TEDSummit
Kevin Kelly: How AI can bring on a second Industrial Revolution
凱文·凱利: 人工智慧將如何引領第二次工業革命？
Filmed:
Readability: 3.8
1,739,624 views
數位觀察家凱文·凱利說：「雨水流入山谷的實際路徑是不可預測的，但它的大方向是必然的。」，而科技在很大程度上也是如此，它是由令人驚奇但又具有某種必然性的趨勢所推動。他說，未來二十年，我們讓事物變得越來越聰明的傾向，將會對我們身邊的每件事情產生深遠的影響。凱利向我們揭露了 AI 大潮中我們需要了解的三個趨勢，了解這三個趨勢可以讓我們能更好的擁抱 AI 並駕馭它的發展。凱利說：「二十年後人人都要用的 AI 產品還沒有被發明出來，意思就是，你還為時未晚」。
翻譯：余易帆
Kevin Kelly - Digital visionary
There may be no one better to contemplate the meaning of cultural change than Kevin Kelly, whose life story reads like a treatise on the value and impacts of technology. Full bio
There may be no one better to contemplate the meaning of cultural change than Kevin Kelly, whose life story reads like a treatise on the value and impacts of technology. Full bio
Double-click the English transcript below to play the video.
00:14
I'm going to talk a little bit
about where technology's going.
about where technology's going.
0
2966
3817
我準備來談談未來科技的走勢。
00:19
And often technology comes to us,
1
7509
2671
每當新的科技發明，
00:22
we're surprised by what it brings.
2
10566
1865
我們總是驚嘆它所帶給我們的驚喜。
00:24
But there's actually
a large aspect of technology
a large aspect of technology
3
12455
3683
但是實際上更大程度的是對科技的形勢
00:28
that's much more predictable,
4
16162
1802
這是很容易預測的，
00:29
and that's because technological systems
of all sorts have leanings,
of all sorts have leanings,
5
17988
4088
因為所有的科技系統
都有一定的脈絡可循，
都有一定的脈絡可循，
00:34
they have urgencies,
6
22100
1175
它們有迫切性，
00:35
they have tendencies.
7
23299
1561
有一定的趨勢，
00:36
And those tendencies are derived
from the very nature of the physics,
from the very nature of the physics,
8
24884
4932
而這些趨勢都是來自於
電線、開關、電子
電線、開關、電子
00:41
chemistry of wires
and switches and electrons,
and switches and electrons,
9
29840
3150
的物理本質與化學原理，
00:45
and they will make reoccurring
patterns again and again.
patterns again and again.
10
33659
3602
而這些模式會周而復始地發生。
00:49
And so those patterns produce
these tendencies, these leanings.
these tendencies, these leanings.
11
37745
4874
所以是這些模式造就了
科技的趨勢及走向。
科技的趨勢及走向。
00:54
You can almost think of it
as sort of like gravity.
as sort of like gravity.
12
42643
2831
你幾乎可以把它看做是一種「萬有引力」。
00:57
Imagine raindrops falling into a valley.
13
45498
2319
想像一下，就像雨滴落到山谷中，
00:59
The actual path of a raindrop
as it goes down the valley
as it goes down the valley
14
47841
3088
雨滴流到山谷中的實際路徑
01:02
is unpredictable.
15
50953
1169
是無法預測的。
01:04
We cannot see where it's going,
16
52651
1518
我們看不到雨滴會怎麼流，
01:06
but the general direction
is very inevitable:
is very inevitable:
17
54193
2277
但大致上的方向是一定的：
01:08
it's downward.
18
56494
1234
這個方向是向下的。
01:10
And so these baked-in
tendencies and urgencies
tendencies and urgencies
19
58377
4572
而這些深根在科技系統裡的
01:14
in technological systems
20
62973
1476
趨勢及迫切性，
01:17
give us a sense of where things
are going at the large form.
are going at the large form.
21
65051
3609
告訴了我們科技的大方向。
01:21
So in a large sense,
22
69149
1401
具體說，
01:22
I would say that telephones
were inevitable,
were inevitable,
23
70574
3361
我認為電話的發明是必然的，
01:27
but the iPhone was not.
24
75005
1342
但 iPhone 就不是了。
01:29
The Internet was inevitable,
25
77094
1478
網際網路的發明是必然的，
01:31
but Twitter was not.
