TED2005
Ray Kurzweil: The accelerating power of technology
Ray Kurzweil 科技将如何改造我们
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发明家，创业家以及梦想家 Ray Kurzweil 用丰富合理的内容来解释为什么到2020年时代，我们将已经反策划了人的大脑；纳米机器人将会在你的意识里运转。
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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好，很高兴来到这里。
00:25
We've heard a lot about the promise of technology, and the peril.
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我们听到了很多关于科技的承诺，和未来的隐患。
00:30
I've been quite interested in both.
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我一直对这两者都很感兴趣。
00:32
If we could convert 0.03 percent
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如果我们将落在地球上的阳光的0.03%
00:36
of the sunlight that falls on the earth into energy,
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转化成能量，
00:38
we could meet all of our projected needs for 2030.
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预计将可以满足我们2030年的能源需求。
00:43
We can't do that today because solar panels are heavy,
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现在我们还不能，因为今天的太阳能板笨重，
00:46
expensive and very inefficient.
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昂贵，且效率很低。
00:48
There are nano-engineered designs,
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不过现在有纳米工程设计的太阳板，
00:51
which at least have been analyzed theoretically,
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它们从理论分析上来说
00:53
that show the potential to be very lightweight,
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可以变得很轻
00:55
very inexpensive, very efficient,
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很便宜，且效率很高。
00:57
and we'd be able to actually provide all of our energy needs in this renewable way.
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而且我们将可以用这种可再生的方法提供我们所需的能量。
01:01
Nano-engineered fuel cells
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纳米工程燃料电池
01:03
could provide the energy where it's needed.
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可以为需要的地方提供能量。
01:06
That's a key trend, which is decentralization,
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分散式分布是一个关键的趋势
01:08
moving from centralized nuclear power plants and
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从集中式的核电厂，
01:11
liquid natural gas tankers
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液体天然气储存储罐
01:13
to decentralized resources that are environmentally more friendly,
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到分散分布的能源会更加环保，
01:17
a lot more efficient
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效率更高
01:20
and capable and safe from disruption.
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且在灾难中更加安全。
01:24
Bono spoke very eloquently,
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Bono 雄辩地指出
01:26
that we have the tools, for the first time,
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我们第一次使用工具
01:30
to address age-old problems of disease and poverty.
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来对待疾病和贫困这些古老的问题。
01:34
Most regions of the world are moving in that direction.
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世界上大部分地区已经朝那个方向前进。
01:38
In 1990, in East Asia and the Pacific region,
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1990年，在东亚和太平洋地区，
01:42
there were 500 million people living in poverty --
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有5亿人生活在贫困里-
01:44
that number now is under 200 million.
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这个数字现在是2亿。
01:47
The World Bank projects by 2011, it will be under 20 million,
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世界银行预测在2011年，这个数字将会在2000万以下。
01:50
which is a reduction of 95 percent.
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下降了95％。
01:53
I did enjoy Bono's comment
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我很喜欢 Bono 的观点
01:56
linking Haight-Ashbury to Silicon Valley.
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把嬉皮区和硅谷连在一起。
02:00
Being from the Massachusetts high-tech community myself,
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作为马萨诸塞州的高科技社区的一员
02:03
I'd point out that we were hippies also in the 1960s,
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我要指出我们也曾经是1960年时代的嬉皮，
02:08
although we hung around Harvard Square.
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尽管我们是在哈佛广场附近活动。
02:11
But we do have the potential to overcome disease and poverty,
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但是我们拥有克服疾病和贫困的潜力。
02:16
and I'm going to talk about those issues, if we have the will.
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而且我要说一说这些问题，如果我们有这个愿望的话。
02:19
Kevin Kelly talked about the acceleration of technology.
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Kevin Kelly 说到了关于科技的加速
02:22
That's been a strong interest of mine,
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我一直对这很感兴趣，
02:25
and a theme that I've developed for some 30 years.
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而且这个主题是我30年来一直在研究的。
02:28
I realized that my technologies had to make sense when I finished a project.
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我意识到在我完成我的项目时，这些技术要有意义。
02:33
That invariably, the world was a different place
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在这个始终不变的前提下，每当我引进一个技术时
02:36
when I would introduce a technology.
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世界已经不再是原来的世界了。
02:38
And, I noticed that most inventions fail,
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还有，我发现大部分的发明失败了，
02:40
not because the R&D department can't get it to work --
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不是因为研发部门不能让它运作 --
02:43
if you look at most business plans, they will actually succeed
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如果你看一看大部分的企业计划书，他们其实是会成功的
02:46
if given the opportunity to build what they say they're going to build --
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如果给他们机会让他们建造计划要建造的东西，
02:50
and 90 percent of those projects or more will fail, because the timing is wrong --
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而其百分之90的这些项目会失败，原因是时机不对－－
02:53
not all the enabling factors will be in place when they're needed.
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不是所有成功所需的因素都会在需要它们时出现。
02:56
So I began to be an ardent student of technology trends,
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因此，我成为一个对技术发展趋势很热衷的学生，
03:00
and track where technology would be at different points in time,
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并关注在不同的时间点，科技将会变成什么样子，
03:03
and began to build the mathematical models of that.
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并且开始建造其数学模型。
03:06
It's kind of taken on a life of its own.
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这个项目已经形成了一个自己的生命，
03:08
I've got a group of 10 people that work with me to gather data
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我有一个10人的小组和我一起来收集数据，
03:11
on key measures of technology in many different areas, and we build models.
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这些数据是反映不同领域科技的重要指标，并据此，我们建造模型。
03:16
And you'll hear people say, well, we can't predict the future.
