TED2009
Ray Kurzweil: A university for the coming singularity
雷‧庫茲威爾：一所迎接新紀元的大學
Filmed:
Readability: 4.9
1,025,725 views
雷‧庫茲威爾最新的圖表顯示，不管經濟蕭條與否，科技只會持續快速發展。庫茲威爾發表了他的新計畫-成立優越大學(Singularity University)來研究未來的科技走勢，並引導科技的發展，使人類能從中受益。註： "Singularity University，專辦些短期的人文課程給行政人員，希望科技高層除技術發展外可以多了解這個世界，增加人文的關懷。"
Ray Kurzweil - Inventor, futurist
Ray Kurzweil is an engineer who has radically advanced the fields of speech, text and audio technology. He's revered for his dizzying -- yet convincing -- writing on the advance of technology, the limits of biology and the future of the human species. Full bio
Ray Kurzweil is an engineer who has radically advanced the fields of speech, text and audio technology. He's revered for his dizzying -- yet convincing -- writing on the advance of technology, the limits of biology and the future of the human species. Full bio
Double-click the English transcript below to play the video.
00:13
Information technology grows in an exponential manner.
0
1000
3000
資訊科技正在以指數的幅度發展
00:16
It's not linear. And our intuition is linear.
1
4000
4000
它並不是線性的。可是對我們來講，直覺知識卻是線性的
00:20
When we walked through the savanna a thousand years ago
2
8000
2000
一千年以前，當我們走過熱帶草原
00:22
we made linear predictions where that animal would be,
3
10000
2000
我們直接推斷獵物會在哪邊
00:24
and that worked fine. It's hardwired in our brains.
4
12000
3000
這樣的推斷是行得通的。我們已經習慣利用線性的方式來估計
00:27
But the pace of exponential growth
5
15000
3000
但是指數發展的速度
00:30
is really what describes information technologies.
6
18000
3000
才能準確地形容目前的資訊科技.
00:33
And it's not just computation.
7
21000
3000
這不僅僅是計算方式的差異.
00:36
There is a big difference between linear and exponential growth.
8
24000
2000
線性和指數增長有著很大的不同.
00:38
If I take 30 steps linearly -- one, two, three, four, five --
9
26000
4000
假如我直線地走個30步, 1, 2, 3, 4, 5
00:42
I get to 30.
10
30000
2000
我到達30.
00:44
If I take 30 steps exponentially -- two, four, eight, 16 --
11
32000
3000
假如我以指數方式走30步, 2, 4, 8, 16,
00:47
I get to a billion.
12
35000
2000
我到達10億多.
00:49
It makes a huge difference.
13
37000
2000
這相差了十萬八千里.
00:51
And that really describes information technology.
14
39000
2000
指數增長確切地描述了資訊科技
00:53
When I was a student at MIT,
15
41000
2000
當年我還在麻省理工學院上學的時候,
00:55
we all shared one computer that took up a whole building.
16
43000
2000
我們班上共用的一台電腦就佔掉了整棟樓的能量資源.
00:57
The computer in your cellphone today is a million times cheaper,
17
45000
3000
現在手機裡面的電腦程式便宜了一百萬倍,
01:00
a million times smaller,
18
48000
2000
小了一百萬倍,
01:02
a thousand times more powerful.
19
50000
2000
強大了一百萬倍.
01:04
That's a billion-fold increase in capability per dollar
20
52000
3000
這相當於一美元就有一億倍的增長能力
01:07
that we've actually experienced since I was a student.
21
55000
2000
從我還是個學生至今, 這就是我們所經歷的.
01:09
And we're going to do it again in the next 25 years.
22
57000
3000
在未來, 這樣的快速發展還會持續25年.
01:12
Information technology progresses
23
60000
2000
通過一系列的S-曲線
01:14
through a series of S-curves
24
62000
2000
資訊科技將會持續進步
01:16
where each one is a different paradigm.