26
79274
1286
但推特就不是了。
01:33
So we have many ongoing
tendencies right now,
tendencies right now,
27
81036
3928
所以我們現在有很多趨勢正在進行，
01:36
and I think one of the chief among them
28
84988
2720
而我認為它們其中一個
主要的趨勢就是，
主要的趨勢就是，
01:39
is this tendency to make things
smarter and smarter.
smarter and smarter.
29
87732
3722
東西越來越聰明了。
01:44
I call it cognifying -- cognification --
30
92041
2212
我稱這個過程為
「認知化 」——認知——
「認知化 」——認知——
01:46
also known as artificial
intelligence, or AI.
intelligence, or AI.
31
94783
2782
也就是大家知道的
人工智慧，或者「AI」
人工智慧，或者「AI」
01:50
And I think that's going to be one
of the most influential developments
of the most influential developments
32
98025
3746
我認為未來20年，
AI 將成為我們社會其中一個
最有影響力的發展、趨勢及驅動力。
最有影響力的發展、趨勢及驅動力。
01:53
and trends and directions and drives
in our society in the next 20 years.
in our society in the next 20 years.
33
101795
5575
02:00
So, of course, it's already here.
34
108021
1985
當然，AI 已經出現了，
02:02
We already have AI,
35
110030
2204
我們已經有 AI 了，
02:04
and often it works in the background,
36
112258
2398
而且它經常在幕後幫助我們，
02:06
in the back offices of hospitals,
37
114680
1586
它出現在醫院後面的辦公室，
02:08
where it's used to diagnose X-rays
better than a human doctor.
better than a human doctor.
38
116290
4686
用 AI 來診斷 X 光片的能力
比人類醫生還精準。
比人類醫生還精準。
02:13
It's in legal offices,
39
121000
1726
它會出現在律師事務所，
02:14
where it's used to go
through legal evidence
through legal evidence
40
122750
2368
用 AI 審閱法律文件，
02:17
better than a human paralawyer.
41
125142
1855
速度比人類的律師還要快。
02:19
It's used to fly the plane
that you came here with.
that you came here with.
42
127506
3656
各位今天坐的飛機也有人工智慧，
02:24
Human pilots only flew it
seven to eight minutes,
seven to eight minutes,
43
132165
2381
人工駕駛只有 7~8 分鐘，
02:26
the rest of the time the AI was driving.
44
134570
1953
剩下的都是 AI 在駕駛
02:28
And of course, in Netflix and Amazon,
45
136547
2173
當然， Netflix 和 Amazon也有，
02:30
it's in the background,
making those recommendations.
making those recommendations.
46
138744
2530
它在幕後給出做出推薦和建議。
02:33
That's what we have today.
47
141298
1261
這是我們目前已經實現的。
02:34
And we have an example, of course,
in a more front-facing aspect of it,
in a more front-facing aspect of it,
48
142583
4801
當然，還有一個更先進的案例，
02:39
with the win of the AlphaGo, who beat
the world's greatest Go champion.
the world's greatest Go champion.
49
147408
6629
就是打敗世界圍棋冠軍的 AlphaGo。
02:46
But it's more than that.
50
154478
4053
但人工智慧不僅於此。
02:50
If you play a video game,
you're playing against an AI.
you're playing against an AI.
51
158555
2642
如果你在玩電動，你對抗的是 AI，
02:53
But recently, Google taught their AI
52
161221
4538
但最近，Google開始教他們的 AI
02:57
to actually learn how to play video games.
53
165783
2412
實際意義上的學習如何打電動。
03:00
Again, teaching video games
was already done,
was already done,
54
168686
2709
重申一下，教 AI 「打電動」是一種層次，
03:03
but learning how to play
a video game is another step.
a video game is another step.
55
171419
3897
但教 AI 「學習如何打電動」又是另一種層次。
03:07
That's artificial smartness.
56
175340
1678
這是人造的智能產品。
03:10
What we're doing is taking
this artificial smartness
this artificial smartness
57
178571
4522
而我們正在做的就是將
這種人造的智能產品
這種人造的智能產品
03:15
and we're making it smarter and smarter.
58
183117
2423
變得越來越聰明。
03:18
There are three aspects
to this general trend
to this general trend
59
186710
3895
這個趨勢大致上有三個面向，
03:22
that I think are underappreciated;
60
190629
1689
我認為尚未被充分認知；
03:24
I think we would understand
AI a lot better
AI a lot better
61
192342
2277
我想如果搞懂這三個面向，
03:26
if we understood these three things.