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然后你会听到有人说，我们不能预测未来。
03:19
And if you ask me,
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且如果你问我，
03:21
will the price of Google be higher or lower than it is today three years from now,
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谷歌的股价3年后会比今天高还是低，
03:24
that's very hard to say.
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那是很难说。
03:26
Will WiMax CDMA G3
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WiMax CDMA G3 会不会
03:29
be the wireless standard three years from now? That's hard to say.
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成为3年后无线领域的标准？那很难说。
03:31
But if you ask me, what will it cost
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但是如果你问我，在2010年
03:33
for one MIPS of computing in 2010,
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每秒百万次计算的成本
03:36
or the cost to sequence a base pair of DNA in 2012,
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或者一个DNA碱基对的排序在2012年的成本，
03:39
or the cost of sending a megabyte of data wirelessly in 2014,
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或者是在2014年无线发送一兆字节数据的成本，
03:43
it turns out that those are very predictable.
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这些东西是非常可以预测的。
03:46
There are remarkably smooth exponential curves
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这些有十分平滑的指数曲线
03:48
that govern price performance, capacity, bandwidth.
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来反应性价比，容量和带宽。
03:51
And I'm going to show you a small sample of this,
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我要给你看一个这个的小例子，
03:53
but there's really a theoretical reason
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但是这里其实有一个理论上的原因
03:55
why technology develops in an exponential fashion.
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为什么科技在一个指数形势发展。
04:00
And a lot of people, when they think about the future, think about it linearly.
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有很多人，当他们考虑到未来，用线性的方法来思想。
04:02
They think they're going to continue
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他们认为他们会持续
04:04
to develop a problem
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发展一个问题
04:06
or address a problem using today's tools,
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或者用今天的工具，
04:09
at today's pace of progress,
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和今天的发展速度来诠释未来的问题，
04:11
and fail to take into consideration this exponential growth.
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但是没有考虑到指数的发展模式。
04:15
The Genome Project was a controversial project in 1990.
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基因组计划曾经在1990年是一个有争议的项目。
04:18
We had our best Ph.D. students,
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我们有我们最好的博士学生，
04:20
our most advanced equipment around the world,
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世界各地最先进的设备，
04:22
we got 1/10,000th of the project done,
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在世界范围呢，我们完成了项目的万分之一，
04:24
so how're we going to get this done in 15 years?
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那么，我们怎么能在15年里完成这个项目呢？
04:26
And 10 years into the project,
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在这个项目进展10年的时候，
04:30
the skeptics were still going strong -- says, "You're two-thirds through this project,
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怀疑的态度还是非常的强大 -- 说“你已经进入到这个项目的三分之二了，
04:32
and you've managed to only sequence
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而你仅仅完成了
04:34
a very tiny percentage of the whole genome."
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整个基因组工程非常小部分的排序。
04:37
But it's the nature of exponential growth
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但是这是指数增长的本质
04:39
that once it reaches the knee of the curve, it explodes.
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当到了曲线的转折点时，它会爆炸。
04:41
Most of the project was done in the last
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大部分的项目是在
04:43
few years of the project.
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项目的最后几年完成的。
04:45
It took us 15 years to sequence HIV --
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我们用了15年完成了艾滋病毒的排序 --
04:47
we sequenced SARS in 31 days.
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而对于非典病毒只用了31天。
04:49
So we are gaining the potential to overcome these problems.
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所以我们正在增加克服这些困难的可能性。
04:53
I'm going to show you just a few examples
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我要给你看几个例子
04:55
of how pervasive this phenomena is.
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说明这个现象是多么的普遍。
04:58
The actual paradigm-shift rate, the rate of adopting new ideas,
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根据我们的模型，事实的思维转化率，也就是新想法被接受的速率，
05:02
is doubling every decade, according to our models.
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每十几年增加一倍。
05:05
These are all logarithmic graphs,
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这些都是对数图，
05:08
so as you go up the levels it represents, generally multiplying by factor of 10 or 100.
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就好比每当你提高它代表的一个等级，一般来讲会乘以10或者100。
05:11
It took us half a century to adopt the telephone,
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我们用了半个世纪来采用电话，
05:14
the first virtual-reality technology.
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第一个虚拟现实的科技。
05:17
Cell phones were adopted in about eight years.
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只用了8年就接受了手机。
05:19
If you put different communication technologies
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如果你把不同的通信科技放在
05:22
on this logarithmic graph,
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这个对数图上，
05:24
television, radio, telephone
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电视，收音机，电话
05:26
were adopted in decades.
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都用了几十年才被采用。
05:28
Recent technologies -- like the PC, the web, cell phones --
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最近的科技 -- 像电脑，网络，手机 --
05:31
were under a decade.
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是十年以下。
05:33
Now this is an interesting chart,
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这是一个有意思的图表，
05:35
and this really gets at the fundamental reason why
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而其这个通道最基本的原因为什么
05:37
an evolutionary process -- and both biology and technology are evolutionary processes --
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一个进化过程 -- 生物学和科技都是进化过程 --
05:41
accelerate.
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加速。
05:43
They work through interaction -- they create a capability,
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他们是一种互动的运转 -- 他们创造一个能力，
05:46
and then it uses that capability to bring on the next stage.
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然后用那个能力来推进到下一个层次。
05:49
So the first step in biological evolution,
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生物进化的第一步，
05:52
the evolution of DNA -- actually it was RNA came first --
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DNA的进化 -- 其实是先有的RNA --
05:54
took billions of years,
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用了几十亿年，
05:56
but then evolution used that information-processing backbone
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但是以后的进化过程是用这个信息处理支柱
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to bring on the next stage.
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来促使下一个层次。
06:01
So the Cambrian Explosion, when all the body plans of the animals were evolved,
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所以寒武纪大爆发，当所有动物的身体结构进化了
06:04
took only 10 million years. It was 200 times faster.
