We've heard a lot about the promise of technology, and the peril.

關於科技可以讓生活更美好的承諾，也有人說它會引發災難

I've been quite interested in both.

我個人對這兩種觀點都深感興趣

If we could convert 0.03 percent

如果到達地球的太陽光的百分之0.03

of the sunlight that falls on the earth into energy,

可以被轉換成能源

we could meet all of our projected needs for 2030.

這些能源將可以滿足人類在2030 年的能源需求

We can't do that today because solar panels are heavy,

然而，這個想法目前無法達成，理由是太陽能板既重

expensive and very inefficient.

又昂貴，而且效率很低

There are nano-engineered designs,

雖然還是在理論分析階段，

which at least have been analyzed theoretically,

但是奈米工程已經設計出

that show the potential to be very lightweight,

可以讓太陽能板變輕

very inexpensive, very efficient,

便宜又有效率的方法

and we'd be able to actually provide all of our energy needs in this renewable way.

這種再生能源將可以滿足人們所有的能源需求

Nano-engineered fuel cells

而奈米燃料電池

could provide the energy where it's needed.

也可以在任何地方提供能源

That's a key trend, which is decentralization,

這些分散式的能源供給將成為關鍵的趨勢

moving from centralized nuclear power plants and

從集中式的核能電廠

liquid natural gas tankers

和液態天然瓦斯槽

to decentralized resources that are environmentally more friendly,

轉變成分散式的天然資源。它們不僅更環保、

a lot more efficient

效能佳

and capable and safe from disruption.

而且能避免能源系統中斷的隱憂

Bono spoke very eloquently,

Bono 曾明確地表示

that we have the tools, for the first time,

疾病和貧窮的問題存在已久

to address age-old problems of disease and poverty.

這是第一次，我們人類掌握了解決這些問題的工具

Most regions of the world are moving in that direction.

在世界上大部分的地區也顯示出這樣的趨勢

In 1990, in East Asia and the Pacific region,

在1990 年時，東亞及太平洋地區

there were 500 million people living in poverty --

有五億的人口處於貧窮狀態

that number now is under 200 million.

如今已經降至二億人以下

The World Bank projects by 2011, it will be under 20 million,

世界銀行預期2011 年這些貧窮人口將低於二千萬

which is a reduction of 95 percent.

也就是降低了 95%

I did enjoy Bono's comment

我很喜歡Bono 的說法

linking Haight-Ashbury to Silicon Valley.

他將舊金山嬉皮區 Haight-Ashbury 和加州的矽谷相比

Being from the Massachusetts high-tech community myself,

我來自麻州的高科技園區

I'd point out that we were hippies also in the 1960s,

我要指出我們在 1960 年代也曾經是嬉皮

although we hung around Harvard Square.

差別只是我們是在哈佛廣場閒蕩

But we do have the potential to overcome disease and poverty,

我們確實有能力去對抗疾病與貧窮

and I'm going to talk about those issues, if we have the will.

只要我們有決心。這些是我將討論的主題

Kevin Kelly talked about the acceleration of technology.

Kevin Kelly 曾探討科技的加速進展過程

That's been a strong interest of mine,

我對這個主題有強烈的興趣

and a theme that I've developed for some 30 years.

也研究了三十年

I realized that my technologies had to make sense when I finished a project.

我體認到研究的成果必須有所貢獻

That invariably, the world was a different place

然而，每當我要導入新科技時

when I would introduce a technology.

卻發現世界已經不一樣了

And, I noticed that most inventions fail,

我發現大部份的發明都是失敗的

not because the R&D department can't get it to work --

並非是因為研發部門沒有達成目標

if you look at most business plans, they will actually succeed

如果你去分析，會看到大部份的商業計畫實際上能達成目標

if given the opportunity to build what they say they're going to build --

但前提是計畫要有機會依照原先設定的目標時去執行

and 90 percent of those projects or more will fail, because the timing is wrong --

但90%甚至更多的計畫都失敗了，原因就是時機錯誤

not all the enabling factors will be in place when they're needed.

在需要時總會欠缺一些關鍵性的成功因素

So I began to be an ardent student of technology trends,

我像個熱切的學生，研究起科技的趨勢

and track where technology would be at different points in time,

我追蹤在什麼時間點，科技會呈現什麼面貌

and began to build the mathematical models of that.

並建立起它的數學模型，

It's kind of taken on a life of its own.

把整個科技發展的過程呈現出來

I've got a group of 10 people that work with me to gather data

我的團隊有十個人，我們蒐集資料

on key measures of technology in many different areas, and we build models.

看一些關鍵的科技如何運在各個領域，然後建立模型

And you'll hear people say, well, we can't predict the future.