62
194643
2301
我們對 AI 的了解，會更深入一些。
03:28
I think these things also would
help us embrace AI,
help us embrace AI,
63
196968
3283
我認為了解這些事，
也可以幫助我們擁抱 AI，
也可以幫助我們擁抱 AI，
03:32
because it's only by embracing it
that we actually can steer it.
that we actually can steer it.
64
200275
3008
唯有擁抱 AI 才能駕馭 AI。
03:35
We can actually steer the specifics
by embracing the larger trend.
by embracing the larger trend.
65
203887
3157
藉由懷抱更大趨勢來駕馭細節。
03:39
So let me talk about
those three different aspects.
those three different aspects.
66
207467
2979
所以容我來談談 AI 的三個不同面向。
03:42
The first one is: our own intelligence
has a very poor understanding
has a very poor understanding
67
210470
3673
第一：以人類目前對智慧的了解，
我們對智慧的認知仍相當貧乏。
03:46
of what intelligence is.
68
214167
1490
03:48
We tend to think of intelligence
as a single dimension,
as a single dimension,
69
216110
3653
我們似乎把智能看的太單一面向了，
03:51
that it's kind of like a note
that gets louder and louder.
that gets louder and louder.
70
219787
2750
它有點像是個音符，會越來越大聲。
03:54
It starts like with IQ measurement.
71
222561
2607
剛開始像個 IQ 測量儀。
03:57
It starts with maybe a simple
low IQ in a rat or mouse,
low IQ in a rat or mouse,
72
225192
4092
一開始的智商也許跟老鼠一樣低，
04:01
and maybe there's more in a chimpanzee,
73
229308
2134
有的像猩猩，稍微多一點，
04:03
and then maybe there's more
in a stupid person,
in a stupid person,
74
231887
2191
之後開始像個低智商的人類，
04:06
and then maybe an average
person like myself,
person like myself,
75
234102
2096
然後進化到像我一樣的普通人，
04:08
and then maybe a genius.
76
236222
1290
然後變成一個天才。
04:09
And this single IQ intelligence
is getting greater and greater.
is getting greater and greater.
77
237536
4433
IQ 智能分數越來越高，
04:14
That's completely wrong.
78
242516
1151
這種看法完全是錯誤的。
04:15
That's not what intelligence is --
not what human intelligence is, anyway.
not what human intelligence is, anyway.
79
243691
3608
這不是智慧該有的樣子——
人類的智慧不僅於此。
人類的智慧不僅於此。
04:19
It's much more like a symphony
of different notes,
of different notes,
80
247673
4506
它像是一首交響樂，
或者由不同的音符組成，
或者由不同的音符組成，
04:24
and each of these notes is played
on a different instrument of cognition.
on a different instrument of cognition.
81
252203
3609
而每一個音符，
由不同的認知樂器所伴奏。
由不同的認知樂器所伴奏。
04:27
There are many types
of intelligences in our own minds.
of intelligences in our own minds.
82
255836
3701
人類腦中有很多不同種類的智慧，
04:31
We have deductive reasoning,
83
259561
3048
我們有演繹推理的能力，
04:34
we have emotional intelligence,
84
262633
2221
我們有情感的智慧，
04:36
we have spatial intelligence;
85
264878
1393
我們有空間概念的智慧，
04:38
we have maybe 100 different types
that are all grouped together,
that are all grouped together,
86
266295
4021
我們可能有100多種
不同的智能聚合在一起，
不同的智能聚合在一起，
04:42
and they vary in different strengths
with different people.
with different people.
87
270340
3905
而且每個人各有各的強項。
04:46
And of course, if we go to animals,
they also have another basket --
they also have another basket --
88
274269
4526
當然，以動物而言，
牠們可能是另一套體系——
牠們可能是另一套體系——
04:50
another symphony of different
kinds of intelligences,
kinds of intelligences,
89
278819
2541
另一種不同的智能交響樂，
04:53
and sometimes those same instruments
are the same that we have.
are the same that we have.
90
281384
3566
有時候跟我們人類的一樣。
04:56
They can think in the same way,
but they may have a different arrangement,
but they may have a different arrangement,
91
284974
3561
牠們可能思考方式相同
但著重點不同，
但著重點不同，
05:00
and maybe they're higher
in some cases than humans,
in some cases than humans,
92
288559
2467
也許在某些方面超過人類，
05:03
like long-term memory in a squirrel
is actually phenomenal,
is actually phenomenal,
93
291050
2837
像是松鼠的長期記憶力，相當出色，
05:05
so it can remember
where it buried its nuts.
where it buried its nuts.