25
64000
2000
到不同的模式.
01:18
So people say, "What's going to happen when Moore's Law comes to an end?"
26
66000
3000
所以人們問, "當摩爾定律到達終點, 這世界會變成怎樣?"
01:21
Which will happen around 2020.
27
69000
2000
當摩爾定律在2020到達終點,
01:23
We'll then go to the next paradigm.
28
71000
2000
我們會進入下一個發展模式.
01:25
And Moore's Law was not the first paradigm
29
73000
2000
但是摩爾定律並不是第一個導致
01:27
to bring exponential growth to computing.
30
75000
2000
資訊科技指數發展的思維模式.
01:29
The exponential growth of computing started
31
77000
2000
資訊科技指數性的進步發生於
01:31
decades before Gordon Moore was even born.
32
79000
2000
戈登.摩爾出生幾十年前
01:33
And it doesn't just apply to computation.
33
81000
4000
科技的指數發展並不限於電腦科技,
01:37
It's really any technology where we can measure
34
85000
2000
它包含任何一樣
01:39
the underlying information properties.
35
87000
3000
我們所知道到的科技.
01:42
Here we have 49 famous computers. I put them in a logarithmic graph.
36
90000
4000
這裡有49台不同年代的電腦，我用對數線圖做個整理
01:46
The logarithmic scale hides the scale of the increase,
37
94000
4000
對數線的大小影藏了真正增長的比率.
01:50
because this represents trillions-fold increase
38
98000
2000
但是這圖表描繪了自1890以來
01:52
since the 1890 census.
39
100000
3000
科技億萬倍的增長.
01:55
In 1950s they were shrinking vacuum tubes,
40
103000
2000
在50年代, 電腦工程師盡可能的縮小真空管,
01:57
making them smaller and smaller. They finally hit a wall;
41
105000
3000
他們一直改良又改良, 最後到達了極限.
02:00
they couldn't shrink the vacuum tube any more and keep the vacuum.
42
108000
2000
他們不能再縮小真空管，只能保留真空部分
02:02
And that was the end of the shrinking of vacuum tubes,
43
110000
3000
而那就是真空管縮小技術的終點
02:05
but it was not the end of the exponential growth of computing.
44
113000
3000
但那可不是資訊科技指數發展的結局.
02:08
We went to the fourth paradigm, transistors,
45
116000
2000
我們到了第四個發展模式, 改良電晶體
02:10
and finally integrated circuits.
46
118000
2000
然後我們又去整合電路.
02:12
When that comes to an end we'll go to the sixth paradigm;
47
120000
2000
當上個步驟結束了, 我們將到達第六個發展模式,
02:14
three-dimensional self-organizing molecular circuits.
48
122000
4000
開發三維自組織分子電路.
02:18
But what's even more amazing, really, than this
49
126000
3000
但比這個驚人的進步更難以置信的,
02:21
fantastic scale of progress,
50
129000
2000
我說真的,
02:23
is that -- look at how predictable this is.
51
131000
2000
是科技的發展有多麼好預測.
02:25
I mean this went through thick and thin,
52
133000
2000
科技的發展經過大跟小,
02:27
through war and peace, through boom times and recessions.
53
135000
3000
戰爭跟和平, 繁榮跟衰退.
02:30
The Great Depression made not a dent in this exponential progression.
54
138000
4000
1930年的經濟大蕭條根本沒影響到科技的指數發展.
02:34
We'll see the same thing in the economic recession we're having now.
55
142000
4000
在這金融危機裡我們會見識到一樣的結果.
02:38
At least the exponential growth of information technology capability
56
146000
3000
至少資訊科技的指數增長的能力
02:41
will continue unabated.
57
149000
3000
將不會減弱.
02:44
And I just updated these graphs.
58
152000
2000
我更新了這些圖
02:46
Because I had them through 2002 in my book, "The Singularity is Near."
