TED2005
Ray Kurzweil: The accelerating power of technology
Ray Kurzweil : 科技將如何改造我們
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發明家，企業家和展望未來的夢想家 Ray Kurzweil， 以豐富合理的事例來描述，人類如何在2020年以前擁有人腦的逆向工程技術，以及可以操縱人類意念的奈米機器人。
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.
00:24
Well, it's great to be here.
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很高興能來到這裡。我們聽過一些
00:25
We've heard a lot about the promise of technology, and the peril.
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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
00:30
I've been quite interested in both.
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我個人對這兩種觀點都深感興趣
00:32
If we could convert 0.03 percent
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如果到達地球的太陽光的百分之0.03
00:36
of the sunlight that falls on the earth into energy,
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可以被轉換成能源
00:38
we could meet all of our projected needs for 2030.
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這些能源將可以滿足人類在2030 年的能源需求
00:43
We can't do that today because solar panels are heavy,
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然而，這個想法目前無法達成，理由是太陽能板既重
00:46
expensive and very inefficient.
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又昂貴，而且效率很低
00:48
There are nano-engineered designs,
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雖然還是在理論分析階段，
00:51
which at least have been analyzed theoretically,
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但是奈米工程已經設計出
00:53
that show the potential to be very lightweight,
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可以讓太陽能板變輕
00:55
very inexpensive, very efficient,
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便宜又有效率的方法
00:57
and we'd be able to actually provide all of our energy needs in this renewable way.
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這種再生能源將可以滿足人們所有的能源需求
01:01
Nano-engineered fuel cells
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而奈米燃料電池
01:03
could provide the energy where it's needed.
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也可以在任何地方提供能源
01:06
That's a key trend, which is decentralization,
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這些分散式的能源供給將成為關鍵的趨勢
01:08
moving from centralized nuclear power plants and
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從集中式的核能電廠
01:11
liquid natural gas tankers
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和液態天然瓦斯槽
01:13
to decentralized resources that are environmentally more friendly,
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轉變成分散式的天然資源。它們不僅更環保、
01:17
a lot more efficient
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效能佳
01:20
and capable and safe from disruption.
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而且能避免能源系統中斷的隱憂
01:24
Bono spoke very eloquently,
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Bono 曾明確地表示
01:26
that we have the tools, for the first time,
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疾病和貧窮的問題存在已久
01:30
to address age-old problems of disease and poverty.
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這是第一次，我們人類掌握了解決這些問題的工具
01:34
Most regions of the world are moving in that direction.
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在世界上大部分的地區也顯示出這樣的趨勢
01:38
In 1990, in East Asia and the Pacific region,
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在1990 年時，東亞及太平洋地區
01:42
there were 500 million people living in poverty --
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有五億的人口處於貧窮狀態
01:44
that number now is under 200 million.
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如今已經降至二億人以下
01:47
The World Bank projects by 2011, it will be under 20 million,
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世界銀行預期2011 年這些貧窮人口將低於二千萬
01:50
which is a reduction of 95 percent.
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也就是降低了 95%
01:53
I did enjoy Bono's comment
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我很喜歡Bono 的說法
01:56
linking Haight-Ashbury to Silicon Valley.
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他將舊金山嬉皮區 Haight-Ashbury 和加州的矽谷相比
02:00
Being from the Massachusetts high-tech community myself,
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我來自麻州的高科技園區
02:03
I'd point out that we were hippies also in the 1960s,
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我要指出我們在 1960 年代也曾經是嬉皮
02:08
although we hung around Harvard Square.
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差別只是我們是在哈佛廣場閒蕩
02:11
But we do have the potential to overcome disease and poverty,
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我們確實有能力去對抗疾病與貧窮
02:16
and I'm going to talk about those issues, if we have the will.
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只要我們有決心。這些是我將討論的主題
02:19
Kevin Kelly talked about the acceleration of technology.
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Kevin Kelly 曾探討科技的加速進展過程
02:22
That's been a strong interest of mine,
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我對這個主題有強烈的興趣
02:25
and a theme that I've developed for some 30 years.
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也研究了三十年
02:28
I realized that my technologies had to make sense when I finished a project.
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我體認到研究的成果必須有所貢獻
02:33
That invariably, the world was a different place
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然而，每當我要導入新科技時
02:36
when I would introduce a technology.
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卻發現世界已經不一樣了
02:38
And, I noticed that most inventions fail,
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我發現大部份的發明都是失敗的
02:40
not because the R&D department can't get it to work --
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並非是因為研發部門沒有達成目標
02:43
if you look at most business plans, they will actually succeed
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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
02:46
if given the opportunity to build what they say they're going to build --
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但前提是計畫要有機會依照原先設定的目標時去執行
02:50
and 90 percent of those projects or more will fail, because the timing is wrong --
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但90%甚至更多的計畫都失敗了，原因就是時機錯誤
02:53
not all the enabling factors will be in place when they're needed.
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在需要時總會欠缺一些關鍵性的成功因素
02:56
So I began to be an ardent student of technology trends,
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我像個熱切的學生，研究起科技的趨勢
03:00
and track where technology would be at different points in time,
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我追蹤在什麼時間點，科技會呈現什麼面貌
03:03
and began to build the mathematical models of that.
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並建立起它的數學模型，
03:06
It's kind of taken on a life of its own.
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把整個科技發展的過程呈現出來
03:08
I've got a group of 10 people that work with me to gather data
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我的團隊有十個人，我們蒐集資料
03:11
on key measures of technology in many different areas, and we build models.
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看一些關鍵的科技如何運在各個領域，然後建立模型
03:16
And you'll hear people say, well, we can't predict the future.