你會聽到人們說，”我們是不可能預測未來的”

And if you ask me,

如果你問我

will the price of Google be higher or lower than it is today three years from now,

三年後Google 的股價會上升還是下跌？

that's very hard to say.

那真的很難預測

Will WiMax CDMA G3

WiMax CDMA G3

be the wireless standard three years from now? That's hard to say.

會成為無線協定嗎？這也很難說

But if you ask me, what will it cost

但是，如果你問我

for one MIPS of computing in 2010,

2010年時，一個計算用的MIPS 會值多少錢？

or the cost to sequence a base pair of DNA in 2012,

或是在2012年，DNA一基本對的序列的成本是多少？

or the cost of sending a megabyte of data wirelessly in 2014,

或是無線傳送百萬位元在2014 年要花費多少？

it turns out that those are very predictable.

這些問題就很容易預測了

There are remarkably smooth exponential curves

性能價格比，處理容量與頻寬間

that govern price performance, capacity, bandwidth.

呈現非常平滑的指數曲線關係

And I'm going to show you a small sample of this,

我給你們看一個小範例

but there's really a theoretical reason

它顯示出理論上

why technology develops in an exponential fashion.

科技是以指數模式在發展

And a lot of people, when they think about the future, think about it linearly.

但多數人卻是用線性的模式在預測未來

They think they're going to continue

他們以為

to develop a problem

處理或解決一個難題

or address a problem using today's tools,

只能用現有的工具

at today's pace of progress,

和現有的步調

and fail to take into consideration this exponential growth.

卻忽略到了指數型成長的因素

The Genome Project was a controversial project in 1990.

基因組計畫在 1990 年時是個很受爭議的計畫

We had our best Ph.D. students,

雖然擁有最好的博士班學生、

our most advanced equipment around the world,

世界上最先進的儀器

we got 1/10,000th of the project done,

卻只完成了計畫的萬分之一

so how're we going to get this done in 15 years?

那怎麼可能在15 年內完成這個計畫？

And 10 years into the project,

十年過去了

the skeptics were still going strong -- says, "You're two-thirds through this project,

人們的質疑依舊強烈。他們說：計畫已經過了 2/3

and you've managed to only sequence

但只勉強地完成了

a very tiny percentage of the whole genome."

很少部份的基因組序列

But it's the nature of exponential growth

然而，這正是指數型成長的特性

that once it reaches the knee of the curve, it explodes.

一但到達曲線彎曲點，它就一躍而上

Most of the project was done in the last

計畫的大部份都在是在最後幾年才完成的

few years of the project.

幾年才完成的

It took us 15 years to sequence HIV --

HIV 愛滋病毒的序列耗費了15 年

we sequenced SARS in 31 days.

但我們在31 天內就完成 SARS 的序列

So we are gaining the potential to overcome these problems.

所以，我們是有能力去克服這些問題的

I'm going to show you just a few examples

我給你看一些例子

of how pervasive this phenomena is.

來證明這樣的現象是很普遍的。根據我們的模型，

The actual paradigm-shift rate, the rate of adopting new ideas,

實際的典範轉移率 - 採用新觀念的比例

is doubling every decade, according to our models.

每十年就呈倍數成長

These are all logarithmic graphs,

這些都是對數的圖形

so as you go up the levels it represents, generally multiplying by factor of 10 or 100.

在達到相對的程度後，通常會以十倍速或百倍的速度變化

It took us half a century to adopt the telephone,

第一個虛擬實境技術－電話

the first virtual-reality technology.

花了半個世紀的時間，才開始普及

Cell phones were adopted in about eight years.

但是手機只花了八年就被普遍使用

If you put different communication technologies

將不同的通訊科技

on this logarithmic graph,

放在這個對數圖表上

television, radio, telephone

會發現電視、收音機跟電話的普及過程

were adopted in decades.

都要花上數十年的時間

Recent technologies -- like the PC, the web, cell phones --

而新科技，像是電腦，網路跟手機

were under a decade.

在十年內就被廣泛接納了

Now this is an interesting chart,

這個圖表很有意思

and this really gets at the fundamental reason why

他說明了演化過程的基本原理

an evolutionary process -- and both biology and technology are evolutionary processes --

無論是生物演化或是科技演化

accelerate.

都是以加速度進行的

They work through interaction -- they create a capability,

透過交互作用，他們創造能力

and then it uses that capability to bring on the next stage.

再用這個能力來改變下個階段

So the first step in biological evolution,

生物演化的第一步

the evolution of DNA -- actually it was RNA came first --

就是DNA 的演化，實際上是從 RNA開始的

took billions of years,

這個歷程歷經數十億年

but then evolution used that information-processing backbone

在這個已形成的資訊處理的架構下

to bring on the next stage.