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用了才一千万年。快了200倍。
06:08
And then evolution used those body plans
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然后进化过程用这些身体结构
06:10
to evolve higher cognitive functions,
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来进化出更高级的认知功能，
06:12
and biological evolution kept accelerating.
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而且生物进化一直在加速。
06:14
It's an inherent nature of an evolutionary process.
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这是一个进化过程固有的性质。
06:17
So Homo sapiens, the first technology-creating species,
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所以智人，第一个创造科技的物种，
06:20
the species that combined a cognitive function
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把认知功能
06:22
with an opposable appendage --
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和大拇指运动结合的物种 --
06:24
and by the way, chimpanzees don't really have a very good opposable thumb --
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顺便提一下，黑猩猩其实没有一个非常好的大拇指 --
06:28
so we could actually manipulate our environment with a power grip
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所以我们可以用很强的握力来操纵我们的环境
06:30
and fine motor coordination,
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和好的动作协调，
06:32
and use our mental models to actually change the world
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和用我们的心智模式来真正改变世界
06:34
and bring on technology.
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而且带来科技。
06:36
But anyway, the evolution of our species took hundreds of thousands of years,
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但是总而言之，我们这个物种的进化用了几十万年，
06:39
and then working through interaction,
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然后通过互动的运转,
06:41
evolution used, essentially,
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从本质上来讲，进化运用
06:43
the technology-creating species to bring on the next stage,
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这种科技来创造下一代物种,
06:46
which were the first steps in technological evolution.
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这是科技进化的第一步。
06:49
And the first step took tens of thousands of years --
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而且这第一步用了几万年 --
06:52
stone tools, fire, the wheel -- kept accelerating.
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石器，火，和轮子 - 一直加速。
06:55
We always used then the latest generation of technology
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我们一直用当时最新一代的科技
06:57
to create the next generation.
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来创造下一代。
06:59
Printing press took a century to be adopted;
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印刷机用了一个世纪来被采用，
07:01
the first computers were designed pen-on-paper -- now we use computers.
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第一个电脑是用笔和纸来设计的 - 现在我们用电脑来设计。
07:05
And we've had a continual acceleration of this process.
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我们在这个过程是不断加速的。
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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顺便说一下，如果你在一个线性图上看这个，好像所有的东西顺其自然地发生，
07:11
but some observer says, "Well, Kurzweil just put points on this graph
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但是一次观察者说，“嗯, Kurzweil 是有意把这些点
07:17
that fall on that straight line."
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放在了这个直线图上。”
07:19
So, I took 15 different lists from key thinkers,
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所以，一共用了15个不同重要思想家的列表，
07:22
like the Encyclopedia Britannica, the Museum of Natural History, Carl Sagan's Cosmic Calendar
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像大英百科全书，自然历史博物馆，卡尔萨根的宇宙日历
07:26
on the same -- and these people were not trying to make my point;
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而这些人并没有试着证明我的观点，
07:29
these were just lists in reference works,
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他们只是列举参考文献。
07:31
and I think that's what they thought the key events were
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我想这就是他们所认为的在生物进化和科技进化中
07:34
in biological evolution and technological evolution.
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的关键事件。
07:37
And again, it forms the same straight line. You have a little bit of thickening in the line
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再次，它形成相同的直线。
07:40
because people do have disagreements, what the key points are,
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因为人们有不同的意见，关于什么是要点
07:43
there's differences of opinion when agriculture started,
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人们对农业什么时候开始的有着不同的意见，
07:45
or how long the Cambrian Explosion took.
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或者什么时候 -- 寒武纪大爆发用了多长时间。
07:48
But you see a very clear trend.
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但是有一个非常明显的趋势。
07:50
There's a basic, profound acceleration of this evolutionary process.
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进化过程有一个基本的，深奥的加速。
07:55
Information technologies double their capacity, price performance, bandwidth,
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信息技术的能力，性价比，带宽，
08:00
every year.
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每年增加一倍。
08:02
And that's a very profound explosion of exponential growth.
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这是一个非常深奥的指数增长爆炸。
08:06
A personal experience, when I was at MIT --
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一个个人的经验，当我在麻省理工学院-
08:08
computer taking up about the size of this room,
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计算机大概是这个房间大，
08:10
less powerful than the computer in your cell phone.
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性能比你手机里的电脑还弱。
08:15
But Moore's Law, which is very often identified with this exponential growth,
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但是根据摩尔定律，经常和这个成倍增一起认定，
08:19
is just one example of many, because it's basically
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只是很多例子里的一个，因为它基本是
08:21
a property of the evolutionary process of technology.
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科技进化过程的一个性质。
08:26
I put 49 famous computers on this logarithmic graph --
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如果我们-我把49个著名的电脑放在这个对数图上-
08:29
by the way, a straight line on a logarithmic graph is exponential growth --
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顺便说一下，一条直线在一个对数图，是成倍增 -
08:33
that's another exponential.
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那是另一个成倍。
08:35
It took us three years to double our price performance of computing in 1900,
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我们用了三年把1900年的计算的性价比翻倍。
08:38
two years in the middle; we're now doubling it every one year.
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中间是两年，我们现在每一年增加一倍。
08:42
And that's exponential growth through five different paradigms.
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这是通过5种不同模式的成倍增。
08:45
Moore's Law was just the last part of that,
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摩尔定律只是最后的部分，
08:47
where we were shrinking transistors on an integrated circuit,
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在一个积体电路，被缩小的晶体管，
08:50
but we had electro-mechanical calculators,
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但我们有机电计算器，
08:53
relay-based computers that cracked the German Enigma Code,
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继电器为基础的计算机破译了德国的密码，
08:55
vacuum tubes in the 1950s predicted the election of Eisenhower,
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真空管在上世纪50年代预测到艾森豪威尔的当选，
08:59
discreet transistors used in the first space flights
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首次太空飞行使用的离散晶体管
09:02
and then Moore's Law.
