Ray Kurzweil: A university for the coming singularity

譯者: Steven Shi 審譯者: Alice Hsueh

Information technology grows in an exponential manner.

資訊科技正在以指數的幅度發展

It's not linear. And our intuition is linear.

它並不是線性的。可是對我們來講，直覺知識卻是線性的

When we walked through the savanna a thousand years ago

一千年以前，當我們走過熱帶草原

we made linear predictions where that animal would be,

我們直接推斷獵物會在哪邊

and that worked fine. It's hardwired in our brains.

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

But the pace of exponential growth

但是指數發展的速度

is really what describes information technologies.

才能準確地形容目前的資訊科技.

And it's not just computation.

這不僅僅是計算方式的差異.

There is a big difference between linear and exponential growth.

線性和指數增長有著很大的不同.

If I take 30 steps linearly -- one, two, three, four, five --

假如我直線地走個30步, 1, 2, 3, 4, 5

I get to 30.

我到達30.

If I take 30 steps exponentially -- two, four, eight, 16 --

假如我以指數方式走30步, 2, 4, 8, 16,

I get to a billion.

我到達10億多.

It makes a huge difference.

這相差了十萬八千里.

And that really describes information technology.

指數增長確切地描述了資訊科技

When I was a student at MIT,

當年我還在麻省理工學院上學的時候,

we all shared one computer that took up a whole building.

我們班上共用的一台電腦就佔掉了整棟樓的能量資源.

The computer in your cellphone today is a million times cheaper,

現在手機裡面的電腦程式便宜了一百萬倍,

a million times smaller,

小了一百萬倍,

a thousand times more powerful.

強大了一百萬倍.

That's a billion-fold increase in capability per dollar

這相當於一美元就有一億倍的增長能力

that we've actually experienced since I was a student.

從我還是個學生至今, 這就是我們所經歷的.

And we're going to do it again in the next 25 years.

在未來, 這樣的快速發展還會持續25年.

Information technology progresses

通過一系列的S-曲線

through a series of S-curves

資訊科技將會持續進步

where each one is a different paradigm.

到不同的模式.

So people say, "What's going to happen when Moore's Law comes to an end?"

所以人們問, "當摩爾定律到達終點, 這世界會變成怎樣?"

Which will happen around 2020.

當摩爾定律在2020到達終點,

We'll then go to the next paradigm.

我們會進入下一個發展模式.

And Moore's Law was not the first paradigm

但是摩爾定律並不是第一個導致

to bring exponential growth to computing.

資訊科技指數發展的思維模式.

The exponential growth of computing started

資訊科技指數性的進步發生於

decades before Gordon Moore was even born.

戈登.摩爾出生幾十年前

And it doesn't just apply to computation.

科技的指數發展並不限於電腦科技,

It's really any technology where we can measure

它包含任何一樣

the underlying information properties.

我們所知道到的科技.

Here we have 49 famous computers. I put them in a logarithmic graph.

這裡有49台不同年代的電腦，我用對數線圖做個整理

The logarithmic scale hides the scale of the increase,

對數線的大小影藏了真正增長的比率.

because this represents trillions-fold increase

但是這圖表描繪了自1890以來

since the 1890 census.

科技億萬倍的增長.

In 1950s they were shrinking vacuum tubes,

在50年代, 電腦工程師盡可能的縮小真空管,

making them smaller and smaller. They finally hit a wall;

他們一直改良又改良, 最後到達了極限.

they couldn't shrink the vacuum tube any more and keep the vacuum.

他們不能再縮小真空管，只能保留真空部分

And that was the end of the shrinking of vacuum tubes,

而那就是真空管縮小技術的終點

but it was not the end of the exponential growth of computing.

但那可不是資訊科技指數發展的結局.

We went to the fourth paradigm, transistors,

我們到了第四個發展模式, 改良電晶體

and finally integrated circuits.

然後我們又去整合電路.

When that comes to an end we'll go to the sixth paradigm;

當上個步驟結束了, 我們將到達第六個發展模式,

three-dimensional self-organizing molecular circuits.

開發三維自組織分子電路.

But what's even more amazing, really, than this

但比這個驚人的進步更難以置信的,

fantastic scale of progress,

我說真的,

is that -- look at how predictable this is.

是科技的發展有多麼好預測.

I mean this went through thick and thin,

科技的發展經過大跟小,

through war and peace, through boom times and recessions.

戰爭跟和平, 繁榮跟衰退.

The Great Depression made not a dent in this exponential progression.

1930年的經濟大蕭條根本沒影響到科技的指數發展.

We'll see the same thing in the economic recession we're having now.

在這金融危機裡我們會見識到一樣的結果.

At least the exponential growth of information technology capability

至少資訊科技的指數增長的能力

will continue unabated.

將不會減弱.

And I just updated these graphs.

我更新了這些圖

Because I had them through 2002 in my book, "The Singularity is Near."