94
293911
2287
能清楚記得堅果的埋藏之處。
05:08
But in other cases they may be lower.
95
296222
1987
但其它方面，牠們也許就比較弱了。
05:10
When we go to make machines,
96
298233
2730
當我們要製造機器時，
05:12
we're going to engineer
them in the same way,
them in the same way,
97
300987
2196
我們會用同樣的方式來設計機器，
05:15
where we'll make some of those types
of smartness much greater than ours,
of smartness much greater than ours,
98
303207
5010
有些智慧型裝置做得比人類聰明得多，
05:20
and many of them won't be
anywhere near ours,
anywhere near ours,
99
308241
2571
但其它方面則遠遠不如我們，
05:22
because they're not needed.
100
310836
1544
因為根本不需要。
05:24
So we're going to take these things,
101
312404
2203
我們會將這些產品
05:26
these artificial clusters,
102
314631
2081
這些人工產品，
05:28
and we'll be adding more varieties
of artificial cognition to our AIs.
of artificial cognition to our AIs.
103
316736
5362
在不同的 AI 上，
裝置不同的人工認知功能，
裝置不同的人工認知功能，
05:34
We're going to make them
very, very specific.
very, very specific.
104
322507
4071
我們可以把它們的特定功能
做得相當、相當出色。
做得相當、相當出色。
05:38
So your calculator is smarter
than you are in arithmetic already;
than you are in arithmetic already;
105
326602
6542
所以你的計算機在計算方面
比你聰明許多；
比你聰明許多；
05:45
your GPS is smarter
than you are in spatial navigation;
than you are in spatial navigation;
106
333168
3697
你的 GPS 在空間導航上比你聰明得多；
05:49
Google, Bing, are smarter
than you are in long-term memory.
than you are in long-term memory.
107
337337
4258
Googl, Bing 的長期記憶比你強。
05:54
And we're going to take, again,
these kinds of different types of thinking
these kinds of different types of thinking
108
342339
4530
然後我們再把這些不同種類的智能，
05:58
and we'll put them into, like, a car.
109
346893
1933
放在，像是，車子裡。
06:00
The reason why we want to put them
in a car so the car drives,
in a car so the car drives,
110
348850
3057
我們之所以這麼做的原因，
06:03
is because it's not driving like a human.
111
351931
2302
是因為它們不會像人類那樣開車，
06:06
It's not thinking like us.
112
354257
1396
它們不會像人類那樣思考。
06:07
That's the whole feature of it.
113
355677
1920
這是它唯一的特色。
06:09
It's not being distracted,
114
357621
1535
它不會分心，
06:11
it's not worrying about whether
it left the stove on,
it left the stove on,
115
359180
2754
它不用擔心瓦斯爐沒關，
06:13
or whether it should have
majored in finance.
majored in finance.
116
361958
2138
它不用考慮要不要主修財經。
06:16
It's just driving.
117
364120
1153
它只會開車。
06:17
(Laughter)
118
365297
1142
（笑聲）
06:18
Just driving, OK?
119
366463
1841
只會開車，好嗎？
06:20
And we actually might even
come to advertise these
come to advertise these
120
368328
2937
而我們最終可能會拿它來廣告
06:23
as "consciousness-free."
121
371289
1545
「無意識」。
06:24
They're without consciousness,
122
372858
1774
它們沒有意識，
06:26
they're not concerned about those things,
123
374656
2104
它們不會關心這些瑣事，
06:28
they're not distracted.
124
376784
1156
它們不會分心。
06:29
So in general, what we're trying to do
125
377964
2966
所以，我們應該盡我們所能
06:32
is make as many different
types of thinking as we can.
types of thinking as we can.
126
380954
4500
去嘗試做出一些不同的想法。
06:37
We're going to populate the space
127
385804
2083
我們將會天馬行空，
06:39
of all the different possible types,
or species, of thinking.
or species, of thinking.
128
387911
4159
去嘗試所有可能的思考方式。
06:44
And there actually may be some problems
129
392094
2068
也許還有一些
06:46
that are so difficult
in business and science
in business and science
130
394186
2800
相當不好解決的商業及科學問題，
06:49
that our own type of human thinking
may not be able to solve them alone.
may not be able to solve them alone.
131
397010
4042
單憑人類自身的想法可能無法解決。
06:53
We may need a two-step program,
132
401076
1992
我們可能需要分兩步走，
06:55
which is to invent new kinds of thinking
133
403092
4203
先發明出新的思考方式，
06:59
that we can work alongside of to solve
these really large problems,
these really large problems,
134
407692
3734
再來解決這些真正的難題，
07:03
say, like dark energy or quantum gravity.