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然后是摩尔定律。
09:04
Every time one paradigm ran out of steam,
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每当一个范例被用尽了，
09:06
another paradigm came out of left field to continue the exponential growth.
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另一个范例从左外野出来继续这个成倍增长。
09:09
They were shrinking vacuum tubes, making them smaller and smaller.
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他们曾经缩小真空管，使他们越来越小。
09:12
That hit a wall. They couldn't shrink them and keep the vacuum.
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这撞上了墙。他们无法继续收缩并保留真空。
09:15
Whole different paradigm -- transistors came out of the woodwork.
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完全不同的范例-木工出来的晶体管。
09:17
In fact, when we see the end of the line for a particular paradigm,
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事实上，当我们看到一个特定范例的结束线时，
09:20
it creates research pressure to create the next paradigm.
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它会创建研究的压力来创造下一个的范例。
09:24
And because we've been predicting the end of Moore's Law
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而且因为我们一直在预测摩尔定律终点
09:27
for quite a long time -- the first prediction said 2002, until now it says 2022.
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用了相当长的时间-第一次预测说2002年，到现在它说2022年。
09:30
But by the teen years,
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但是到了23世纪，
09:33
the features of transistors will be a few atoms in width,
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晶体管的特点将会是几个原子的宽度
09:36
and we won't be able to shrink them any more.
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我们将无法继续把它缩小。
09:38
That'll be the end of Moore's Law, but it won't be the end of
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这将结束摩尔定律，但这不会结束
09:41
the exponential growth of computing, because chips are flat.
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计算的倍数增长，因为芯片是平的。
09:43
We live in a three-dimensional world; we might as well use the third dimension.
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我们生活在一个三维的世界，我们也应该利用第三纬。
09:46
We will go into the third dimension
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我们将会走入第三纬
09:48
and there's been tremendous progress, just in the last few years,
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而且它已经在最近几年有了惊人的进展，
09:51
of getting three-dimensional, self-organizing molecular circuits to work.
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包括运用三维的，自组织分子电路来工作。
09:55
We'll have those ready well before Moore's Law runs out of steam.
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我们将会在莫尔定律走到尽头以前准备好。
10:02
Supercomputers -- same thing.
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超级计算机也是一样。
10:05
Processor performance on Intel chips,
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以英特尔处理器的性能为例，
10:08
the average price of a transistor --
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看一下晶体管的价格－－
10:11
1968, you could buy one transistor for a dollar.
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在1968年，一美元可以买一个晶体管。
10:14
You could buy 10 million in 2002.
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而在2002年，一美元可以买一千万个。
10:17
It's pretty remarkable how smooth
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这是一个非常显著的平顺的
10:20
an exponential process that is.
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指数过程。
10:22
I mean, you'd think this is the result of some tabletop experiment,
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你会认为这是一个实验桌上的结果，
10:26
but this is the result of worldwide chaotic behavior --
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但是我认为这个是一个世界范围内，无章法的行为的结果－－
10:29
countries accusing each other of dumping products,
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各个国家指责彼此倾销商品，
10:31
IPOs, bankruptcies, marketing programs.
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首次公开发行股票，破产，市场活动。
10:33
You would think it would be a very erratic process,
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你会认为这是一个非常不确定的过程，
10:36
and you have a very smooth
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而你会看到这样一个混乱的过程的结果
10:38
outcome of this chaotic process.
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是非常平顺的。
10:40
Just as we can't predict
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正如我们无法预测
10:42
what one molecule in a gas will do --
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汽油中的一个分子如何运动一样－－
10:44
it's hopeless to predict a single molecule --
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我们是无法预测一个分子的－－
10:47
yet we can predict the properties of the whole gas,
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但是运用热力学，我们可以非常准确低知道
10:49
using thermodynamics, very accurately.
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作为一个整体，汽油有什么样的性质。
10:52
It's the same thing here. We can't predict any particular project,
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这里是一样的。我们无法预测某一个项目会怎样，
10:55
but the result of this whole worldwide,
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但是可以知道世界范围内的趋势－－
10:57
chaotic, unpredictable activity of competition
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世界范围内的，无序的，不可预测的竞争。
11:02
and the evolutionary process of technology is very predictable.
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科技进步的过程是可以被很好预测的。
11:05
And we can predict these trends far into the future.
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而我们可以预言科技进步的未来趋势。
11:10
Unlike Gertrude Stein's roses,
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不象Gertrude Stein的玫瑰，
11:12
it's not the case that a transistor is a transistor.
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这病不是一个晶体管是一个晶体管。
11:14
As we make them smaller and less expensive,
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当我们把他们做地越来越小时，
11:16
the electrons have less distance to travel.
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电子运动的距离会变小。
11:18
They're faster, so you've got exponential growth in the speed of transistors,
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他们的运动非常快，所以我们会发现晶体管的性能的指数性增长，
11:22
so the cost of a cycle of one transistor
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进而，晶体管的价格
11:26
has been coming down with a halving rate of 1.1 years.
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将会在每1.1年下降一半。
11:29
You add other forms of innovation and processor design,
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加入一种创新和另一种处理器的设计，
11:32
you get a doubling of price performance of computing every one year.
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你将会使计算的性价比每年提高一倍。
11:36
And that's basically deflation --
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这其实就是价格下降－－
11:39
50 percent deflation.