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你會聽到人們說，”我們是不可能預測未來的”
03:19
And if you ask me,
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如果你問我
03:21
will the price of Google be higher or lower than it is today three years from now,
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三年後Google 的股價會上升還是下跌？
03:24
that's very hard to say.
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那真的很難預測
03:26
Will WiMax CDMA G3
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WiMax CDMA G3
03:29
be the wireless standard three years from now? That's hard to say.
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會成為無線協定嗎？這也很難說
03:31
But if you ask me, what will it cost
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但是，如果你問我
03:33
for one MIPS of computing in 2010,
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2010年時，一個計算用的MIPS 會值多少錢？
03:36
or the cost to sequence a base pair of DNA in 2012,
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或是在2012年，DNA一基本對的序列的成本是多少？
03:39
or the cost of sending a megabyte of data wirelessly in 2014,
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或是無線傳送百萬位元在2014 年要花費多少？
03:43
it turns out that those are very predictable.
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這些問題就很容易預測了
03:46
There are remarkably smooth exponential curves
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性能價格比，處理容量與頻寬間
03:48
that govern price performance, capacity, bandwidth.
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呈現非常平滑的指數曲線關係
03:51
And I'm going to show you a small sample of this,
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我給你們看一個小範例
03:53
but there's really a theoretical reason
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它顯示出理論上
03:55
why technology develops in an exponential fashion.
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科技是以指數模式在發展
04:00
And a lot of people, when they think about the future, think about it linearly.
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但多數人卻是用線性的模式在預測未來
04:02
They think they're going to continue
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他們以為
04:04
to develop a problem
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處理或解決一個難題
04:06
or address a problem using today's tools,
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只能用現有的工具
04:09
at today's pace of progress,
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和現有的步調
04:11
and fail to take into consideration this exponential growth.
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卻忽略到了指數型成長的因素
04:15
The Genome Project was a controversial project in 1990.
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基因組計畫在 1990 年時是個很受爭議的計畫
04:18
We had our best Ph.D. students,
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雖然擁有最好的博士班學生、
04:20
our most advanced equipment around the world,
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世界上最先進的儀器
04:22
we got 1/10,000th of the project done,
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卻只完成了計畫的萬分之一
04:24
so how're we going to get this done in 15 years?
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那怎麼可能在15 年內完成這個計畫？
04:26
And 10 years into the project,
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十年過去了
04:30
the skeptics were still going strong -- says, "You're two-thirds through this project,
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人們的質疑依舊強烈。他們說：計畫已經過了 2/3
04:32
and you've managed to only sequence
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但只勉強地完成了
04:34
a very tiny percentage of the whole genome."
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很少部份的基因組序列
04:37
But it's the nature of exponential growth
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然而，這正是指數型成長的特性
04:39
that once it reaches the knee of the curve, it explodes.
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一但到達曲線彎曲點，它就一躍而上
04:41
Most of the project was done in the last
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計畫的大部份都在是在最後幾年才完成的
04:43
few years of the project.
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幾年才完成的
04:45
It took us 15 years to sequence HIV --
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HIV 愛滋病毒的序列耗費了15 年
04:47
we sequenced SARS in 31 days.
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但我們在31 天內就完成 SARS 的序列
04:49
So we are gaining the potential to overcome these problems.
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所以，我們是有能力去克服這些問題的
04:53
I'm going to show you just a few examples
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我給你看一些例子
04:55
of how pervasive this phenomena is.
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來證明這樣的現象是很普遍的。根據我們的模型，
04:58
The actual paradigm-shift rate, the rate of adopting new ideas,
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實際的典範轉移率 - 採用新觀念的比例
05:02
is doubling every decade, according to our models.
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每十年就呈倍數成長
05:05
These are all logarithmic graphs,
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這些都是對數的圖形
05:08
so as you go up the levels it represents, generally multiplying by factor of 10 or 100.
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在達到相對的程度後，通常會以十倍速或百倍的速度變化
05:11
It took us half a century to adopt the telephone,
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第一個虛擬實境技術－電話
05:14
the first virtual-reality technology.
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花了半個世紀的時間，才開始普及
05:17
Cell phones were adopted in about eight years.
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但是手機只花了八年就被普遍使用
05:19
If you put different communication technologies
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將不同的通訊科技
05:22
on this logarithmic graph,
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放在這個對數圖表上
05:24
television, radio, telephone
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會發現電視、收音機跟電話的普及過程
05:26
were adopted in decades.
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都要花上數十年的時間
05:28
Recent technologies -- like the PC, the web, cell phones --
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而新科技，像是電腦，網路跟手機
05:31
were under a decade.
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在十年內就被廣泛接納了
05:33
Now this is an interesting chart,
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這個圖表很有意思
05:35
and this really gets at the fundamental reason why
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他說明了演化過程的基本原理
05:37
an evolutionary process -- and both biology and technology are evolutionary processes --
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無論是生物演化或是科技演化
05:41
accelerate.
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都是以加速度進行的
05:43
They work through interaction -- they create a capability,
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透過交互作用，他們創造能力
05:46
and then it uses that capability to bring on the next stage.
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再用這個能力來改變下個階段
05:49
So the first step in biological evolution,
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生物演化的第一步
05:52
the evolution of DNA -- actually it was RNA came first --
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就是DNA 的演化，實際上是從 RNA開始的
05:54
took billions of years,
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這個歷程歷經數十億年
05:56
but then evolution used that information-processing backbone
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在這個已形成的資訊處理的架構下
05:59
to bring on the next stage.
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演化持續推展至下一個階段
06:01
So the Cambrian Explosion, when all the body plans of the animals were evolved,
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所以在寒武紀大爆發時，動物的身體結構
06:04
took only 10 million years. It was 200 times faster.