135
411450
2918
比如說，像是暗能量或量子引力。
07:08
What we're doing
is making alien intelligences.
is making alien intelligences.
136
416496
2646
我們所做的實際上
就是在創造「異形智能」。
就是在創造「異形智能」。
07:11
You might even think of this
as, sort of, artificial aliens
as, sort of, artificial aliens
137
419166
4069
在某種程度上，
這概念有點像是，人造異形。
07:15
in some senses.
138
423259
1207
07:16
And they're going to help
us think different,
us think different,
139
424490
2300
它們將幫助我們從不同的角度思考，
07:18
because thinking different
is the engine of creation
is the engine of creation
140
426814
3632
因為不同的想法是創新、
07:22
and wealth and new economy.
141
430470
1867
財富和新經濟的引擎。
07:25
The second aspect of this
is that we are going to use AI
is that we are going to use AI
142
433835
4923
第二方面：我們將用 AI
進行第二次的工業革命。
07:30
to basically make a second
Industrial Revolution.
Industrial Revolution.
143
438782
2950
07:34
The first Industrial Revolution
was based on the fact
was based on the fact
144
442135
2773
在第一次工業革命中，
是以我稱之為「人工力量」為基礎的革命。
07:36
that we invented something
I would call artificial power.
I would call artificial power.
145
444932
3462
07:40
Previous to that,
146
448879
1150
在此之前，
07:42
during the Agricultural Revolution,
147
450053
2034
在農業革命時期，
07:44
everything that was made
had to be made with human muscle
had to be made with human muscle
148
452111
3702
每樣東西都需要用人力
07:47
or animal power.
149
455837
1307
或畜力完成。
07:49
That was the only way
to get anything done.
to get anything done.
150
457565
2063
除此之外別無它法。
07:51
The great innovation during
the Industrial Revolution was,
the Industrial Revolution was,
151
459652
2945
在工業革命期間最偉大的發明就是
07:54
we harnessed steam power, fossil fuels,
152
462621
3109
我們利用水蒸氣、石化燃料
07:57
to make this artificial power
that we could use
that we could use
153
465754
3856
產生人工力量，
來做任何我們想做的事情。
08:01
to do anything we wanted to do.
154
469634
1669
08:03
So today when you drive down the highway,
155
471327
2772
今日，當你開車行駛在高速公路上，
08:06
you are, with a flick of the switch,
commanding 250 horses --
commanding 250 horses --
156
474571
4525
只要輕輕撥弄開關，
就相當於在駕馭250匹馬，
就相當於在駕馭250匹馬，
08:11
250 horsepower --
157
479120
1572
或者說，250馬力。
08:12
which we can use to build skyscrapers,
to build cities, to build roads,
to build cities, to build roads,
158
480716
4692
它可以讓我們蓋大樓、
建造城市、修建道路，
建造城市、修建道路，
08:17
to make factories that would churn out
lines of chairs or refrigerators
lines of chairs or refrigerators
159
485432
5789
開辦能夠源源不斷
生產椅子或冰箱的工廠，
生產椅子或冰箱的工廠，
這都遠遠超出人力所為。
08:23
way beyond our own power.
160
491245
1654
08:24
And that artificial power can also
be distributed on wires on a grid
be distributed on wires on a grid
161
492923
6111
而且這樣的人工電力可以透過電線、電網
08:31
to every home, factory, farmstead,
162
499058
3199
輸送到每一個家庭、工廠、農場，
08:34
and anybody could buy
that artificial power,
that artificial power,
163
502281
4191
讓每個人都可以買到這樣的人工電力，
只要插上插頭就可以使用。
08:38
just by plugging something in.
164
506496
1472
08:39
So this was a source
of innovation as well,
of innovation as well,
165
507992
2439
所以，這也是創新的來源之一，
08:42
because a farmer could take
a manual hand pump,
a manual hand pump,
166
510455
3418
因為農民可以為手工幫浦通上電，
08:45
and they could add this artificial
power, this electricity,
power, this electricity,
167
513897
2916
有了這種人工力量，
08:48
and he'd have an electric pump.
168
516837
1497
就變成了電動幫浦。
08:50
And you multiply that by thousands
or tens of thousands of times,
or tens of thousands of times,
169
518358
3318
你將這種力量擴大成千上萬倍，
08:53
and that formula was what brought us
the Industrial Revolution.
the Industrial Revolution.