因為在我的書"奇點迫近"(The Singularity is Near), 數據只延伸到2002年,

So we updated them,

所以我們更新了資料

so I could present it here, to 2007.

讓我才能夠在2007年發表.

And I was asked, "Well aren't you nervous?

很多人問我, "你不緊張嗎?

Maybe it kind of didn't stay on this exponential progression."

說不定數據並不證明你所說的指數發展."

I was a little nervous

我是有點緊張.

because maybe the data wouldn't be right,

害怕數據可能會不合.

but I've done this now for 30 years,

可是我做這行30多年了,

and it has stayed on this exponential progression.

數據總是證明科技是朝向指數發展的.

Look at this graph here.You could buy one transistor for a dollar in 1968.

看. 在1968年你要花一美元才能買一個電晶體

You can buy half a billion today,

今天一美元可以買五千萬個電晶體

and they are actually better, because they are faster.

實際上今天的晶體管更好, 更快.

But look at how predictable this is.

看科技的發展有多麼好預測.

And I'd say this knowledge is over-fitting to past data.

我會說這資訊是過去式了.

I've been making these forward-looking predictions for about 30 years.

我做了超過30年的前瞻性預測.

And the cost of a transistor cycle,

電晶體的費用,

which is a measure of the price performance of electronics,

相應地呈現了電子的市場價格,

comes down about every year.

每年都下降.

That's a 50 percent deflation rate.

那說明了百分之五十的下降.

And it's also true of other examples,

而且它也適用於其他的例子

like DNA data or brain data.

例如DNA數據或大腦的數據.

But we more than make up for that.

但是我們的社會進步的更快.

We actually ship more than twice as much

實際上我們生產一倍以上

of every form of information technology.

一種同樣的科技.

We've had 18 percent growth in constant dollars

過去半個世紀，不管哪種資訊科技，

in every form of information technology for the last half-century,

衡定價值都有百分之十八的增長

despite the fact that you can get twice as much of it each year.

儘管你每年都可以得到一倍以上的回報

This is a completely different example.

這是個完全不同的例子.

This is not Moore's Law.

這不是摩爾定律.

The amount of DNA data

我們所獲得DNA數據的總量

we've sequenced has doubled every year.

總是增加一倍以上.

The cost has come down by half every year.

而每年費用卻下跌一半.

And this has been a smooth progression

自從人類基因定序計劃(Human Genome Project),

since the beginning of the genome project.

這已經成為了一個持續的發展定律.

And halfway through the project, skeptics said,

當這計劃進行到一半時, 有人懷疑

"Well, this is not working out. You're halfway through the genome project

"這不會成功的. 已過了一半的計劃時間,

and you've finished one percent of the project."

你卻只完成了百分之一的任務."

But that was really right on schedule.

可是那工程是如期進行.

Because if you double one percent seven more times,

因為如果你將百分之一乘兩倍，並連乘七次以上

which is exactly what happened,

實際上所產生的,

you get 100 percent. And the project was finished on time.

就是百分之百. 如此工程按照時間地完成了.

Communication technologies:

傳播科技

50 different ways to measure this,

可用50種不同的方式來評量

the number of bits being moved around, the size of the Internet.

正在移動的位元數目, 網路的大小.

But this has progressed at an exponential pace.

但科技正在以指數的步伐進步.

This is deeply democratizing.

這是強烈地民主化

I wrote, over 20 years ago in "The Age of Intelligent Machines,"

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,

when the Soviet Union was going strong, that it would be swept away

當蘇聯正強大的時候,

by this growth of decentralized communication.

它會被這鼓增長的非主流通訊勢力瓦解

And we will have plenty of computation as we go through the 21st century

當我們經過21世紀, 我們能運用大量電腦科技

to do things like simulate regions of the human brain.

來做些事，例如模擬人類大腦區域

But where will we get the software?

但是我們要從哪裡得到這科技?

Some critics say, "Oh, well software is stuck in the mud."

有寫評論家說, "喔, 科技還沒那麼發達."

But we are learning more and more about the human brain.

事實上, 我們越來越了解人類大腦

Spatial resolution of brain scanning is doubling every year.

每年腦部掃描的空間分辨率都比前年高了一倍.

The amount of data we're getting about the brain is doubling every year.

每年我們所得到有關人類大腦的訊息都增加了一倍.

And we're showing that we can actually turn this data

我們證明，事實上可以轉化這個數據

into working models and simulations of brain regions.

便成大腦區域的模型和模擬

There is about 20 regions of the brain that have been modeled,

目前人類大概建構，模擬並測試了

simulated and tested:

20個大腦區域:

the auditory cortex, regions of the visual cortex;

不同的聽覺和視覺皮層區域,

cerebellum, where we do our skill formation;

構成不同能力的小腦,

slices of the cerebral cortex, where we do our rational thinking.

做理性思考的大腦等.

And all of this has fueled

所有的發現,

an increase, very smooth and predictable, of productivity.

以相當平穩可預測的模式，增加了生產力.