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在一千萬年之間就建構完成。足足快了兩百倍
06:08
And then evolution used those body plans
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接著，演化在這已身體架構上
06:10
to evolve higher cognitive functions,
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建構出更高階的認知功能
06:12
and biological evolution kept accelerating.
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生物的演化持續地加速進行
06:14
It's an inherent nature of an evolutionary process.
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這就是演化與生俱來的天性
06:17
So Homo sapiens, the first technology-creating species,
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第一個具備創造科技能力的物種－智人
06:20
the species that combined a cognitive function
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已經結合了認知的功能
06:22
with an opposable appendage --
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以及可以與四指相對的拇指
06:24
and by the way, chimpanzees don't really have a very good opposable thumb --
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順便一提，大猩猩的拇指無法很好的與其他四指相對
06:28
so we could actually manipulate our environment with a power grip
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我們因為具備很強的握力和細緻的操控力
06:30
and fine motor coordination,
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所以才能對抗環境
06:32
and use our mental models to actually change the world
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同時運用我們的心智來改變世界
06:34
and bring on technology.
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並發展科技
06:36
But anyway, the evolution of our species took hundreds of thousands of years,
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總而言之，物種的演化花了數十萬年
06:39
and then working through interaction,
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然後透過交互影響和演化的作用
06:41
evolution used, essentially,
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和演化的作用
06:43
the technology-creating species to bring on the next stage,
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這個能創造科技的物種已經可以帶來新階段的發展了
06:46
which were the first steps in technological evolution.
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這個階段就是科技演化的第一步
06:49
And the first step took tens of thousands of years --
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而這一步僅花了數千年
06:52
stone tools, fire, the wheel -- kept accelerating.
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從石製工具到輪軸，變化持續加速著
06:55
We always used then the latest generation of technology
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我們總是用上一階段的科技
06:57
to create the next generation.
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來創造下一階段
06:59
Printing press took a century to be adopted;
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印刷科技花了一個世紀才普及
07:01
the first computers were designed pen-on-paper -- now we use computers.
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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
07:05
And we've had a continual acceleration of this process.
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我們正在持續加速這樣的過程，順便一提
07:08
Now by the way, if you look at this on a linear graph, it looks like everything has just happened,
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你觀察這個線性圖形，似乎是每件事情都才剛剛發生
07:11
but some observer says, "Well, Kurzweil just put points on this graph
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於是有些觀察家說” 喔 Kurzweil 只不過是把一些點放在圖表上
07:17
that fall on that straight line."
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然後，剛好變成一條直線而已
07:19
So, I took 15 different lists from key thinkers,
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所以，我列出十五份重要思想家的名單
07:22
like the Encyclopedia Britannica, the Museum of Natural History, Carl Sagan's Cosmic Calendar
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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
07:26
on the same -- and these people were not trying to make my point;
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這些人並沒有要為我的觀點背書
07:29
these were just lists in reference works,
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他們都選自參考文獻中的作者列表
07:31
and I think that's what they thought the key events were
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我想他們也會認同重要的關鍵在
07:34
in biological evolution and technological evolution.
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生物演化和科技演化
07:37
And again, it forms the same straight line. You have a little bit of thickening in the line
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再一次地，這些都形成了直線。你看到一些
07:40
because people do have disagreements, what the key points are,
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較粗的直線，是因為人們對於關鍵點有些疑義
07:43
there's differences of opinion when agriculture started,
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像是農業開始發展的時間點
07:45
or how long the Cambrian Explosion took.
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或是寒武紀到底持續多久
07:48
But you see a very clear trend.
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然而，這個趨勢卻是相當顯著的
07:50
There's a basic, profound acceleration of this evolutionary process.
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這個演化的加速過程是根本且深遠的
07:55
Information technologies double their capacity, price performance, bandwidth,
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在資訊科技界，容量、性能價格比和頻寬
08:00
every year.
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每年都加倍成長
08:02
And that's a very profound explosion of exponential growth.
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這就指數型態的爆炸性成長
08:06
A personal experience, when I was at MIT --
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以我個人的經驗，當年我在麻省理工時
08:08
computer taking up about the size of this room,
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電腦大約是一個房間的大小
08:10
less powerful than the computer in your cell phone.
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性能也比不上你們現在的手機
08:15
But Moore's Law, which is very often identified with this exponential growth,
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摩爾定律的概念和這個指數成長的概念非常相似
08:19
is just one example of many, because it's basically
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但也只是眾多例子中的一個
08:21
a property of the evolutionary process of technology.
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基本上，它只是科技演化發展的基本特性之一
08:26
I put 49 famous computers on this logarithmic graph --
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如果我們將49 台著名的電腦放到這個對數圖表上
08:29
by the way, a straight line on a logarithmic graph is exponential growth --
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順便一提，這個對數圖表上的線是指數成長的
08:33
that's another exponential.
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這是另一個指數型的範例
08:35
It took us three years to double our price performance of computing in 1900,
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在1900年，電腦的性能價格比花了三年才提升一倍
08:38
two years in the middle; we're now doubling it every one year.
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中間的兩年，現在我們每年都可以提升一倍
08:42
And that's exponential growth through five different paradigms.
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這五個不同的範例都顯示了指數型態的增長
08:45
Moore's Law was just the last part of that,
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摩爾的定律只說明了這個定律的後半部
08:47
where we were shrinking transistors on an integrated circuit,
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也就是說在積體電路的發展中，電晶體的尺寸不斷地縮減
08:50
but we had electro-mechanical calculators,
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但我們是在經歷過電子機械式的計算機
08:53
relay-based computers that cracked the German Enigma Code,
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取代德國密碼機的繼電器型電腦
08:55
vacuum tubes in the 1950s predicted the election of Eisenhower,
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1950 年代就能預測艾森豪選舉的真空管電腦
08:59
discreet transistors used in the first space flights
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用於首次太空飛行的分立電晶體之後
09:02
and then Moore's Law.
