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

資訊科技正在以指數的幅度發展

When we walked through the savanna a thousand years ago

它並不是線性的。可是對我們來講，直覺知識卻是線性的

we made linear predictions where that animal would be,

一千年以前，當我們走過熱帶草原

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

我們直接推斷獵物會在哪邊

But the pace of exponential growth

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

is really what describes information technologies.

但是指數發展的速度

And it's not just computation.

才能準確地形容目前的資訊科技.

There is a big difference between linear and exponential growth.

這不僅僅是計算方式的差異.

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

線性和指數增長有著很大的不同.

I get to 30.

假如我直線地走個30步, 1, 2, 3, 4, 5

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

我到達30.

I get to a billion.

假如我以指數方式走30步, 2, 4, 8, 16,

It makes a huge difference.

我到達10億多.

And that really describes information technology.

這相差了十萬八千里.

When I was a student at MIT,

指數增長確切地描述了資訊科技

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

當年我還在麻省理工學院上學的時候,

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

我們班上共用的一台電腦就佔掉了整棟樓的能量資源.

a million times smaller,

現在手機裡面的電腦程式便宜了一百萬倍,

a thousand times more powerful.

小了一百萬倍,

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

強大了一百萬倍.

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

這相當於一美元就有一億倍的增長能力

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

從我還是個學生至今, 這就是我們所經歷的.

Information technology progresses

在未來, 這樣的快速發展還會持續25年.

through a series of S-curves

通過一系列的S-曲線

where each one is a different paradigm.

資訊科技將會持續進步

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

到不同的模式.

Which will happen around 2020.

所以人們問, "當摩爾定律到達終點, 這世界會變成怎樣?"

We'll then go to the next paradigm.

當摩爾定律在2020到達終點,

And Moore's Law was not the first paradigm

我們會進入下一個發展模式.

to bring exponential growth to computing.

但是摩爾定律並不是第一個導致

The exponential growth of computing started

資訊科技指數發展的思維模式.

decades before Gordon Moore was even born.

資訊科技指數性的進步發生於

And it doesn't just apply to computation.

戈登.摩爾出生幾十年前

It's really any technology where we can measure

科技的指數發展並不限於電腦科技,

the underlying information properties.

它包含任何一樣

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

我們所知道到的科技.

The logarithmic scale hides the scale of the increase,

這裡有49台不同年代的電腦，我用對數線圖做個整理

because this represents trillions-fold increase

對數線的大小影藏了真正增長的比率.

since the 1890 census.

但是這圖表描繪了自1890以來

In 1950s they were shrinking vacuum tubes,

科技億萬倍的增長.

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

在50年代, 電腦工程師盡可能的縮小真空管,

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

他們一直改良又改良, 最後到達了極限.

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

他們不能再縮小真空管，只能保留真空部分

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

而那就是真空管縮小技術的終點

We went to the fourth paradigm, transistors,

但那可不是資訊科技指數發展的結局.

and finally integrated circuits.

我們到了第四個發展模式, 改良電晶體

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

然後我們又去整合電路.

three-dimensional self-organizing molecular circuits.

當上個步驟結束了, 我們將到達第六個發展模式,

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

開發三維自組織分子電路.

fantastic scale of progress,

但比這個驚人的進步更難以置信的,

is that -- look at how predictable this is.

我說真的,

I mean this went through thick and thin,

是科技的發展有多麼好預測.

through war and peace, through boom times and recessions.

科技的發展經過大跟小,

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

戰爭跟和平, 繁榮跟衰退.

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

1930年的經濟大蕭條根本沒影響到科技的指數發展.

At least the exponential growth of information technology capability

在這金融危機裡我們會見識到一樣的結果.

will continue unabated.

至少資訊科技的指數增長的能力

And I just updated these graphs.

將不會減弱.

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

我更新了這些圖

So we updated them,

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

so I could present it here, to 2007.

所以我們更新了資料

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

讓我才能夠在2007年發表.

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

很多人問我, "你不緊張嗎?