We've gone from 30 dollars to 130 dollars

因為資訊科技的進步,

in constant dollars in the value of an average hour of human labor,

我們的工作價值從每小時30元美金

fueled by this information technology.

到每小時130元美金.

And we're all concerned about energy and the environment.

這還只是能源和環境的影響.

Well this is a logarithmic graph.

嗯, 這是一個對數圖.

This represents a smooth doubling,

每兩年,

every two years, of the amount of solar energy we're creating,

我們製造的太陽能持續倍增.

particularly as we're now applying nanotechnology,

特別是我們現在正在運用奈米科技,

a form of information technology, to solar panels.

一種資訊科技, 在太陽能電池板上.

And we're only eight doublings away

我們現在只離我們所需要的百分之百能量

from it meeting 100 percent of our energy needs.

八次的雙倍增長.

And there is 10 thousand times more sunlight than we need.

而太陽能則超過我們一萬多倍的需求.

We ultimately will merge with this technology. It's already very close to us.

最後太陽能會和科技結合。時間就快到了。

When I was a student it was across campus, now it's in our pockets.

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.

What used to take up a building now fits in our pockets.

以前用掉整棟大樓資源的現在適合放進我們的口袋裡.

What now fits in our pockets would fit in a blood cell in 25 years.

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.

And we will begin to actually deeply influence

當我們越來越接近這科技,

our health and our intelligence,

我們會真正開始左右

as we get closer and closer to this technology.

我們的健康跟智慧.

Based on that we are announcing, here at TED,

所以我們要以TED一貫的傳統，,

in true TED tradition, Singularity University.

在TED這裡宣布，我們要設立優越大學.

It's a new university

這是一所全新的大學

that's founded by Peter Diamandis, who is here in the audience,

由台下的聽眾，彼得‧岱爾莽第斯先生

and myself.

和我所創立.

It's backed by NASA and Google,

它獲得美國太空總署(NASA)和Google的贊助

and other leaders in the high-tech and science community.

還有其他在高科技領域的領袖們的支持.

And our goal was to assemble the leaders,

我們的目標是召集領導人,

both teachers and students,

--老師和學生,

in these exponentially growing information technologies,

來研究這個指數發展的資訊科技

and their application.

和它的用途.

But Larry Page made an impassioned speech

裴基(Larry Page)先生在我們的會議上

at our organizing meeting,

發表了一段熱烈的演講.

saying we should devote this study

他說我們應致力研究於

to actually addressing some of the major challenges facing humanity.

真正解決一些人類面臨的重大挑戰.

And if we did that, then Google would back this.

假如我們做了這選擇, Google會資助我們.

And so that's what we've done.

所以我們做了研究上的一些改變.

The last third of the nine-week intensive summer session

在密集的九週暑期學營裡的最後三週,

will be devoted to a group project to address

我們將會分組專門來提出

some major challenge of humanity.

一些社會上面臨的重大挑戰.

Like for example, applying the Internet,

例如將今天已經很普及的網路,

which is now ubiquitous, in the rural areas of China or in Africa,

提供給中國和非洲的鄉村地區,

to bringing health information

好將健康資訊

to developing areas of the world.

傳播到世界的每個發展地區.

And these projects will continue past these sessions,

這些科研項目會延展到這些學營外,

using collaborative interactive communication.

通過協作地互動溝通討論.

All the intellectual property that is created and taught

所有萌生和傳授的智慧財產

will be online and available,

將會在網路上公開,

and developed online in a collaborative fashion.

並在網路上互相合作發展.

Here is our founding meeting.

這是我們的創校會議的照片.

But this is being announced today.

今天我們在這裡發佈.

It will be permanently headquartered in Silicon Valley,

優越大學(Singulariy University)將會永久設置在矽谷,

at the NASA Ames research center.

在NASA的艾密斯研究中心.

There are different programs for graduate students,

我們提供不同的課程給研究生,

for executives at different companies.

和不同公司的高階主管.

The first six tracks here -- artificial intelligence,

這裡的六種首要研究方向, 人工智能,

advanced computing technologies, biotechnology, nanotechnology --

先進的電腦科技，生物科技，奈米科技

are the different core areas of information technology.

分別是資訊科技不同的的核心領域.

Then we are going to apply them to the other areas,

然後我們將會將它們應用到其他領域,

like energy, ecology,

例如能源, 生態環境,

policy law and ethics, entrepreneurship,

政策法律和道德, 企業態度,

so that people can bring these new technologies to the world.

使人們可以把這些新技術帶給世界.

So we're very appreciative of the support we've gotten

我們非常感謝我們所得到,

from both the intellectual leaders, the high-tech leaders,

來自知識份子和高科技領導人們的支持,

particularly Google and NASA.

特別是Google和NASA.

This is an exciting new venture.

這是個興奮的全新研究.

And we invite you to participate. Thank you very much.

我們誠心地邀請你的加入. 謝謝.

(Applause)

(鼓掌)