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才有了摩爾定律
09:04
Every time one paradigm ran out of steam,
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每當一個範例的發展到了限度
09:06
another paradigm came out of left field to continue the exponential growth.
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另一個範例就接著進入指數成長期
09:09
They were shrinking vacuum tubes, making them smaller and smaller.
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真空管尺寸被縮小，更小還要再小
09:12
That hit a wall. They couldn't shrink them and keep the vacuum.
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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
09:15
Whole different paradigm -- transistors came out of the woodwork.
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全新型態的電晶體開始崛起
09:17
In fact, when we see the end of the line for a particular paradigm,
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事實上，每當一種例子到達發展的頂端時
09:20
it creates research pressure to create the next paradigm.
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就是新產品的研發的壓力
09:24
And because we've been predicting the end of Moore's Law
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長期以來，我們一直在預測後摩爾定律時代的降臨
09:27
for quite a long time -- the first prediction said 2002, until now it says 2022.
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一開始預測是2002 年，現在又說是2012 年
09:30
But by the teen years,
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在10 年內
09:33
the features of transistors will be a few atoms in width,
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電晶體的寬度就會變得跟幾個原子的寬度一樣
09:36
and we won't be able to shrink them any more.
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已經沒有辦法再被縮小
09:38
That'll be the end of Moore's Law, but it won't be the end of
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這是摩爾定律的結束
09:41
the exponential growth of computing, because chips are flat.
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但不是運算指數型態成長的結束。因為晶片是平的
09:43
We live in a three-dimensional world; we might as well use the third dimension.
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而我們處在三度的立體空間，我們可以利用第三度空間
09:46
We will go into the third dimension
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我們將會走入第三度空間
09:48
and there's been tremendous progress, just in the last few years,
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並獲得極大的進展，就像我們過去幾年一樣
09:51
of getting three-dimensional, self-organizing molecular circuits to work.
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我們將完成在三度空間的自組式的分子電路。
09:55
We'll have those ready well before Moore's Law runs out of steam.
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在摩爾定律到達極限前，這些科技就會準備好
10:02
Supercomputers -- same thing.
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同樣的事情也曾發生在超級電腦上
10:05
Processor performance on Intel chips,
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英代爾的處理器上
10:08
the average price of a transistor --
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電晶體的平均價格
10:11
1968, you could buy one transistor for a dollar.
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在1968 年是一美金一個電晶體
10:14
You could buy 10 million in 2002.
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在 2002 年時，同樣的價格可以買到一千萬個
10:17
It's pretty remarkable how smooth
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這個指數發展的過程
10:20
an exponential process that is.
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顯得如此平順
10:22
I mean, you'd think this is the result of some tabletop experiment,
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以至於被認為這只是實驗桌上做出來的實驗數據
10:26
but this is the result of worldwide chaotic behavior --
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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
10:29
countries accusing each other of dumping products,
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包括國際間互相指責傾銷
10:31
IPOs, bankruptcies, marketing programs.
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公開募股、破產及行銷策略
10:33
You would think it would be a very erratic process,
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這些通常被認為是沒有章法的過程
10:36
and you have a very smooth
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然而這混亂的過程卻形成了
10:38
outcome of this chaotic process.
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一個相當平順的結果
10:40
Just as we can't predict
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就像，我們也許無法預測
10:42
what one molecule in a gas will do --
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一個氣體內的分子的行為
10:44
it's hopeless to predict a single molecule --
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預測單一分子是不可能的
10:47
yet we can predict the properties of the whole gas,
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然而，我們卻可以用熱電學
10:49
using thermodynamics, very accurately.
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非常準確地預測氣體的整體特性
10:52
It's the same thing here. We can't predict any particular project,
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同樣地，我們無法預測單一特定的計畫
10:55
but the result of this whole worldwide,
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然而這整個世界
10:57
chaotic, unpredictable activity of competition
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這些混亂又無法預測的競爭行為
11:02
and the evolutionary process of technology is very predictable.
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還有這個科技演化的過程卻都是可以預期的
11:05
And we can predict these trends far into the future.
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而且，我們得到的這個趨勢也適用於未來
11:10
Unlike Gertrude Stein's roses,
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和格特鲁德•斯泰因的玫瑰不同，
11:12
it's not the case that a transistor is a transistor.
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電晶體不僅僅只是一個電晶體
11:14
As we make them smaller and less expensive,
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當我們讓它變小變便宜之後
11:16
the electrons have less distance to travel.
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電子間移動的距離變小了
11:18
They're faster, so you've got exponential growth in the speed of transistors,
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它們變的更快，所以在電晶體的速度上就呈現了指數型進展。
11:22
so the cost of a cycle of one transistor
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電晶體的周期成本
11:26
has been coming down with a halving rate of 1.1 years.
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在1.1年內下降到一半
11:29
You add other forms of innovation and processor design,
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加上其他形式的發明跟處理器設計
11:32
you get a doubling of price performance of computing every one year.
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電腦產品的性能價格比每年都提升一倍
11:36
And that's basically deflation --
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這是最基本的通貨緊縮
11:39
50 percent deflation.
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- 50百分比的通貨緊縮
11:41
And it's not just computers. I mean, it's true of DNA sequencing;
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這不僅僅是發生在電腦產業。也發生在DNA序列上
11:44
it's true of brain scanning;
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在大腦掃描上
11:46
it's true of the World Wide Web. I mean, anything that we can quantify,
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在網際網路上也都有同樣的情形。任何可以被量化的東西
11:48
we have hundreds of different measurements
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數百種的指標
11:51
of different, information-related measurements --
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和資訊相關的指標
11:54
capacity, adoption rates --
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無論容量或是採用率
11:56
and they basically double every 12, 13, 15 months,
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依照項目的相異，它們分別以每隔12,13,15 個月
11:59
depending on what you're looking at.