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50％的价格下降。
11:41
And it's not just computers. I mean, it's true of DNA sequencing;
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而这不仅仅是计算机。这对于基因组序列
11:44
it's true of brain scanning;
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和大脑的扫描，
11:46
it's true of the World Wide Web. I mean, anything that we can quantify,
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和国际互联网也是成立的。我的意思是对于任何我们可以量化的东西，
11:48
we have hundreds of different measurements
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我们有几百种不同的指标
11:51
of different, information-related measurements --
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不同的信息相关的指标－－
11:54
capacity, adoption rates --
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存储量，采用率－－
11:56
and they basically double every 12, 13, 15 months,
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他们几乎每12，13 或15个月就要翻一番，
11:59
depending on what you're looking at.
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关键在于我们如何看待。
12:01
In terms of price performance, that's a 40 to 50 percent deflation rate.
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对于性价比，这是一个百分之50 到 百分之40 的价格下降。
12:06
And economists have actually started worrying about that.
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而经济学家已经开始担心这些。
12:08
We had deflation during the Depression,
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我们在经济萧条的时候会经历价格下降，通货紧缩，
12:10
but that was collapse of the money supply,
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但是那是由于货币的供应崩溃，
12:12
collapse of consumer confidence, a completely different phenomena.
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消费者信心的崩溃，一个完全不同的现象。
12:15
This is due to greater productivity,
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这是由于生产力的极大提高，
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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但是经济学家说：“没有办法来保持这样的节奏。”
12:20
If you have 50 percent deflation, people may increase their volume
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如果有50％的价格下降，人们的购买量会增加
12:23
30, 40 percent, but they won't keep up with it."
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百分之30－40，但是没办法保持这个增长。
12:25
But what we're actually seeing is that
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但是我们真正看到的
12:27
we actually more than keep up with it.
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是我们不仅仅是保持。
12:29
We've had 28 percent per year compounded growth in dollars
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我们看到在过去的50年里，
12:32
in information technology over the last 50 years.
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信息产业的美元在以每年28％的复合增长速度增长。
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，人们不会在10年制造价值10,000美元的iPod.
12:39
As the price performance makes new applications feasible,
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当性价比使得新应用称为可能，
12:42
new applications come to the market.
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这些新的应用将走向市场。
12:44
And this is a very widespread phenomena.
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这是一个非常广泛的现象。
12:47
Magnetic data storage --
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磁存储技术－－
12:49
that's not Moore's Law, it's shrinking magnetic spots,
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这不是摩尔定律，这个缩小磁点，
12:52
different engineers, different companies, same exponential process.
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不同的工程师，不同公司，但是相同的指数增长过程。
12:56
A key revolution is that we're understanding our own biology
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一个关键性革命是我们通过信息，
13:00
in these information terms.
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了解了我们自身的生命体。
13:02
We're understanding the software programs
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我们懂得了让我们的机体运转
13:04
that make our body run.
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的软件程序。
13:06
These were evolved in very different times --
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这些都是在不同的时间进化－－
13:08
we'd like to actually change those programs.
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实际上，我们会改变这些程序。
13:10
One little software program, called the fat insulin receptor gene,
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一个叫做脂肪胰岛素受体基因的软件，
13:12
basically says, "Hold onto every calorie,
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简单地说, 要合理使用每个卡路里，
13:14
because the next hunting season may not work out so well."
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因为下一个狩猎季节也许不会很顺利。
13:18
That was in the interests of the species tens of thousands of years ago.
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这是千百年前，复合物种生存条件的一个例子。
13:21
We'd like to actually turn that program off.
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我们现在关掉这个程序。
13:24
They tried that in animals, and these mice ate ravenously
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我们把它用到其他动物身上，老鼠们非常贪婪地吃着，
13:27
and remained slim and got the health benefits of being slim.
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并且保持着很瘦地身材，而且更加健康。
13:29
They didn't get diabetes; they didn't get heart disease;
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他们不会得糖尿病，也没有心脏病。
13:32
they lived 20 percent longer; they got the health benefits of caloric restriction
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他们的寿命延长了20％，他们从卡路里的约束中
13:35
without the restriction.
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得到了更加健康。
13:37
Four or five pharmaceutical companies have noticed this,
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四五个制药公司已经注意到了这一点。
13:40
felt that would be
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觉得这将会
13:43
interesting drug for the human market,
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称为市场上非常有趣的药品，
13:46
and that's just one of the 30,000 genes
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而那只是30,000个影响
13:48
that affect our biochemistry.
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我们生物化学的基因中的一个。
13:51
We were evolved in an era where it wasn't in the interests of people
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我们发展进化的时代是这样一个时代，像在座的各位，包括我在内
13:54
at the age of most people at this conference, like myself,
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希望活得更长，但是却事与愿违。
13:57
to live much longer, because we were using up the precious resources
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因为我们正在用尽宝贵的资源，
14:01
which were better deployed towards the children
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这些资源可以被我们的子孙后代以及更在意这些资源的人
14:02
and those caring for them.
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所更好地利用。
14:04
So, life -- long lifespans --
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所以，生命，长寿
14:06
like, that is to say, much more than 30 --
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30年以上的寿命
14:08
weren't selected for,
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并不是自然选择的结果
14:11
but we are learning to actually manipulate
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而是我们通过生命科技的进步来学习如何控制
14:14
and change these software programs
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这些程序
14:16
through the biotechnology revolution.
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的结果。
14:18
For example, we can inhibit genes now with RNA interference.
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例如，我们可以通过影响RNA来抑制某些基因。
14:22
There are exciting new forms of gene therapy
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这些令人兴奋的新的基因疗法
14:24
that overcome the problem of placing the genetic material
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成功地实现了将这些基因材料
14:26
in the right place on the chromosome.