59
154000
3000
因為在我的書"奇點迫近"(The Singularity is Near), 數據只延伸到2002年,
02:49
So we updated them,
60
157000
2000
所以我們更新了資料
02:51
so I could present it here, to 2007.
61
159000
3000
讓我才能夠在2007年發表.
02:54
And I was asked, "Well aren't you nervous?
62
162000
2000
很多人問我, "你不緊張嗎?
02:56
Maybe it kind of didn't stay on this exponential progression."
63
164000
4000
說不定數據並不證明你所說的指數發展."
03:00
I was a little nervous
64
168000
2000
我是有點緊張.
03:02
because maybe the data wouldn't be right,
65
170000
2000
害怕數據可能會不合.
03:04
but I've done this now for 30 years,
66
172000
2000
可是我做這行30多年了,
03:06
and it has stayed on this exponential progression.
67
174000
3000
數據總是證明科技是朝向指數發展的.
03:09
Look at this graph here.You could buy one transistor for a dollar in 1968.
68
177000
3000
看. 在1968年你要花一美元才能買一個電晶體
03:12
You can buy half a billion today,
69
180000
2000
今天一美元可以買五千萬個電晶體
03:14
and they are actually better, because they are faster.
70
182000
2000
實際上今天的晶體管更好, 更快.
03:16
But look at how predictable this is.
71
184000
2000
看科技的發展有多麼好預測.
03:18
And I'd say this knowledge is over-fitting to past data.
72
186000
3000
我會說這資訊是過去式了.
03:21
I've been making these forward-looking predictions for about 30 years.
73
189000
4000
我做了超過30年的前瞻性預測.
03:25
And the cost of a transistor cycle,
74
193000
2000
電晶體的費用,
03:27
which is a measure of the price performance of electronics,
75
195000
2000
相應地呈現了電子的市場價格,
03:29
comes down about every year.
76
197000
2000
每年都下降.
03:31
That's a 50 percent deflation rate.
77
199000
2000
那說明了百分之五十的下降.
03:33
And it's also true of other examples,
78
201000
2000
而且它也適用於其他的例子
03:35
like DNA data or brain data.
79
203000
2000
例如DNA數據或大腦的數據.
03:37
But we more than make up for that.
80
205000
2000
但是我們的社會進步的更快.
03:39
We actually ship more than twice as much
81
207000
2000
實際上我們生產一倍以上
03:41
of every form of information technology.
82
209000
2000
一種同樣的科技.
03:43
We've had 18 percent growth in constant dollars
83
211000
3000
過去半個世紀，不管哪種資訊科技，
03:46
in every form of information technology for the last half-century,
84
214000
3000
衡定價值都有百分之十八的增長
03:49
despite the fact that you can get twice as much of it each year.
85
217000
4000
儘管你每年都可以得到一倍以上的回報
03:53
This is a completely different example.
86
221000
2000
這是個完全不同的例子.
03:55
This is not Moore's Law.
87
223000
2000
這不是摩爾定律.
03:57
The amount of DNA data
88
225000
2000
我們所獲得DNA數據的總量
03:59
we've sequenced has doubled every year.
89
227000
2000
總是增加一倍以上.
04:01
The cost has come down by half every year.
90
229000
3000
而每年費用卻下跌一半.
04:04
And this has been a smooth progression
91
232000
2000
自從人類基因定序計劃(Human Genome Project),
04:06
since the beginning of the genome project.
92
234000
2000
這已經成為了一個持續的發展定律.
04:08
And halfway through the project, skeptics said,
93
236000
2000
當這計劃進行到一半時, 有人懷疑
04:10
"Well, this is not working out. You're halfway through the genome project
94
238000
3000
"這不會成功的. 已過了一半的計劃時間,
04:13
and you've finished one percent of the project."
95
241000
2000
你卻只完成了百分之一的任務."
04:15
But that was really right on schedule.
96
243000
2000
可是那工程是如期進行.