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就加倍的速度成長
12:01
In terms of price performance, that's a 40 to 50 percent deflation rate.
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至於性能價格比，則是呈現50- 約40-50 的緊縮幅度
12:06
And economists have actually started worrying about that.
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經濟學家已經開始擔心這個現象
12:08
We had deflation during the Depression,
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大蕭條時期我們曾經歷過經濟緊縮
12:10
but that was collapse of the money supply,
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但是那是導因於貨幣供給系統的崩潰
12:12
collapse of consumer confidence, a completely different phenomena.
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它也摧毀了消費者信心，是截然不同的現象
12:15
This is due to greater productivity,
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這次則是因為生產力大增所致
12:18
but the economist says, "But there's no way you're going to be able to keep up with that.
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但是經濟學家依舊認為：”我們不可能跟得上這個變化的腳步
12:20
If you have 50 percent deflation, people may increase their volume
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當物價有50% 的通貨緊縮
12:23
30, 40 percent, but they won't keep up with it."
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人們就會增加 30%-40% 的消費，人們不可能一直跟得上這個變化”
12:25
But what we're actually seeing is that
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可是，事實顯示
12:27
we actually more than keep up with it.
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我們不僅跟上這個變化
12:29
We've had 28 percent per year compounded growth in dollars
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在過去50 年，花在資訊科技上的消費
12:32
in information technology over the last 50 years.
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還呈現了28%的複合性成長
12:35
I mean, people didn't build iPods for 10,000 dollars 10 years ago.
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我的意思是，10 年前，沒有人會花一萬美金去買ipod
12:39
As the price performance makes new applications feasible,
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但是當性能價格提升到某種程度
12:42
new applications come to the market.
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新發明的應用就會很合理而進入市場
12:44
And this is a very widespread phenomena.
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這現象非常廣泛
12:47
Magnetic data storage --
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雖然不適用摩爾定律
12:49
that's not Moore's Law, it's shrinking magnetic spots,
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但是在磁記錄媒體方面，磁點的尺寸也正持續縮減中
12:52
different engineers, different companies, same exponential process.
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相異的工程師與相異的公司，都依循相同的指數模式在進展
12:56
A key revolution is that we're understanding our own biology
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另一個關鍵性的變革是我們開始運用資訊科技
13:00
in these information terms.
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來解讀生物學
13:02
We're understanding the software programs
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我們正在學習
13:04
that make our body run.
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讓我們身體運作的軟體
13:06
These were evolved in very different times --
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這些軟體是在不同的時期逐漸發展起來的
13:08
we'd like to actually change those programs.
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我們卻想要改變身體運作的程式
13:10
One little software program, called the fat insulin receptor gene,
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有個小軟體程式叫做脂肪胰島素受體基因
13:12
basically says, "Hold onto every calorie,
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基本上，它發出的訊息是：”維持住卡洛里
13:14
because the next hunting season may not work out so well."
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因為下一個狩獵季可能什麼都獵不到”
13:18
That was in the interests of the species tens of thousands of years ago.
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在數萬年前，這個機能上是對物種有益的
13:21
We'd like to actually turn that program off.
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現在，我們想關掉這個機能
13:24
They tried that in animals, and these mice ate ravenously
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我們在動物上實驗，讓老鼠們大口大口的吃，
13:27
and remained slim and got the health benefits of being slim.
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卻能保持苗條。因為體態輕盈而老鼠還保持了健康
13:29
They didn't get diabetes; they didn't get heart disease;
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沒有糖尿病，沒有心臟病
13:32
they lived 20 percent longer; they got the health benefits of caloric restriction
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牠們甚至延長了20% 的年紀。要限制熱量攝取才能得到的健康
13:35
without the restriction.
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這些老鼠無需限制熱量也依舊保有
13:37
Four or five pharmaceutical companies have noticed this,
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四到五家的製藥公司注意到這一點
13:40
felt that would be
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他們覺得
13:43
interesting drug for the human market,
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這對人類的市場將會是個有趣的藥品
13:46
and that's just one of the 30,000 genes
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而這只不過是影響我們生物化學的3萬個基因
13:48
that affect our biochemistry.
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其中的一個
13:51
We were evolved in an era where it wasn't in the interests of people
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我們所處的世代，並不是為了
13:54
at the age of most people at this conference, like myself,
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讓那些與參加這會議的大多數人相似年紀的人，例如我本人
13:57
to live much longer, because we were using up the precious resources
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活得更長久而考量。因為我們正在耗盡人類的珍貴資源
14:01
which were better deployed towards the children
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這些資源原本是預留給我們的下一代的兒童
14:02
and those caring for them.
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和那些珍惜資源的人
14:04
So, life -- long lifespans --
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超過三十歲
14:06
like, that is to say, much more than 30 --
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的長壽生命
14:08
weren't selected for,
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並不是自然界物競天擇的結果
14:11
but we are learning to actually manipulate
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而是由於我們在生物科技革命中
14:14
and change these software programs
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已經學到如何操縱
14:16
through the biotechnology revolution.
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並改變這些軟體的技能
14:18
For example, we can inhibit genes now with RNA interference.
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舉例來說，我們已經懂得用RNA干擾去抑制基因
14:22
There are exciting new forms of gene therapy
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新型態的基因治療法令人雀躍，
14:24
that overcome the problem of placing the genetic material
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它們已經能成功地
14:26
in the right place on the chromosome.
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將遺傳物質置於正確的染色體位置
14:28
There's actually a -- for the first time now,
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這是第一次，基因治療
14:31
something going to human trials, that actually cures pulmonary hypertension --
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真的在人體試驗中治癒了肺動脈高血壓
14:34
a fatal disease -- using gene therapy.