170
521700
3159
而這個公式為我們帶來了工業革命。
08:56
All the things that we see,
all this progress that we now enjoy,
all this progress that we now enjoy,
171
524883
3585
而我們所看到的一切、
那些我們現今享受的過程，
那些我們現今享受的過程，
09:00
has come from the fact
that we've done that.
that we've done that.
172
528492
2063
幾乎都來源於此。
09:02
We're going to do
the same thing now with AI.
the same thing now with AI.
173
530579
2348
現在我們也要在 AI 上做同樣的事。
09:04
We're going to distribute that on a grid,
174
532951
2075
我們將用網路傳送 AI，
09:07
and now you can take that electric pump.
175
535050
2374
現在好比你有一個“電泵”
09:09
You can add some artificial intelligence,
176
537448
2968
你把”電泵“加上人工智能，
09:12
and now you have a smart pump.
177
540440
1481
你就會得到聰明的”電泵”，
09:13
And that, multiplied by a million times,
178
541945
1928
類似的改造做上幾百萬次，
09:15
is going to be this second
Industrial Revolution.
Industrial Revolution.
179
543897
2363
就會引爆第二次的工業革命。
09:18
So now the car is going down the highway,
180
546284
2382
將來汽車行駛在高速公路上，
09:20
it's 250 horsepower,
but in addition, it's 250 minds.
but in addition, it's 250 minds.
181
548690
4294
它不僅有250 匹馬力，還有 250 種腦力。
09:25
That's the auto-driven car.
182
553008
1769
這就是自動駕駛車。
09:26
It's like a new commodity;
183
554801
1389
它是一種新的商品；
09:28
it's a new utility.
184
556214
1303
它是一種新的基礎設施。
09:29
The AI is going to flow
across the grid -- the cloud --
across the grid -- the cloud --
185
557541
3041
AI 將會在網路、雲端上傳輸
09:32
in the same way electricity did.
186
560606
1567
就跟電一樣。
09:34
So everything that we had electrified,
187
562197
2380
所以之前每樣東西我們都把它們電力化，
09:36
we're now going to cognify.
188
564601
1723
現在，我們要把它們認知化，
09:38
And I owe it to Jeff, then,
189
566693
1385
所以，誠如 Jeff 所說的，
09:40
that the formula
for the next 10,000 start-ups
for the next 10,000 start-ups
190
568102
3732
接下來的一萬家初創公司的公式，
09:43
is very, very simple,
191
571858
1162
相當， 相當簡單，
09:45
which is to take x and add AI.
192
573044
3167
就是拿某樣東西 X，加上 AI
09:49
That is the formula,
that's what we're going to be doing.
that's what we're going to be doing.
193
577100
2812
這個公式就是我們將來要做的。
09:51
And that is the way
in which we're going to make
in which we're going to make
194
579936
3306
我們將以這種方式
09:55
this second Industrial Revolution.
195
583266
1858
創造第二次的工業革命。
09:57
And by the way -- right now, this minute,
196
585148
2154
順帶一提，目前，此時此刻，
09:59
you can log on to Google
197
587326
1169
你可以登入Google
10:00
and you can purchase
AI for six cents, 100 hits.
AI for six cents, 100 hits.
198
588519
3882
用六美分購買 AI
來提交一百個圖像識別請求。
來提交一百個圖像識別請求。
10:04
That's available right now.
199
592758
1604
目前已經有這項服務了。
10:06
So the third aspect of this
200
594386
2286
第三個形勢：
10:09
is that when we take this AI
and embody it,
and embody it,
201
597315
2678
如果我們將 AI 編組起來，
10:12
we get robots.
202
600017
1173
我們會得到機械人。
10:13
And robots are going to be bots,
203
601214
1703
而機械人就是一些小型的任務執行器，
10:14
they're going to be doing many
of the tasks that we have already done.
of the tasks that we have already done.
204
602941
3328
它們將會取代我們現在已經在做的事。
10:20
A job is just a bunch of tasks,
205
608357
1528
工作只是一堆任務，
10:21
so they're going to redefine our jobs
206
609909
1762
所以人類的工作會被重新定義，
10:23
because they're going to do
some of those tasks.
some of those tasks.
207
611695
2259
因為它們會幫我們執行這些任務。
10:25
But they're also going to curate
whole new categories,
whole new categories,
208
613978
3197
但它們也會創造出全新的分類
10:29
a whole new slew of tasks
209
617199
2247
很多全新種類的任務，
10:31
that we didn't know
we wanted to do before.
we wanted to do before.