04:17
Because if you double one percent seven more times,
97
245000
2000
因為如果你將百分之一乘兩倍，並連乘七次以上
04:19
which is exactly what happened,
98
247000
2000
實際上所產生的,
04:21
you get 100 percent. And the project was finished on time.
99
249000
3000
就是百分之百. 如此工程按照時間地完成了.
04:24
Communication technologies:
100
252000
2000
傳播科技
04:26
50 different ways to measure this,
101
254000
2000
可用50種不同的方式來評量
04:28
the number of bits being moved around, the size of the Internet.
102
256000
3000
正在移動的位元數目, 網路的大小.
04:31
But this has progressed at an exponential pace.
103
259000
2000
但科技正在以指數的步伐進步.
04:33
This is deeply democratizing.
104
261000
2000
這是強烈地民主化
04:35
I wrote, over 20 years ago in "The Age of Intelligent Machines,"
105
263000
3000
20年前，我在我的書"誰會代替人類:智能簡史" (The Age of Intelligent Machines) 中寫到,
04:38
when the Soviet Union was going strong, that it would be swept away
106
266000
3000
當蘇聯正強大的時候,
04:41
by this growth of decentralized communication.
107
269000
4000
它會被這鼓增長的非主流通訊勢力瓦解
04:45
And we will have plenty of computation as we go through the 21st century
108
273000
3000
當我們經過21世紀, 我們能運用大量電腦科技
04:48
to do things like simulate regions of the human brain.
109
276000
4000
來做些事，例如模擬人類大腦區域
04:52
But where will we get the software?
110
280000
2000
但是我們要從哪裡得到這科技?
04:54
Some critics say, "Oh, well software is stuck in the mud."
111
282000
3000
有寫評論家說, "喔, 科技還沒那麼發達."
04:57
But we are learning more and more about the human brain.
112
285000
2000
事實上, 我們越來越了解人類大腦
04:59
Spatial resolution of brain scanning is doubling every year.
113
287000
3000
每年腦部掃描的空間分辨率都比前年高了一倍.
05:02
The amount of data we're getting about the brain is doubling every year.
114
290000
3000
每年我們所得到有關人類大腦的訊息都增加了一倍.
05:05
And we're showing that we can actually turn this data
115
293000
3000
我們證明，事實上可以轉化這個數據
05:08
into working models and simulations of brain regions.
116
296000
3000
便成大腦區域的模型和模擬
05:11
There is about 20 regions of the brain that have been modeled,
117
299000
2000
目前人類大概建構，模擬並測試了
05:13
simulated and tested:
118
301000
2000
20個大腦區域:
05:15
the auditory cortex, regions of the visual cortex;
119
303000
3000
不同的聽覺和視覺皮層區域,
05:18
cerebellum, where we do our skill formation;
120
306000
2000
構成不同能力的小腦,
05:20
slices of the cerebral cortex, where we do our rational thinking.
121
308000
4000
做理性思考的大腦等.
05:24
And all of this has fueled
122
312000
2000
所有的發現,
05:26
an increase, very smooth and predictable, of productivity.
123
314000
3000
以相當平穩可預測的模式，增加了生產力.
05:29
We've gone from 30 dollars to 130 dollars
124
317000
2000
因為資訊科技的進步,
05:31
in constant dollars in the value of an average hour of human labor,
125
319000
4000
我們的工作價值從每小時30元美金
05:35
fueled by this information technology.
126
323000
3000
到每小時130元美金.
05:38
And we're all concerned about energy and the environment.
127
326000
3000
這還只是能源和環境的影響.
05:41
Well this is a logarithmic graph.
128
329000
2000
嗯, 這是一個對數圖.
05:43
This represents a smooth doubling,
129
331000
2000
每兩年,
05:45
every two years, of the amount of solar energy we're creating,
130
333000
4000
我們製造的太陽能持續倍增.