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這種致命的疾病
14:37
So we'll have not just designer babies, but designer baby boomers.
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所以我們不僅有訂造的嬰兒，還會有訂造的嬰兒潮
14:40
And this technology is also accelerating.
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目前這個科技也在加速中
14:43
It cost 10 dollars per base pair in 1990,
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1990 年基因複製時鹼基的成本是10 美金
14:46
then a penny in 2000.
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到2000年時只要一分錢
14:48
It's now under a 10th of a cent.
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現在則是一分錢的十分之一
14:50
The amount of genetic data --
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基因資料的數量
14:52
basically this shows that smooth exponential growth
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也顯示出每年增加一倍
14:55
doubled every year,
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的指數型成長
14:57
enabling the genome project to be completed.
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促成基因組計畫的實現
15:00
Another major revolution: the communications revolution.
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另一個重大的革命就是通訊革命
15:03
The price performance, bandwidth, capacity of communications measured many different ways;
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用通訊的性能價格比、頻寬和容量可以顯示出不同層次的進展
15:08
wired, wireless is growing exponentially.
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有線和無線通訊的數量都是以指數型式增長
15:11
The Internet has been doubling in power and continues to,
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在耗用的電力和其他方面的數據
15:14
measured many different ways.
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也都顯示網際網路的發展已經增加一倍
15:16
This is based on the number of hosts.
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這圖表是以主機的數量為基準
15:18
Miniaturization -- we're shrinking the size of technology
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微型化 - 科技產品的尺寸
15:20
at an exponential rate,
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正以指數的倍率縮小
15:22
both wired and wireless.
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無論是有線或無線。
15:24
These are some designs from Eric Drexler's book --
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德萊思勒書中有一些設計
15:28
which we're now showing are feasible
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經過超級電腦的模擬
15:30
with super-computing simulations,
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已經證明是合理可行的
15:32
where actually there are scientists building
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科學家們已經開始製造
15:34
molecule-scale robots.
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分子機器人
15:36
One has one that actually walks with a surprisingly human-like gait,
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其中一具分子機器人甚至可以用人類的步伐行走
15:38
that's built out of molecules.
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甚至可以用人類的步伐行走
15:41
There are little machines doing things in experimental bases.
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實驗室裡的小機器也有了實用的機能
15:45
The most exciting opportunity
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最令人興奮的是
15:48
is actually to go inside the human body
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機器人已經可以進入人體
15:50
and perform therapeutic and diagnostic functions.
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進行治療跟診斷
15:53
And this is less futuristic than it may sound.
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聽起來像是遙遠未來才能實現的功能其實並不遙遠
15:55
These things have already been done in animals.
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有些已經運用在動物身上了
15:57
There's one nano-engineered device that cures type 1 diabetes. It's blood cell-sized.
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有種奈米工程的裝置可以治療第一型糖尿病，大小和血球相近
16:01
They put tens of thousands of these
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它已經在老鼠上進行實驗。數萬個這種裝置
16:03
in the blood cell -- they tried this in rats --
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被放於血球中
16:05
it lets insulin out in a controlled fashion,
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它們控制胰島素以適當的速度釋放
16:07
and actually cures type 1 diabetes.
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以治療第一型的糖尿病
16:09
What you're watching is a design
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這是人造紅血球
16:12
of a robotic red blood cell,
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的其中一種
16:14
and it does bring up the issue that our biology
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這類人造的紅血球引發新的議論
16:16
is actually very sub-optimal,
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雖然生物的構造已錯綜複雜
16:18
even though it's remarkable in its intricacy.
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但並非處在最佳狀態
16:21
Once we understand its principles of operation,
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一旦我們了解這個準則
16:24
and the pace with which we are reverse-engineering biology is accelerating,
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而生物學的逆向工程也加速進展
16:28
we can actually design these things to be
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比現今功能強數千倍的能力
16:30
thousands of times more capable.
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都可能達成
16:32
An analysis of this respirocyte, designed by Rob Freitas,
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一個針對Freitas博士設計的人造红血球的分析指出
16:37
indicates if you replace 10 percent of your red blood cells with these robotic versions,
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如果以人造紅血球取代人體血液中的紅血球的10%
16:40
you could do an Olympic sprint for 15 minutes without taking a breath.
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你可以在奧運比賽中可以連續衝刺15 分鐘而不用換上一口氣
16:43
You could sit at the bottom of your pool for four hours --
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或是在游泳池底連續坐四小時
16:46
so, "Honey, I'm in the pool," will take on a whole new meaning.
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當你說＂親愛的，我現在在游泳池＂，可能表示了一種全新的意義
16:50
It will be interesting to see what we do in our Olympic trials.
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人們可以在奧運會的選拔賽做出什麼樣的表現呢，這將會變的很有趣
16:52
Presumably we'll ban them,
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可以預見地，這種人工紅血球會被禁止
16:54
but then we'll have the specter of teenagers in their high schools gyms
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但是，青少年怪傑將不斷地出現，他們在學校體育館中
16:56
routinely out-performing the Olympic athletes.
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就可以創下奧運紀錄
17:01
Freitas has a design for a robotic white blood cell.
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Freitas博士也設計了人造白血球
17:04
These are 2020-circa scenarios,
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以上是預計2020 年左右會發生的劇情
17:08
but they're not as futuristic as it may sound.
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雖然很像遙遠未來的故事，但事實並非如此
17:10
There are four major conferences on building blood cell-sized devices;
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已經有四場主要的會議在討論製造這類血球大小的裝置
17:14
there are many experiments in animals.
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也進行了許多動物試驗
17:16
There's actually one going into human trial,
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有一個已經進行人體試驗
17:18
so this is feasible technology.