I was a little nervous

說不定數據並不證明你所說的指數發展."

because maybe the data wouldn't be right,

我是有點緊張.

but I've done this now for 30 years,

害怕數據可能會不合.

and it has stayed on this exponential progression.

可是我做這行30多年了,

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

數據總是證明科技是朝向指數發展的.

You can buy half a billion today,

看. 在1968年你要花一美元才能買一個電晶體

and they are actually better, because they are faster.

今天一美元可以買五千萬個電晶體

But look at how predictable this is.

實際上今天的晶體管更好, 更快.

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

看科技的發展有多麼好預測.

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

我會說這資訊是過去式了.

And the cost of a transistor cycle,

我做了超過30年的前瞻性預測.

which is a measure of the price performance of electronics,

電晶體的費用,

comes down about every year.

相應地呈現了電子的市場價格,

That's a 50 percent deflation rate.

每年都下降.

And it's also true of other examples,

那說明了百分之五十的下降.

like DNA data or brain data.

而且它也適用於其他的例子

But we more than make up for that.

例如DNA數據或大腦的數據.

We actually ship more than twice as much

但是我們的社會進步的更快.

of every form of information technology.

實際上我們生產一倍以上

We've had 18 percent growth in constant dollars

一種同樣的科技.

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

過去半個世紀，不管哪種資訊科技，

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

衡定價值都有百分之十八的增長

This is a completely different example.

儘管你每年都可以得到一倍以上的回報

This is not Moore's Law.

這是個完全不同的例子.

The amount of DNA data

這不是摩爾定律.

we've sequenced has doubled every year.

我們所獲得DNA數據的總量

The cost has come down by half every year.

總是增加一倍以上.

And this has been a smooth progression

而每年費用卻下跌一半.

since the beginning of the genome project.

自從人類基因定序計劃(Human Genome Project),

And halfway through the project, skeptics said,

這已經成為了一個持續的發展定律.

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

當這計劃進行到一半時, 有人懷疑

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

"這不會成功的. 已過了一半的計劃時間,

But that was really right on schedule.

你卻只完成了百分之一的任務."

Because if you double one percent seven more times,

可是那工程是如期進行.

which is exactly what happened,

因為如果你將百分之一乘兩倍，並連乘七次以上

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

實際上所產生的,

Communication technologies:

就是百分之百. 如此工程按照時間地完成了.

50 different ways to measure this,

傳播科技

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

可用50種不同的方式來評量

But this has progressed at an exponential pace.

正在移動的位元數目, 網路的大小.

This is deeply democratizing.

但科技正在以指數的步伐進步.

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

這是強烈地民主化

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

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,

by this growth of decentralized communication.

當蘇聯正強大的時候,

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

它會被這鼓增長的非主流通訊勢力瓦解

to do things like simulate regions of the human brain.

當我們經過21世紀, 我們能運用大量電腦科技

But where will we get the software?

來做些事，例如模擬人類大腦區域

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

但是我們要從哪裡得到這科技?

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

有寫評論家說, "喔, 科技還沒那麼發達."

Spatial resolution of brain scanning is doubling every year.

事實上, 我們越來越了解人類大腦

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

每年腦部掃描的空間分辨率都比前年高了一倍.

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

每年我們所得到有關人類大腦的訊息都增加了一倍.

into working models and simulations of brain regions.

我們證明，事實上可以轉化這個數據

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

便成大腦區域的模型和模擬

simulated and tested:

目前人類大概建構，模擬並測試了

the auditory cortex, regions of the visual cortex;

20個大腦區域:

cerebellum, where we do our skill formation;

不同的聽覺和視覺皮層區域,

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

構成不同能力的小腦,

And all of this has fueled

做理性思考的大腦等.

an increase, very smooth and predictable, of productivity.

所有的發現,

We've gone from 30 dollars to 130 dollars

以相當平穩可預測的模式，增加了生產力.

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

因為資訊科技的進步,

fueled by this information technology.

我們的工作價值從每小時30元美金

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

到每小時130元美金.

Well this is a logarithmic graph.

這還只是能源和環境的影響.