05:49
particularly as we're now applying nanotechnology,
131
337000
2000
特別是我們現在正在運用奈米科技,
05:51
a form of information technology, to solar panels.
132
339000
3000
一種資訊科技, 在太陽能電池板上.
05:54
And we're only eight doublings away
133
342000
2000
我們現在只離我們所需要的百分之百能量
05:56
from it meeting 100 percent of our energy needs.
134
344000
2000
八次的雙倍增長.
05:58
And there is 10 thousand times more sunlight than we need.
135
346000
4000
而太陽能則超過我們一萬多倍的需求.
06:02
We ultimately will merge with this technology. It's already very close to us.
136
350000
5000
最後太陽能會和科技結合。時間就快到了。
06:07
When I was a student it was across campus, now it's in our pockets.
137
355000
3000
當我還是個學生, 它在校園的對面. 現在它可以放進我們的口袋裡.
06:10
What used to take up a building now fits in our pockets.
138
358000
3000
以前用掉整棟大樓資源的現在適合放進我們的口袋裡.
06:13
What now fits in our pockets would fit in a blood cell in 25 years.
139
361000
3000
現在放得進我們口袋裡的，25年後將可以放在一個紅血球裡.
06:16
And we will begin to actually deeply influence
140
364000
4000
當我們越來越接近這科技,
06:20
our health and our intelligence,
141
368000
2000
我們會真正開始左右
06:22
as we get closer and closer to this technology.
142
370000
4000
我們的健康跟智慧.
06:26
Based on that we are announcing, here at TED,
143
374000
3000
所以我們要以TED一貫的傳統，,
06:29
in true TED tradition, Singularity University.
144
377000
3000
在TED這裡宣布，我們要設立優越大學.
06:32
It's a new university
145
380000
2000
這是一所全新的大學
06:34
that's founded by Peter Diamandis, who is here in the audience,
146
382000
2000
由台下的聽眾，彼得‧岱爾莽第斯先生
06:36
and myself.
147
384000
2000
和我所創立.
06:38
It's backed by NASA and Google,
148
386000
2000
它獲得美國太空總署(NASA)和Google的贊助
06:40
and other leaders in the high-tech and science community.
149
388000
4000
還有其他在高科技領域的領袖們的支持.
06:44
And our goal was to assemble the leaders,
150
392000
3000
我們的目標是召集領導人,
06:47
both teachers and students,
151
395000
2000
--老師和學生,
06:49
in these exponentially growing information technologies,
152
397000
2000
來研究這個指數發展的資訊科技
06:51
and their application.
153
399000
2000
和它的用途.
06:53
But Larry Page made an impassioned speech
154
401000
2000
裴基(Larry Page)先生在我們的會議上
06:55
at our organizing meeting,
155
403000
2000
發表了一段熱烈的演講.
06:57
saying we should devote this study
156
405000
5000
他說我們應致力研究於
07:02
to actually addressing some of the major challenges facing humanity.
157
410000
4000
真正解決一些人類面臨的重大挑戰.
07:06
And if we did that, then Google would back this.
158
414000
2000
假如我們做了這選擇, Google會資助我們.
07:08
And so that's what we've done.
159
416000
2000
所以我們做了研究上的一些改變.
07:10
The last third of the nine-week intensive summer session
160
418000
4000
在密集的九週暑期學營裡的最後三週,
07:14
will be devoted to a group project to address
161
422000
2000
我們將會分組專門來提出
07:16
some major challenge of humanity.
162
424000
2000
一些社會上面臨的重大挑戰.
07:18
Like for example, applying the Internet,
163
426000
2000
例如將今天已經很普及的網路,
07:20
which is now ubiquitous, in the rural areas of China or in Africa,
164
428000
5000
提供給中國和非洲的鄉村地區,
07:25
to bringing health information
165
433000
2000
好將健康資訊
07:27
to developing areas of the world.