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所以這種科技是非常可行的
17:22
If we come back to our exponential growth of computing,
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以計算能力的指數型成長來看
17:24
1,000 dollars of computing is now somewhere between an insect and a mouse brain.
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現今1000 美元計算機的功能大約介於昆蟲或是老鼠的大腦
17:27
It will intersect human intelligence
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以儲存容量來看
17:30
in terms of capacity in the 2020s,
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大約2020 年左右會接近人類的智慧
17:33
but that'll be the hardware side of the equation.
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但這裡指的是硬體方面的比較
17:35
Where will we get the software?
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那麼相近於人腦的軟體該從哪裡取得呢？
17:37
Well, it turns out we can see inside the human brain,
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我們必須先來分析人腦的內部
17:39
and in fact not surprisingly,
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事實並不太令人意外
17:41
the spatial and temporal resolution of brain scanning is doubling every year.
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目前我們在腦部掃描的空間分辨力和瞬時分辨力每年都提升一倍
17:45
And with the new generation of scanning tools,
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有了新一代的掃瞄儀器
17:47
for the first time we can actually see
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第一次我們看到了
17:49
individual inter-neural fibers
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個別的神經間的纖維
17:51
and see them processing and signaling in real time --
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還即時地看到它們是如何的處理和傳送訊息
17:54
but then the question is, OK, we can get this data now,
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是的，我們現在已經可以取得資料了
17:56
but can we understand it?
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但是問題是我們能理解這些資料嗎？
17:58
Doug Hofstadter wonders, well, maybe our intelligence
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Doug Hofstadter 曾經懷疑：也許以人類的智慧
18:01
just isn't great enough to understand our intelligence,
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是無法去了解人類的智慧的
18:04
and if we were smarter, well, then our brains would be that much more complicated,
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因為當我們更聰明後，大腦的構造也會變得更複雜
18:07
and we'd never catch up to it.
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所以，我們永遠追不上大腦的進展
18:10
It turns out that we can understand it.
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但結果證明，我們已經能了解大腦了
18:13
This is a block diagram of
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這個方塊圖是個模型
18:16
a model and simulation of the human auditory cortex
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它在模擬人類大腦聽覺皮質上
18:20
that actually works quite well --
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有很好的表現
18:22
in applying psychoacoustic tests, gets very similar results to human auditory perception.
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在聽覺心理學測驗中，它和人類聽覺的結果非常類似
18:26
There's another simulation of the cerebellum --
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另外，也有個小腦的模擬圖
18:29
that's more than half the neurons in the brain --
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小腦涵蓋了人腦半數以上的神經元
18:31
again, works very similarly to human skill formation.
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它和人類在技能構成的運作非常類似
18:35
This is at an early stage, but you can show
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雖然現在是在發展的初期階段
18:38
with the exponential growth of the amount of information about the brain
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但在與大腦的相關的資訊量已經呈現指數成長
18:41
and the exponential improvement
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腦部掃描的分辨力上
18:43
in the resolution of brain scanning,
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也有指數型的改進
18:45
we will succeed in reverse-engineering the human brain
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在2020 年代以前
18:48
by the 2020s.
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人類大腦的逆向工程會有所成果
18:50
We've already had very good models and simulation of about 15 regions
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在腦部的數百個區域中，其中15個
18:53
out of the several hundred.
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已經有了非常好的模型和模擬
18:56
All of this is driving
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所有這些都會導向
18:58
exponentially growing economic progress.
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指數型的經濟成長
19:00
We've had productivity go from 30 dollars to 150 dollars per hour
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過去50 年，在勞工產值上已經從每位勞工每小時30 美金
19:05
of labor in the last 50 years.
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提升到150 美金
19:07
E-commerce has been growing exponentially. It's now a trillion dollars.
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電子商務也顯示指數型的成長。現在已經是上兆元的產業
19:10
You might wonder, well, wasn't there a boom and a bust?
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你也許會想問，它不是發生有過繁榮期跟泡沫化嗎？
19:12
That was strictly a capital-markets phenomena.
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這其實是資本市場的現象
19:14
Wall Street noticed that this was a revolutionary technology, which it was,
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當時華爾街察覺到這會是個革命性的科技，它確實是
19:18
but then six months later, when it hadn't revolutionized all business models,
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但是六個月後，它沒有讓所有的商業模式都產生革命性變革時
19:21
they figured, well, that was wrong,
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人們想，糟了
19:23
and then we had this bust.
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然後，泡沫化就發生了
19:26
All right, this is a technology
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好的。在這種科技裡
19:28
that we put together using some of the technologies we're involved in.
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融合運用了目前正在發展中的科技
19:31
This will be a routine feature in a cell phone.
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這會成為手機的標準功能
19:35
It would be able to translate from one language to another.
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它能將一種語言翻譯成另一種語言
19:47
So let me just end with a couple of scenarios.
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我將以一些遠景做為結尾
19:49
By 2010 computers will disappear.
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2010 年前，電腦即將消失
19:53
They'll be so small, they'll be embedded in our clothing, in our environment.
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它們變得非常微小，以致於它們被植入在衣服和環境當中
19:56
Images will be written directly to our retina,
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影像被直接寫在我們的視網膜上
19:58
providing full-immersion virtual reality,
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提供沉浸式的虛擬實境
20:00
augmented real reality. We'll be interacting with virtual personalities.
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真實感增加。我們也可以和虛擬人物互動
20:04
But if we go to 2029, we really have the full maturity of these trends,
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如果前往 2029 年，到那時，這些趨勢已臻成熟
20:08
and you have to appreciate how many turns of the screw
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你感念這些科技產生的過程，它們都曾歷經數次大轉折
20:11
in terms of generations of technology, which are getting faster and faster, we'll have at that point.
