
362
1066494
1871
我們很難去判斷，
18:00
because human performance
grows at this gradual rate,
grows at this gradual rate,
363
1068365
2739
因為人類的能力
以這個速度逐漸成長，
以這個速度逐漸成長，
18:03
but we now have a system, deep learning,
364
1071104
2562
我們現在有了深度學習系統，
18:05
that we know actually grows
in capability exponentially.
in capability exponentially.
365
1073666
3227
我們知道
以指數的方式增長。
以指數的方式增長。
18:08
And we're here.
366
1076893
1605
我們在這裡。
18:10
So currently, we see the things around us
367
1078498
2061
最近，我們看周圍的事物
18:12
and we say, "Oh, computers
are still pretty dumb." Right?
are still pretty dumb." Right?
368
1080559
2676
會說：電腦還是很笨，不是嗎？
18:15
But in five years' time,
computers will be off this chart.
computers will be off this chart.
369
1083235
3429
但是在五年內，
電腦將會超越這張圖表。
電腦將會超越這張圖表。
18:18
So we need to be starting to think
about this capability right now.
about this capability right now.
370
1086664
3865
我們需要開始思考這個能力。
18:22
We have seen this once before, of course.
371
1090529
2050
當然，我們曾經看過這個。
18:24
In the Industrial Revolution,
372
1092579
1387
在工業革命時期，
18:25
we saw a step change
in capability thanks to engines.
in capability thanks to engines.
373
1093966
2851
發動機讓生產力往前跨一大步。
18:29
The thing is, though,
that after a while, things flattened out.
that after a while, things flattened out.
374
1097667
3138
雖然，一段時間之後，
事情轉為平靜。
事情轉為平靜。
18:32
There was social disruption,
375
1100805
1702
那時社會混亂，
18:34
but once engines were used
to generate power in all the situations,
to generate power in all the situations,
376
1102507
3439
發動機被普遍使用
產生動力，
產生動力，
18:37
things really settled down.
377
1105946
2354
事情就能真正得到解決。
18:40
The Machine Learning Revolution
378
1108300
1473
機器學習革命
18:41
is going to be very different
from the Industrial Revolution,
from the Industrial Revolution,
379
1109773
2909
與工業革命大不相同，
18:44
because the Machine Learning Revolution,
it never settles down.
it never settles down.
380
1112682
2950
因為機器學習革命，
永遠不會停下來。
永遠不會停下來。
18:47
The better computers get
at intellectual activities,
at intellectual activities,
381
1115632
2982
電腦更具智力活動，
18:50
the more they can build better computers
to be better at intellectual capabilities,
to be better at intellectual capabilities,
382
1118614
4248
他們能製造更好的電腦
去運作更好的智能活動，
去運作更好的智能活動，
18:54
so this is going to be a kind of change
383
1122862
1908
這是一種改變
18:56
that the world has actually
never experienced before,
never experienced before,
384
1124770
2478
從未經歷過的改變，
18:59
so your previous understanding
of what's possible is different.
of what's possible is different.
385
1127248
3306
你之前的理解的可能性是不同的。
19:02
This is already impacting us.
386
1130974
1780
這已經影響我們。
19:04
In the last 25 years,
as capital productivity has increased,
as capital productivity has increased,
387
1132754
3630
過去 25 年，
資本生產力一直在增長，
資本生產力一直在增長，
19:08
labor productivity has been flat,
in fact even a little bit down.
in fact even a little bit down.
388
1136400
4188
勞動生產力已經放緩，
事實上已有一點點下降。
事實上已有一點點下降。
19:13
So I want us to start
having this discussion now.
having this discussion now.
389
1141408
2741
我想我們開始討論這個議題。
19:16
I know that when I often tell people
about this situation,
about this situation,
390
1144149
3027
我知道當我告訴別人這種情況時，
19:19
people can be quite dismissive.
391
1147176
1490
人們可以不以為然。
19:20
Well, computers can't really think,
392
1148666
1673
電腦不會思考，
19:22
they don't emote,
they don't understand poetry,
they don't understand poetry,
393
1150339
3028
它們沒有感情，
也不了解詩，
也不了解詩，
19:25
we don't really understand how they work.
394
1153367
2521
我們不真正理解它們怎麼運作。
19:27
So what?
395
1155888
1486
可是，哪又如何？
19:29
Computers right now can do the things
396
1157374
1804
電腦現在可以作
19:31
that humans spend most
of their time being paid to do,
of their time being paid to do,
397
1159178
2719
人們花大部分時間
得到報酬所做的事情，
得到報酬所做的事情，
19:33
so now's the time to start thinking
398
1161897
1731
所以我們該是思考的時候
19:35
about how we're going to adjust our
social structures and economic structures
social structures and economic structures
399
1163628
4387
我們如何調整我們的社會和經濟結構
19:40
to be aware of this new reality.
400
1168015
1840
請關注這些新的改變。
19:41
Thank you.
401
1169855
1533
謝謝
19:43
(Applause)
402
1171388
802
（掌聲）
ABOUT THE SPEAKER
Jeremy Howard - Data scientistJeremy Howard imagines how advanced machine learning can improve our lives.
Why you should listen
Jeremy Howard is the CEO of Enlitic, an advanced machine learning company in San Francisco. Previously, he was the president and chief scientist at Kaggle, a community and competition platform of over 200,000 data scientists. Howard is a faculty member at Singularity University, where he teaches data science. He is also a Young Global Leader with the World Economic Forum, and spoke at the World Economic Forum Annual Meeting 2014 on "Jobs for the Machines."
Howard advised Khosla Ventures as their Data Strategist, identifying opportunities for investing in data-driven startups and mentoring portfolio companies to build data-driven businesses. He was the founding CEO of two successful Australian startups, FastMail and Optimal Decisions Group.
Jeremy Howard | Speaker | TED.com