210
619470
2457
一些我們從未聽過的工作。
10:33
They're going to actually
engender new kinds of jobs,
engender new kinds of jobs,
211
621951
3637
它們實際上會催生出新的職業，
10:37
new kinds of tasks that we want done,
212
625612
2271
一些我們願意從事的新工作，
10:39
just as automation made up
a whole bunch of new things
a whole bunch of new things
213
627907
3405
就像自動化所引發的許多新事物，
10:43
that we didn't know we needed before,
214
631336
1834
我們之前並知道會需要它們，
10:45
and now we can't live without them.
215
633194
1956
但時至今日，我們已經離不開它們了。
10:47
So they're going to produce
even more jobs than they take away,
even more jobs than they take away,
216
635174
3956
機器人產生的新工作
比我們被取代的工作還要多，
比我們被取代的工作還要多，
10:51
but it's important that a lot of the tasks
that we're going to give them
that we're going to give them
217
639154
3434
更重要的是，我們交給它們的那些任務
10:54
are tasks that can be defined
in terms of efficiency or productivity.
in terms of efficiency or productivity.
218
642612
4572
都需要效率或生產率。
10:59
If you can specify a task,
219
647676
1828
如果一個任務，不管是體力的還是腦力的，
可以用效率或生產率來衡量的話，
可以用效率或生產率來衡量的話，
11:01
either manual or conceptual,
220
649528
2235
11:03
that can be specified in terms
of efficiency or productivity,
of efficiency or productivity,
221
651787
4780
那麽就應該交給機器人來完成。
11:08
that goes to the bots.
222
656591
1777
11:10
Productivity is for robots.
223
658758
2178
機器人擅長的就是生產率。
11:12
What we're really good at
is basically wasting time.
is basically wasting time.
224
660960
3070
我們真正擅長的是浪費時間。
11:16
(Laughter)
225
664054
1028
（笑聲）
11:17
We're really good at things
that are inefficient.
that are inefficient.
226
665106
2316
我們最擅長做那些沒有效率的事情。
11:19
Science is inherently inefficient.
227
667446
3025
科學從本質上來說是低效的。
11:22
It runs on that fact that you have
one failure after another.
one failure after another.
228
670816
2906
它的運作方式實際上是
一次又一次的失敗，
一次又一次的失敗，
11:25
It runs on the fact that you make tests
and experiments that don't work,
and experiments that don't work,
229
673746
3424
很多試驗和嘗試都徒勞無功，
11:29
otherwise you're not learning.
230
677194
1442
不這樣做，你學不到東西。
11:30
It runs on the fact
231
678660
1162
事實就是，
11:31
that there is not
a lot of efficiency in it.
a lot of efficiency in it.
232
679846
2083
科學研究沒有效率可言。
11:33
Innovation by definition is inefficient,
233
681953
2779
創新從定義上來說就是低效的。
11:36
because you make prototypes,
234
684756
1391
因為我們需要製作原型，
11:38
because you try stuff that fails,
that doesn't work.
that doesn't work.
235
686171
2707
需要做各種嘗試，經歷各種失敗。
11:40
Exploration is inherently inefficiency.
236
688902
3112
探索本質上是低效的。
11:44
Art is not efficient.
237
692038
1531
藝術是低效的。
11:45
Human relationships are not efficient.
238
693593
2127
人際關係也是低效的。
11:47
These are all the kinds of things
we're going to gravitate to,
we're going to gravitate to,
239
695744
2940
這些都是我們喜歡做的事情，
11:50
because they're not efficient.
240
698708
1475
因為它們是低效的。
11:52
Efficiency is for robots.
241
700207
2315
要效率找機器人才對。
11:55
We're also going to learn
that we're going to work with these AIs
that we're going to work with these AIs
242
703338
4123
我們要知道，我們將和 AI 一起工作，
11:59
because they think differently than us.
243
707485
1997
因為它們的思維與我們不同。
12:02
When Deep Blue beat
the world's best chess champion,
the world's best chess champion,
244
710005
4314
當深藍打敗西洋棋的世界冠軍後，
12:06
people thought it was the end of chess.
245
714343
1929
人們認為西洋棋玩完了。
12:08
But actually, it turns out that today,
the best chess champion in the world
the best chess champion in the world
246
716296
4402
但事實上，如今全世界最厲害的西洋棋冠軍
12:12
is not an AI.
247
720722
1557
並不是 AI，
12:14
And it's not a human.