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而且愈變愈快的轉折終究才成功的
20:15
I mean, we will have two-to-the-25th-power
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性能比、容量和頻寬
20:17
greater price performance, capacity and bandwidth
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是現在的2 到25 倍
20:20
of these technologies, which is pretty phenomenal.
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這是相當驚人的成就
20:22
It'll be millions of times more powerful than it is today.
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它比目前的科技強大百萬倍
20:24
We'll have completed the reverse-engineering of the human brain,
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我們將完成人類大腦的逆向工程
20:27
1,000 dollars of computing will be far more powerful
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就一般的容量來比
20:30
than the human brain in terms of basic raw capacity.
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一千美金的計算機將比人腦的功能更加強大
20:34
Computers will combine
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電腦會結合
20:36
the subtle pan-recognition powers
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人類智慧所擁有的細微的全辨識功能
20:38
of human intelligence with ways in which machines are already superior,
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加上機器原本就優於人腦-的項目
20:41
in terms of doing analytic thinking,
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例如：處理分析思考
20:43
remembering billions of facts accurately.
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與正確地記憶數十億的論據的方面
20:45
Machines can share their knowledge very quickly.
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機器更可以快速的分享知識
20:47
But it's not just an alien invasion of intelligent machines.
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智慧型機器不只像是外星人入侵
20:52
We are going to merge with our technology.
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還會和我們的科技結合
20:54
These nano-bots I mentioned
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我提及的這些奈米機器人
20:56
will first be used for medical and health applications:
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將首次被用在醫藥和健康的應用上。
21:00
cleaning up the environment, providing powerful fuel cells
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清理環境，提供能源－像是強大的燃料電池
21:03
and widely distributed decentralized solar panels and so on in the environment.
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和分佈很廣的分散式的太陽能板，等諸如此類的應用
21:08
But they'll also go inside our brain,
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它們也會走入我們的大腦中
21:10
interact with our biological neurons.
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和我們的生物神經元產生交互作用
21:12
We've demonstrated the key principles of being able to do this.
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我們已經證明了可以達成這個目標的關鍵性原理
21:15
So, for example,
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舉例來說
21:17
full-immersion virtual reality from within the nervous system,
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在與神經系統結合的沉浸式虛擬實境中
21:19
the nano-bots shut down the signals coming from your real senses,
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奈米機器人會及阻斷我們真實感受到的訊息
21:22
replace them with the signals that your brain would be receiving
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取而代之的是假定你在虛擬的環境下所該收到的訊息
21:25
if you were in the virtual environment,
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所該收到的訊息
21:27
and then it'll feel like you're in that virtual environment.
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大腦收到這樣的訊息，所以它感覺你是真實地存在虛擬世界裡
21:29
You can go there with other people, have any kind of experience
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你可以和他人一同前往虛擬世界，所有這些感官產生的經驗
21:31
with anyone involving all of the senses.
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都可以和他人共享
21:34
"Experience beamers," I call them, will put their whole flow of sensory experiences
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我稱它為”經驗傳送器”`。情感對應的神經所產生的感官經驗
21:37
in the neurological correlates of their emotions out on the Internet.
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會被放在網際網路上
21:40
You can plug in and experience what it's like to be someone else.
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只要連上它們，就能體驗另一個人的感覺
21:43
But most importantly,
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但最重要的是
21:45
it'll be a tremendous expansion
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透過這種和科技的直接合併
21:47
of human intelligence through this direct merger with our technology,
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人類的智慧會急遽地擴展
21:51
which in some sense we're doing already.
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就某些層面而言，我們已經在進行了
21:53
We routinely do intellectual feats
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有了科技的協助
21:55
that would be impossible without our technology.
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人類才能不時地展現出智慧的成就
21:57
Human life expectancy is expanding. It was 37 in 1800,
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人類的預期壽命不斷地延長，在 1800 年時是37歲
22:00
and with this sort of biotechnology, nano-technology revolutions,
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隨著這類的生化科技與奈米科技革命的發展
22:05
this will move up very rapidly
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預期壽命會在未來幾年
22:07
in the years ahead.
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快速的增長
22:09
My main message is that progress in technology
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我要傳達的重點是科技的進步
22:13
is exponential, not linear.
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是指數型的，不是線型的
22:16
Many -- even scientists -- assume a linear model,
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很多人，甚至是科學家，常以線型模型來預期未來的發展
22:20
so they'll say, "Oh, it'll be hundreds of years
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所以，他們才會認為 “要花上數百年
22:22
before we have self-replicating nano-technology assembly
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我們才能發展出具備自我複製能力的奈米科技組裝
22:25
or artificial intelligence."
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或是人工智慧”
22:27
If you really look at the power of exponential growth,
472
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但如果你看到指數型成長的力量
22:30
you'll see that these things are pretty soon at hand.
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你會預期這些事將在不久後實現
22:33
And information technology is increasingly encompassing
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資訊科技會持續地擴展到
22:36
all of our lives, from our music to our manufacturing
475
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生活的各個層面，從音樂到生產製造
22:40
to our biology to our energy to materials.
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生物、能源以及材料
22:44
We'll be able to manufacture almost anything we need in the 2020s,
477
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在 2020 年代
22:47
from information, in very inexpensive raw materials,
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有了資訊科技，再加上便宜的原料
22:49
using nano-technology.
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以及奈米科技，我們幾乎能製造出所有的產品
22:52
These are very powerful technologies.
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這些有影響力的科技
22:54
They both empower our promise and our peril.
481
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不僅能帶來美好未來，也可能導致悲慘命運
22:58
So we have to have the will to apply them to the right problems.
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所以，我們必須有決心，確保它們只能用在正確的方向上
23:01
Thank you very much.
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非常感謝
23:02
(Applause)
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(掌聲)
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