166
435000
3000
傳播到世界的每個發展地區.
07:30
And these projects will continue past these sessions,
167
438000
3000
這些科研項目會延展到這些學營外,
07:33
using collaborative interactive communication.
168
441000
3000
通過協作地互動溝通討論.
07:36
All the intellectual property that is created and taught
169
444000
4000
所有萌生和傳授的智慧財產
07:40
will be online and available,
170
448000
2000
將會在網路上公開,
07:42
and developed online in a collaborative fashion.
171
450000
3000
並在網路上互相合作發展.
07:45
Here is our founding meeting.
172
453000
2000
這是我們的創校會議的照片.
07:47
But this is being announced today.
173
455000
2000
今天我們在這裡發佈.
07:49
It will be permanently headquartered in Silicon Valley,
174
457000
3000
優越大學(Singulariy University)將會永久設置在矽谷,
07:52
at the NASA Ames research center.
175
460000
2000
在NASA的艾密斯研究中心.
07:54
There are different programs for graduate students,
176
462000
2000
我們提供不同的課程給研究生,
07:56
for executives at different companies.
177
464000
3000
和不同公司的高階主管.
07:59
The first six tracks here -- artificial intelligence,
178
467000
2000
這裡的六種首要研究方向, 人工智能,
08:01
advanced computing technologies, biotechnology, nanotechnology --
179
469000
3000
先進的電腦科技，生物科技，奈米科技
08:04
are the different core areas of information technology.
180
472000
4000
分別是資訊科技不同的的核心領域.
08:08
Then we are going to apply them to the other areas,
181
476000
2000
然後我們將會將它們應用到其他領域,
08:10
like energy, ecology,
182
478000
3000
例如能源, 生態環境,
08:13
policy law and ethics, entrepreneurship,
183
481000
2000
政策法律和道德, 企業態度,
08:15
so that people can bring these new technologies to the world.
184
483000
4000
使人們可以把這些新技術帶給世界.
08:19
So we're very appreciative of the support we've gotten
185
487000
5000
我們非常感謝我們所得到,
08:24
from both the intellectual leaders, the high-tech leaders,
186
492000
2000
來自知識份子和高科技領導人們的支持,
08:26
particularly Google and NASA.
187
494000
2000
特別是Google和NASA.
08:28
This is an exciting new venture.
188
496000
2000
這是個興奮的全新研究.
08:30
And we invite you to participate. Thank you very much.
189
498000
3000
我們誠心地邀請你的加入. 謝謝.
08:33
(Applause)
190
501000
3000
(鼓掌)
ABOUT THE SPEAKER
Ray Kurzweil - Inventor, futuristRay Kurzweil is an engineer who has radically advanced the fields of speech, text and audio technology. He's revered for his dizzying -- yet convincing -- writing on the advance of technology, the limits of biology and the future of the human species.
Why you should listen
Inventor, entrepreneur, visionary, Ray Kurzweil's accomplishments read as a startling series of firsts -- a litany of technological breakthroughs we've come to take for granted. Kurzweil invented the first optical character recognition (OCR) software for transforming the written word into data, the first print-to-speech software for the blind, the first text-to-speech synthesizer, and the first music synthesizer capable of recreating the grand piano and other orchestral instruments, and the first commercially marketed large-vocabulary speech recognition.
Yet his impact as a futurist and philosopher is no less significant. In his best-selling books, which include How to Create a Mind, The Age of Spiritual Machines, The Singularity Is Near: When Humans Transcend Biology, Kurzweil depicts in detail a portrait of the human condition over the next few decades, as accelerating technologies forever blur the line between human and machine.
In 2009, he unveiled Singularity University, an institution that aims to "assemble, educate and inspire leaders who strive to understand and facilitate the development of exponentially advancing technologies." He is a Director of Engineering at Google, where he heads up a team developing machine intelligence and natural language comprehension.
Ray Kurzweil | Speaker | TED.com