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放置染色体的正确位置。
14:28
There's actually a -- for the first time now,
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现在，第一次出现了能够治愈肺动脉高血压症
14:31
something going to human trials, that actually cures pulmonary hypertension --
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这样一个致命病症地人体实验
14:34
a fatal disease -- using gene therapy.
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这都是运用的基因疗法。
14:37
So we'll have not just designer babies, but designer baby boomers.
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所以，我们不仅仅是有了婴儿的设计师，更是婴儿潮地设计师。
14:40
And this technology is also accelerating.
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而这个技术也是在加速发展。
14:43
It cost 10 dollars per base pair in 1990,
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在1990年，每个碱基对要花10美元，
14:46
then a penny in 2000.
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2000年只需要一美分。
14:48
It's now under a 10th of a cent.
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现在是十分之一分。
14:50
The amount of genetic data --
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基因数据每年增长一倍
14:52
basically this shows that smooth exponential growth
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基本上来说
14:55
doubled every year,
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是指数增长，
14:57
enabling the genome project to be completed.
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这个发展会促进基因组测序计划的成功。
15:00
Another major revolution: the communications revolution.
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另一项重要的革命是通信革命。
15:03
The price performance, bandwidth, capacity of communications measured many different ways;
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从性价比，带宽，通信容量来看，
15:08
wired, wireless is growing exponentially.
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有线和无线通信都是指数增长。
15:11
The Internet has been doubling in power and continues to,
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从各个方面看，国际互联网的能量已经翻番
15:14
measured many different ways.
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并还将继续。
15:16
This is based on the number of hosts.
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这长图是基于主机的数量。
15:18
Miniaturization -- we're shrinking the size of technology
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小型化，我们缩小这个技术的速度
15:20
at an exponential rate,
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是指数增长的。
15:22
both wired and wireless.
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无论是有线还是无线。
15:24
These are some designs from Eric Drexler's book --
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从Eric Drexler书中的设计来看，
15:28
which we're now showing are feasible
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我们所展示的，
15:30
with super-computing simulations,
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都是超级计算模拟出可行的设计，
15:32
where actually there are scientists building
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科学家们正在制造
15:34
molecule-scale robots.
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分子级的机器人。
15:36
One has one that actually walks with a surprisingly human-like gait,
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某些机器人非常令人惊讶地以人类的步态行走。
15:38
that's built out of molecules.
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那是由分子建造的。
15:41
There are little machines doing things in experimental bases.
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一些小机器已经在实验室环境中成型。
15:45
The most exciting opportunity
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最令人兴奋的前景
15:48
is actually to go inside the human body
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实际上是在人体内部
15:50
and perform therapeutic and diagnostic functions.
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完成治疗和诊断的功能。
15:53
And this is less futuristic than it may sound.
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这并没有看起来那么遥远。
15:55
These things have already been done in animals.
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这些机器人已经运用在了动物实验上。
15:57
There's one nano-engineered device that cures type 1 diabetes. It's blood cell-sized.
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已经有纳米工程的装置可以治愈1型糖尿病，而它只有血细胞的大小。
16:01
They put tens of thousands of these
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科学家将很多的这些装置
16:03
in the blood cell -- they tried this in rats --
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放入老鼠的血液中，
16:05
it lets insulin out in a controlled fashion,
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它可以控制胰岛素的释放，
16:07
and actually cures type 1 diabetes.
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而确实治愈了1型糖尿病。
16:09
What you're watching is a design
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现在我们看到是
16:12
of a robotic red blood cell,
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一个血红细胞机器人，
16:14
and it does bring up the issue that our biology
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它引发的话题表明，我们的生命体
16:16
is actually very sub-optimal,
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仅仅是次优
16:18
even though it's remarkable in its intricacy.
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尽管有其显著的复杂程度。
16:21
Once we understand its principles of operation,
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一旦我们了解了运作的原理，
16:24
and the pace with which we are reverse-engineering biology is accelerating,
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我们逆向生命工程的发展是加速的。
16:28
we can actually design these things to be
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我们可以将这些东西设计得
16:30
thousands of times more capable.
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强大数千倍。
16:32
An analysis of this respirocyte, designed by Rob Freitas,
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Rob Freitas发明的人造红细胞的分析
16:37
indicates if you replace 10 percent of your red blood cells with these robotic versions,
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显示，如果你将身体中百分之十的红细胞替换成人造红细胞，
16:40
you could do an Olympic sprint for 15 minutes without taking a breath.
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你将可以不废吹灰之力完成15分钟的奥林匹克冲刺。
16:43
You could sit at the bottom of your pool for four hours --
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你可以坐在游泳池底部4小时－－
16:46
so, "Honey, I'm in the pool," will take on a whole new meaning.
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所以，“亲爱的，我在游泳池” 将会有全新的意思。
16:50
It will be interesting to see what we do in our Olympic trials.
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我们做这个奥林匹克的实验将会非常有趣。
16:52
Presumably we'll ban them,
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可以预测，我们将会禁止这样做。
16:54
but then we'll have the specter of teenagers in their high schools gyms
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我们会发现我们的青少年在高中的体育馆中的表现，
16:56
routinely out-performing the Olympic athletes.
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会经常超过奥林匹克运动员。
17:01
Freitas has a design for a robotic white blood cell.
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Freitas 设计了一个白细胞机器人。
17:04
These are 2020-circa scenarios,
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有一个大概的2020年的方案，
17:08
but they're not as futuristic as it may sound.
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但是他们并没有那么遥不可及。
17:10
There are four major conferences on building blood cell-sized devices;
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有四个研讨会组织正在研究建造血细胞大小的设备，
17:14
there are many experiments in animals.