This represents a smooth doubling,

嗯, 這是一個對數圖.

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

每兩年,

particularly as we're now applying nanotechnology,

我們製造的太陽能持續倍增.

a form of information technology, to solar panels.

特別是我們現在正在運用奈米科技,

And we're only eight doublings away

一種資訊科技, 在太陽能電池板上.

from it meeting 100 percent of our energy needs.

我們現在只離我們所需要的百分之百能量

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

八次的雙倍增長.

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

而太陽能則超過我們一萬多倍的需求.

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

最後太陽能會和科技結合。時間就快到了。

What used to take up a building now fits 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 now fits in our pockets would fit in a blood cell in 25 years.

以前用掉整棟大樓資源的現在適合放進我們的口袋裡.

And we will begin to actually deeply influence

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.

our health and our intelligence,

當我們越來越接近這科技,

as we get closer and closer to this technology.

我們會真正開始左右

Based on that we are announcing, here at TED,

我們的健康跟智慧.

in true TED tradition, Singularity University.

所以我們要以TED一貫的傳統，,

It's a new university

在TED這裡宣布，我們要設立優越大學.

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

這是一所全新的大學

and myself.

由台下的聽眾，彼得‧岱爾莽第斯先生

It's backed by NASA and Google,

和我所創立.

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

它獲得美國太空總署(NASA)和Google的贊助

And our goal was to assemble the leaders,

還有其他在高科技領域的領袖們的支持.

both teachers and students,

我們的目標是召集領導人,

in these exponentially growing information technologies,

--老師和學生,

and their application.

來研究這個指數發展的資訊科技

But Larry Page made an impassioned speech

和它的用途.

at our organizing meeting,

裴基(Larry Page)先生在我們的會議上

saying we should devote this study

發表了一段熱烈的演講.

to actually addressing some of the major challenges facing humanity.

他說我們應致力研究於

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

真正解決一些人類面臨的重大挑戰.

And so that's what we've done.

假如我們做了這選擇, Google會資助我們.

The last third of the nine-week intensive summer session

所以我們做了研究上的一些改變.

will be devoted to a group project to address

在密集的九週暑期學營裡的最後三週,

some major challenge of humanity.

我們將會分組專門來提出

Like for example, applying the Internet,

一些社會上面臨的重大挑戰.

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

例如將今天已經很普及的網路,

to bringing health information

提供給中國和非洲的鄉村地區,

to developing areas of the world.

好將健康資訊

And these projects will continue past these sessions,

傳播到世界的每個發展地區.

using collaborative interactive communication.

這些科研項目會延展到這些學營外,

All the intellectual property that is created and taught

通過協作地互動溝通討論.

will be online and available,

所有萌生和傳授的智慧財產

and developed online in a collaborative fashion.

將會在網路上公開,

Here is our founding meeting.

並在網路上互相合作發展.

But this is being announced today.

這是我們的創校會議的照片.

It will be permanently headquartered in Silicon Valley,

今天我們在這裡發佈.

at the NASA Ames research center.

優越大學(Singulariy University)將會永久設置在矽谷,

There are different programs for graduate students,

在NASA的艾密斯研究中心.

for executives at different companies.

我們提供不同的課程給研究生,

The first six tracks here -- artificial intelligence,

和不同公司的高階主管.

advanced computing technologies, biotechnology, nanotechnology --

這裡的六種首要研究方向, 人工智能,

are the different core areas of information technology.

先進的電腦科技，生物科技，奈米科技

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

分別是資訊科技不同的的核心領域.

like energy, ecology,

然後我們將會將它們應用到其他領域,

policy law and ethics, entrepreneurship,

例如能源, 生態環境,

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

政策法律和道德, 企業態度,

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

使人們可以把這些新技術帶給世界.

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

我們非常感謝我們所得到,

particularly Google and NASA.

來自知識份子和高科技領導人們的支持,

This is an exciting new venture.

特別是Google和NASA.

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

這是個興奮的全新研究.

(Applause)

我們誠心地邀請你的加入. 謝謝.