248
722906
1181
也不是人類，
12:16
It's the team of a human and an AI.
249
724111
2715
而是由人類和 AI 組成的團隊。
12:18
The best medical diagnostician
is not a doctor, it's not an AI,
is not a doctor, it's not an AI,
250
726850
4000
最棒的醫學診療師不是醫生，也不是 AI，
12:22
it's the team.
251
730874
1176
而是他們組成的團隊。
12:24
We're going to be working with these AIs,
252
732074
2149
我們將和 AI 一起工作，
12:26
and I think you'll be paid in the future
253
734247
1995
你將來的薪資，
12:28
by how well you work with these bots.
254
736266
2391
很可能取決於你跟機器人合作得如何。
12:31
So that's the third thing,
is that they're different,
is that they're different,
255
739026
4257
這就是我想說的第三點：AI 是不同於我們的，
12:35
they're utility
256
743307
1165
它們是基礎設施，
12:36
and they are going to be something
we work with rather than against.
we work with rather than against.
257
744496
3816
我們將與它們一起工作，
12:40
We're working with these
rather than against them.
rather than against them.
258
748336
2639
而非競爭。
12:42
So, the future:
259
750999
1477
所以，未來：
12:44
Where does that take us?
260
752500
1420
AI 將帶我們到哪裡?
12:45
I think that 25 years from now,
they'll look back
they'll look back
261
753944
3567
我想，二十五年後，
12:49
and look at our understanding
of AI and say,
of AI and say,
262
757535
3125
人們回頭看今日我們對 AI 的理解，
他們會說：
他們會說：
12:52
"You didn't have AI. In fact,
you didn't even have the Internet yet,
you didn't even have the Internet yet,
263
760684
3300
「你們那都不叫 AI，實際上，你們甚至都還沒有真正的網際網路呢！」
12:56
compared to what we're going
to have 25 years from now."
to have 25 years from now."
264
764008
2741
和25年後相比較的話
12:59
There are no AI experts right now.
265
767849
3047
我們還沒有真正的 AI 專家。
13:02
There's a lot of money going to it,
266
770920
1699
目前有大量的資本投資在這個領域，
已經花了數十億美金；
已經花了數十億美金；
13:04
there are billions of dollars
being spent on it;
being spent on it;
267
772643
2268
13:06
it's a huge business,
268
774935
2164
這是一個巨大的產業。
13:09
but there are no experts, compared
to what we'll know 20 years from now.
to what we'll know 20 years from now.
269
777123
4272
和20 年後相比較，我們尚未有真正的 AI 專家。
13:14
So we are just at the beginning
of the beginning,
of the beginning,
270
782064
2885
我們還處在剛開始的開始，
13:16
we're in the first hour of all this.
271
784973
2163
所有這一切才剛開始。
13:19
We're in the first hour of the Internet.
272
787160
1935
我們處在網際網路的第一個小時裏。
13:21
We're in the first hour of what's coming.
273
789119
2040
我們正處在所有事物到來的
第一個小時裏。
第一個小時裏。
13:23
The most popular AI product
in 20 years from now,
in 20 years from now,
274
791183
4153
二十年後最受人們喜愛的 AI 產品，
13:27
that everybody uses,
275
795360
1444
人人都會用的 AI 產品，
13:29
has not been invented yet.
276
797499
1544
還沒有被發明出來。
13:32
That means that you're not late.
277
800464
2467
也就是說，你還為時未晚。
13:35
Thank you.
278
803684
1151
謝謝！
13:36
(Laughter)
279
804859
1026
（笑聲）
13:37
(Applause)
280
805909
2757
（掌聲）
ABOUT THE SPEAKER
Kevin Kelly - Digital visionaryThere may be no one better to contemplate the meaning of cultural change than Kevin Kelly, whose life story reads like a treatise on the value and impacts of technology.
Why you should listen
Kelly has been publisher of the Whole Earth Review, executive editor at Wired magazine (which he co-founded, and where he now holds the title of Senior Maverick), founder of visionary nonprofits and writer on biology, business and “cool tools.” He’s renounced all material things save his bicycle (which he then rode 3,000 miles), founded an organization (the All-Species Foundation) to catalog all life on Earth, championed projects that look 10,000 years into the future (at the Long Now Foundation), and more. He’s admired for his acute perspectives on technology and its relevance to history, biology and society. His new book, The Inevitable, just published, explores 12 technological forces that will shape our future.
Kevin Kelly | Speaker | TED.com