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有很多用在动物身上的实验。
17:16
There's actually one going into human trial,
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实际上有一个已经进入了人体实验的阶段，
17:18
so this is feasible technology.
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所以，这是可行的科技。
17:22
If we come back to our exponential growth of computing,
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如果我们回到我们计算的指数增长模型，
17:24
1,000 dollars of computing is now somewhere between an insect and a mouse brain.
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1000美元的计算现在相当于昆虫或者老鼠的大脑。
17:27
It will intersect human intelligence
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到2020年时
17:30
in terms of capacity in the 2020s,
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从存储量上来说，将会有人类的存量。
17:33
but that'll be the hardware side of the equation.
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但是这只是方程式的硬件的那一边。
17:35
Where will we get the software?
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我们从哪里得到我们的软件呢？
17:37
Well, it turns out we can see inside the human brain,
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嗯，我们将会看到我们大脑的内部，
17:39
and in fact not surprisingly,
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并且事实上并不惊讶，
17:41
the spatial and temporal resolution of brain scanning is doubling every year.
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大脑的扫描空间和时间分辨率是每年翻一番。
17:45
And with the new generation of scanning tools,
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并且会有新一代的扫描工具出现，
17:47
for the first time we can actually see
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实现我们第一次看到
17:49
individual inter-neural fibers
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单个的跨神经纤维
17:51
and see them processing and signaling in real time --
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并且实时地看到他们是如何处理并且发送信号
17:54
but then the question is, OK, we can get this data now,
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于是，之后就没问题了，我们现在可以得到数据了，
17:56
but can we understand it?
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但是我们能明白这些数据吗？
17:58
Doug Hofstadter wonders, well, maybe our intelligence
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Doug Hofstadter怀疑也许我们的理解力
18:01
just isn't great enough to understand our intelligence,
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不足以明白我们自己的智力，
18:04
and if we were smarter, well, then our brains would be that much more complicated,
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如果我们更加聪明一点，那么我们的大脑会便得更加复杂，
18:07
and we'd never catch up to it.
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我们永远都无法赶上。
18:10
It turns out that we can understand it.
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最终我们可以明白。
18:13
This is a block diagram of
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这个是一个框图，
18:16
a model and simulation of the human auditory cortex
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这个框图是一个人类听觉皮层的模型和仿真
18:20
that actually works quite well --
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这个模型的拟真程度很好－－
18:22
in applying psychoacoustic tests, gets very similar results to human auditory perception.
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在音质测试的实验中，它得到了非常类似人类听觉的结果。
18:26
There's another simulation of the cerebellum --
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在另一项小脑的仿真中－－
18:29
that's more than half the neurons in the brain --
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小脑包含了人脑中一半的神经－－
18:31
again, works very similarly to human skill formation.
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同样，这个仿真的模拟效果非常好。
18:35
This is at an early stage, but you can show
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这是早期的阶段，但是你可以看出
18:38
with the exponential growth of the amount of information about the brain
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对于人脑数据的指数增长，
18:41
and the exponential improvement
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和人脑扫描解析度
18:43
in the resolution of brain scanning,
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的增长，
18:45
we will succeed in reverse-engineering the human brain
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到2020年，我们将会成功地
18:48
by the 2020s.
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实现人脑的反向工程研究。
18:50
We've already had very good models and simulation of about 15 regions
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我们已经有了几百个区域中
18:53
out of the several hundred.
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15个区域非常好的模型和仿真。
18:56
All of this is driving
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所有的这些都是指数增长－
18:58
exponentially growing economic progress.
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指数增长经济的进展。
19:00
We've had productivity go from 30 dollars to 150 dollars per hour
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在过去的50年中，我们的生产率
19:05
of labor in the last 50 years.
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从一小时30美元提高到一小时150美元
19:07
E-commerce has been growing exponentially. It's now a trillion dollars.
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电子商务已经在以指数增长。现在已经是万亿美元。
19:10
You might wonder, well, wasn't there a boom and a bust?
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你也许会怀疑，那么，那会不会有繁荣期也有萧条期呢？
19:12
That was strictly a capital-markets phenomena.
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这是一个严格的资本市场的现象。
19:14
Wall Street noticed that this was a revolutionary technology, which it was,
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华尔街注意到了这个革命性的科技，的确，
19:18
but then six months later, when it hadn't revolutionized all business models,
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但是6个月之后，如果它并没有革命性的商业模型，
19:21
they figured, well, that was wrong,
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他们认为，那不对，
19:23
and then we had this bust.
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于是，我们有了萧条。
19:26
All right, this is a technology
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好吧，这是科技
19:28
that we put together using some of the technologies we're involved in.
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这科技可以把我们所用的一切技术整合到一起。
19:31
This will be a routine feature in a cell phone.
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手机会有常规的功能。
19:35
It would be able to translate from one language to another.
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它将可以把一种语言翻译成另一种语言。
19:47
So let me just end with a couple of scenarios.
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那么，让我来以两个情景来结束。
19:49
By 2010 computers will disappear.
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到2010年，计算机将消失。
19:53
They'll be so small, they'll be embedded in our clothing, in our environment.
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他们将会变得非常小，会嵌入到衣服，和我们的环境中。
19:56
Images will be written directly to our retina,
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图像将会直接写到我们的视网膜上，
19:58
providing full-immersion virtual reality,
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展现出全沉浸的虚拟现实，
20:00
augmented real reality. We'll be interacting with virtual personalities.
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增强真实的显示。我们会直接和虚拟人物互动。
20:04
But if we go to 2029, we really have the full maturity of these trends,
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但是如果到2029年，这些趋势将会发展成熟，
20:08
and you have to appreciate how many turns of the screw
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你必须了解科技发展中
20:11
in terms of generations of technology, which are getting faster and faster, we'll have at that point.