演化持續推展至下一個階段

So the Cambrian Explosion, when all the body plans of the animals were evolved,

所以在寒武紀大爆發時，動物的身體結構

took only 10 million years. It was 200 times faster.

在一千萬年之間就建構完成。足足快了兩百倍

And then evolution used those body plans

接著，演化在這已身體架構上

to evolve higher cognitive functions,

建構出更高階的認知功能

and biological evolution kept accelerating.

生物的演化持續地加速進行

It's an inherent nature of an evolutionary process.

這就是演化與生俱來的天性

So Homo sapiens, the first technology-creating species,

第一個具備創造科技能力的物種－智人

the species that combined a cognitive function

已經結合了認知的功能

with an opposable appendage --

以及可以與四指相對的拇指

and by the way, chimpanzees don't really have a very good opposable thumb --

順便一提，大猩猩的拇指無法很好的與其他四指相對

so we could actually manipulate our environment with a power grip

我們因為具備很強的握力和細緻的操控力

and fine motor coordination,

所以才能對抗環境

and use our mental models to actually change the world

同時運用我們的心智來改變世界

and bring on technology.

並發展科技

But anyway, the evolution of our species took hundreds of thousands of years,

總而言之，物種的演化花了數十萬年

and then working through interaction,

然後透過交互影響和演化的作用

evolution used, essentially,

和演化的作用

the technology-creating species to bring on the next stage,

這個能創造科技的物種已經可以帶來新階段的發展了

which were the first steps in technological evolution.

這個階段就是科技演化的第一步

And the first step took tens of thousands of years --

而這一步僅花了數千年

stone tools, fire, the wheel -- kept accelerating.

從石製工具到輪軸，變化持續加速著

We always used then the latest generation of technology

我們總是用上一階段的科技

to create the next generation.

來創造下一階段

Printing press took a century to be adopted;

印刷科技花了一個世紀才普及

the first computers were designed pen-on-paper -- now we use computers.

第一台電腦是靠筆和紙設計出來的。而現今電腦變成我們的工具

And we've had a continual acceleration of this process.

我們正在持續加速這樣的過程，順便一提

Now by the way, if you look at this on a linear graph, it looks like everything has just happened,

你觀察這個線性圖形，似乎是每件事情都才剛剛發生

but some observer says, "Well, Kurzweil just put points on this graph

於是有些觀察家說” 喔 Kurzweil 只不過是把一些點放在圖表上

that fall on that straight line."

然後，剛好變成一條直線而已

So, I took 15 different lists from key thinkers,

所以，我列出十五份重要思想家的名單

like the Encyclopedia Britannica, the Museum of Natural History, Carl Sagan's Cosmic Calendar

名單選自大英百科全書、自然歷史博物館，卡爾沙根的宇宙日曆

on the same -- and these people were not trying to make my point;

這些人並沒有要為我的觀點背書

these were just lists in reference works,

他們都選自參考文獻中的作者列表

and I think that's what they thought the key events were

我想他們也會認同重要的關鍵在

in biological evolution and technological evolution.

生物演化和科技演化

And again, it forms the same straight line. You have a little bit of thickening in the line

再一次地，這些都形成了直線。你看到一些

because people do have disagreements, what the key points are,

較粗的直線，是因為人們對於關鍵點有些疑義

there's differences of opinion when agriculture started,

像是農業開始發展的時間點

or how long the Cambrian Explosion took.

或是寒武紀到底持續多久

But you see a very clear trend.

然而，這個趨勢卻是相當顯著的

There's a basic, profound acceleration of this evolutionary process.

這個演化的加速過程是根本且深遠的

Information technologies double their capacity, price performance, bandwidth,

在資訊科技界，容量、性能價格比和頻寬

every year.

每年都加倍成長

And that's a very profound explosion of exponential growth.

這就指數型態的爆炸性成長

A personal experience, when I was at MIT --

以我個人的經驗，當年我在麻省理工時

computer taking up about the size of this room,

電腦大約是一個房間的大小

less powerful than the computer in your cell phone.

性能也比不上你們現在的手機

But Moore's Law, which is very often identified with this exponential growth,

摩爾定律的概念和這個指數成長的概念非常相似

is just one example of many, because it's basically

但也只是眾多例子中的一個

a property of the evolutionary process of technology.

基本上，它只是科技演化發展的基本特性之一

I put 49 famous computers on this logarithmic graph --

如果我們將49 台著名的電腦放到這個對數圖表上

by the way, a straight line on a logarithmic graph is exponential growth --

順便一提，這個對數圖表上的線是指數成長的

that's another exponential.

這是另一個指數型的範例

It took us three years to double our price performance of computing in 1900,

在1900年，電腦的性能價格比花了三年才提升一倍

two years in the middle; we're now doubling it every one year.