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很多的转折，这些转折会越来越快，
20:15
I mean, we will have two-to-the-25th-power
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我是说我们会有2到二十五倍
20:17
greater price performance, capacity and bandwidth
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这些科技的性价比，存量和带宽，
20:20
of these technologies, which is pretty phenomenal.
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这些变化是巨大的。
20:22
It'll be millions of times more powerful than it is today.
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它将会比现在的科技强大数百万倍。
20:24
We'll have completed the reverse-engineering of the human brain,
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我们将完成人脑的反向工程计算，
20:27
1,000 dollars of computing will be far more powerful
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1000美元的计算以将会比人脑的基本裸存量
20:30
than the human brain in terms of basic raw capacity.
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还要强大很多。
20:34
Computers will combine
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计算机将会集合
20:36
the subtle pan-recognition powers
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非常微妙的人类智能的认知能力，
20:38
of human intelligence with ways in which machines are already superior,
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和非常强大的机器，
20:41
in terms of doing analytic thinking,
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可以完成分析思考，
20:43
remembering billions of facts accurately.
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准确地记住数十亿的事实。
20:45
Machines can share their knowledge very quickly.
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机器可以非常迅速地分享它们的知识，
20:47
But it's not just an alien invasion of intelligent machines.
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但是这不只是智能机器的入侵。
20:52
We are going to merge with our technology.
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我们将会融合我们的科技。
20:54
These nano-bots I mentioned
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这些我刚提到过的纳米机器人
20:56
will first be used for medical and health applications:
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将首次被用于药物和健康；
21:00
cleaning up the environment, providing powerful fuel cells
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清理我们的环境，提供燃料－－非常强大的燃料电池
21:03
and widely distributed decentralized solar panels and so on in the environment.
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广泛分布的分布式太阳能板，和其他很多在环境中的应用。
21:08
But they'll also go inside our brain,
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但是它们将会走进我们的大脑，
21:10
interact with our biological neurons.
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和我们的生物神经交互。
21:12
We've demonstrated the key principles of being able to do this.
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我们将会展示这些成功的原理。
21:15
So, for example,
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例如，
21:17
full-immersion virtual reality from within the nervous system,
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在神经系统内部的全沉浸虚拟现实，
21:19
the nano-bots shut down the signals coming from your real senses,
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纳米机器人会关掉你真实感受的信号，
21:22
replace them with the signals that your brain would be receiving
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替代它们并传递给大脑
21:25
if you were in the virtual environment,
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如果你是在一个虚拟的环境，
21:27
and then it'll feel like you're in that virtual environment.
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你将会感觉到你正在这个虚拟的环境中。
21:29
You can go there with other people, have any kind of experience
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你可以和其他人一起进入，
21:31
with anyone involving all of the senses.
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和其他人一起去感受这些感觉。
21:34
"Experience beamers," I call them, will put their whole flow of sensory experiences
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我把他们叫做" Experience Beamers", 将会把在神经系统中的
21:37
in the neurological correlates of their emotions out on the Internet.
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感觉流引起的情感放入互联网上。
21:40
You can plug in and experience what it's like to be someone else.
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你可以进入然后体验别人的感觉。
21:43
But most importantly,
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但是最重要的，
21:45
it'll be a tremendous expansion
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它将会是人类智能的惊人扩散
21:47
of human intelligence through this direct merger with our technology,
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通过和我们科技的直接融合，
21:51
which in some sense we're doing already.
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从某些方面来说，我们已经在这样做。
21:53
We routinely do intellectual feats
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我们经常的智能表现
21:55
that would be impossible without our technology.
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是离开了我们的科技无法实现的。
21:57
Human life expectancy is expanding. It was 37 in 1800,
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在1800年，人类的预期寿命是37岁，
22:00
and with this sort of biotechnology, nano-technology revolutions,
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但是随着生物技术，纳米科技的革命，
22:05
this will move up very rapidly
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在未来几年，
22:07
in the years ahead.
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预期寿命会增长的非常迅速。
22:09
My main message is that progress in technology
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我主要传递的想法，是科技进步的速度
22:13
is exponential, not linear.
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是指数增长的，而非线性增长。
22:16
Many -- even scientists -- assume a linear model,
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很多人，甚至是科学家，都在线性模型的基础上假设，
22:20
so they'll say, "Oh, it'll be hundreds of years
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所以他们会说，这将会用几百年，
22:22
before we have self-replicating nano-technology assembly
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我们才能实现自复制纳米技术组装
22:25
or artificial intelligence."
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或人工智能。
22:27
If you really look at the power of exponential growth,
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如果你真地看到指数增长的力量，
22:30
you'll see that these things are pretty soon at hand.
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你将会看到这些事情会更快变成现实。
22:33
And information technology is increasingly encompassing
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信息技术正在加速指引着
22:36
all of our lives, from our music to our manufacturing
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我们的生活，从我们的音乐到生产制造，
22:40
to our biology to our energy to materials.
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到我们的生物体，到能源，到材料。
22:44
We'll be able to manufacture almost anything we need in the 2020s,
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到21世纪20年代，我们将有能力生产我们所需的任何东西，
22:47
from information, in very inexpensive raw materials,
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从信息，非常便宜的原材料，
22:49
using nano-technology.
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运用纳米技术。
22:52
These are very powerful technologies.
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它们是非常强大的科技。
22:54
They both empower our promise and our peril.
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它们将会成就我们的前景和隐患。
22:58
So we have to have the will to apply them to the right problems.
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所以，我们必须将他们运用在正确的地方。
23:01
Thank you very much.
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非常感谢。
23:02
(Applause)
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（掌声）
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