中間的兩年，現在我們每年都可以提升一倍

And that's exponential growth through five different paradigms.

這五個不同的範例都顯示了指數型態的增長

Moore's Law was just the last part of that,

摩爾的定律只說明了這個定律的後半部

where we were shrinking transistors on an integrated circuit,

也就是說在積體電路的發展中，電晶體的尺寸不斷地縮減

but we had electro-mechanical calculators,

但我們是在經歷過電子機械式的計算機

relay-based computers that cracked the German Enigma Code,

取代德國密碼機的繼電器型電腦

vacuum tubes in the 1950s predicted the election of Eisenhower,

1950 年代就能預測艾森豪選舉的真空管電腦

discreet transistors used in the first space flights

用於首次太空飛行的分立電晶體之後

and then Moore's Law.

才有了摩爾定律

Every time one paradigm ran out of steam,

每當一個範例的發展到了限度

another paradigm came out of left field to continue the exponential growth.

另一個範例就接著進入指數成長期

They were shrinking vacuum tubes, making them smaller and smaller.

真空管尺寸被縮小，更小還要再小

That hit a wall. They couldn't shrink them and keep the vacuum.

到達一個瓶頸後，當真空管不能再更小了，我們就放棄真空管

Whole different paradigm -- transistors came out of the woodwork.

全新型態的電晶體開始崛起

In fact, when we see the end of the line for a particular paradigm,

事實上，每當一種例子到達發展的頂端時

it creates research pressure to create the next paradigm.

就是新產品的研發的壓力

And because we've been predicting the end of Moore's Law

長期以來，我們一直在預測後摩爾定律時代的降臨

for quite a long time -- the first prediction said 2002, until now it says 2022.

一開始預測是2002 年，現在又說是2012 年

But by the teen years,

在10 年內

the features of transistors will be a few atoms in width,

電晶體的寬度就會變得跟幾個原子的寬度一樣

and we won't be able to shrink them any more.

已經沒有辦法再被縮小

That'll be the end of Moore's Law, but it won't be the end of

這是摩爾定律的結束

the exponential growth of computing, because chips are flat.

但不是運算指數型態成長的結束。因為晶片是平的

We live in a three-dimensional world; we might as well use the third dimension.

而我們處在三度的立體空間，我們可以利用第三度空間

We will go into the third dimension

我們將會走入第三度空間

and there's been tremendous progress, just in the last few years,

並獲得極大的進展，就像我們過去幾年一樣

of getting three-dimensional, self-organizing molecular circuits to work.

我們將完成在三度空間的自組式的分子電路。

We'll have those ready well before Moore's Law runs out of steam.

在摩爾定律到達極限前，這些科技就會準備好

Supercomputers -- same thing.

同樣的事情也曾發生在超級電腦上

Processor performance on Intel chips,

英代爾的處理器上

the average price of a transistor --

電晶體的平均價格

1968, you could buy one transistor for a dollar.

在1968 年是一美金一個電晶體

You could buy 10 million in 2002.

在 2002 年時，同樣的價格可以買到一千萬個

It's pretty remarkable how smooth

這個指數發展的過程

an exponential process that is.

顯得如此平順

I mean, you'd think this is the result of some tabletop experiment,

以至於被認為這只是實驗桌上做出來的實驗數據

but this is the result of worldwide chaotic behavior --

但這分析的資料其實來自發生在世界各地的各種混沌行為

countries accusing each other of dumping products,

包括國際間互相指責傾銷

IPOs, bankruptcies, marketing programs.

公開募股、破產及行銷策略

You would think it would be a very erratic process,

這些通常被認為是沒有章法的過程

and you have a very smooth

然而這混亂的過程卻形成了

outcome of this chaotic process.

一個相當平順的結果

Just as we can't predict

就像，我們也許無法預測

what one molecule in a gas will do --

一個氣體內的分子的行為

it's hopeless to predict a single molecule --

預測單一分子是不可能的

yet we can predict the properties of the whole gas,

然而，我們卻可以用熱電學

using thermodynamics, very accurately.

非常準確地預測氣體的整體特性

It's the same thing here. We can't predict any particular project,

同樣地，我們無法預測單一特定的計畫

but the result of this whole worldwide,

然而這整個世界

chaotic, unpredictable activity of competition

這些混亂又無法預測的競爭行為

and the evolutionary process of technology is very predictable.

還有這個科技演化的過程卻都是可以預期的

And we can predict these trends far into the future.

而且，我們得到的這個趨勢也適用於未來

Unlike Gertrude Stein's roses,

和格特鲁德•斯泰因的玫瑰不同，

it's not the case that a transistor is a transistor.

電晶體不僅僅只是一個電晶體