TED1994
Danny Hillis: Back to the future (of 1994)
丹尼·希利斯：回到未来（1994）
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从那被放在TED很后面的档案库里，丹尼·希利斯借着将生命本身的演化和科技变化的脚步是如何且为什么看似不断的加速这两点展开，然后简单地论述了这个耐人寻味的看法。他所呈现的演说技巧或许看起来过时，但想法却是相当切题且有意义的。
Danny Hillis - Computer theorist
Inventor, scientist, author, engineer -- over his broad career, Danny Hillis has turned his ever-searching brain on an array of subjects, with surprising results. Full bio
Inventor, scientist, author, engineer -- over his broad career, Danny Hillis has turned his ever-searching brain on an array of subjects, with surprising results. Full bio
Double-click the English transcript below to play the video.
00:15
Because I usually take the role
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由于我经常
00:18
of trying to explain to people
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向人们解释
00:20
how wonderful the new technologies
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即将到来的新科技
00:23
that are coming along are going to be,
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将会多么的美妙
00:25
and I thought that, since I was among friends here,
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我想既然我跟各位朋友们一起在这
00:28
I would tell you what I really think
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就让我来说说我真正的想法
00:32
and try to look back and try to understand
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并试着回顾和理解
00:34
what is really going on here
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这到底是如何发生的
00:37
with these amazing jumps in technology
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有了这些科技上的惊人进步。
00:42
that seem so fast that we can barely keep on top of it.
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科技的进步似乎快到我们根本无法赶上它的脚步。
00:45
So I'm going to start out
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让我先从这开始
00:47
by showing just one very boring technology slide.
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一页很无趣的科技幻灯片。
00:50
And then, so if you can just turn on the slide that's on.
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然后可以开始放幻灯片了。（对工作人员说）
00:56
This is just a random slide
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这只是我从我的文件中
00:58
that I picked out of my file.
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随机挑选出的一张。
01:00
What I want to show you is not so much the details of the slide,
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我想要你们看的并不是它的细节，
01:03
but the general form of it.
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而是它的总体形式。
01:05
This happens to be a slide of some analysis that we were doing
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这个是我们做的
01:08
about the power of RISC microprocessors
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关于RISC精简指令集微处理器功率
01:11
versus the power of local area networks.
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与本地网路功率分析的幻灯片。
01:14
And the interesting thing about it
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有趣的是
01:16
is that this slide,
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这页幻灯片
01:18
like so many technology slides that we're used to,
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就像很多我们所熟悉的幻灯片一样，
01:21
is a sort of a straight line
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是半对数曲线图
01:23
on a semi-log curve.
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上的一条直线。
01:25
In other words, every step here
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也就是这里的每一层，
01:27
represents an order of magnitude
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代表了性能程度
01:29
in performance scale.
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大小的一级。
01:31
And this is a new thing
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在半对数曲线图上
01:33
that we talk about technology
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讨论科技，
01:35
on semi-log curves.
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这很新鲜。
01:37
Something really weird is going on here.
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这其中有点奇特。
01:39
And that's basically what I'm going to be talking about.
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这基本上是我接下来要说的。
01:42
So, if you could bring up the lights.
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（对工作人员）麻烦开一下灯。
01:47
If you could bring up the lights higher,
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请把灯开亮点，
01:49
because I'm just going to use a piece of paper here.
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因为我要用张纸。
01:52
Now why do we draw technology curves
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为什么我们要用对数曲线
01:54
in semi-log curves?
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描绘科技曲线呢？
01:56
Well the answer is, if I drew it on a normal curve
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嗯，答案是，如果我用普通曲线画，
01:59
where, let's say, this is years,
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我们说，这是年份，
02:01
this is time of some sort,
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这是某个时间，
02:03
and this is whatever measure of the technology
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这是我准备画的
02:06
that I'm trying to graph,
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科技的某种测量值，
02:09
the graphs look sort of silly.
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这图看起来有点傻。
02:12
They sort of go like this.
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就有点像是这样。
02:15
And they don't tell us much.
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而且并没有提供什么资讯。
02:18
Now if I graph, for instance,
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现在，如果我画，比如说，
02:21
some other technology, say transportation technology,
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另一种技术，像是交通运输，
02:23
on a semi-log curve,
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在半对数曲线上，
02:25
it would look very stupid, it would look like a flat line.
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它看起来很蠢，会像条很平的线。
02:28
But when something like this happens,
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但是如果出现像这种
02:30
things are qualitatively changing.
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质变的情况。
02:32
So if transportation technology
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如果交通运输技术
02:34
was moving along as fast as microprocessor technology,
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进步地像微处理器技术一样快的话，
02:37
then the day after tomorrow,
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那，后天
02:39
I would be able to get in a taxi cab
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我就能搭一辆出租车
02:41
and be in Tokyo in 30 seconds.
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然后在30秒内到东京。
02:43
It's not moving like that.
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但它并没有进步得那么快。
02:45
And there's nothing precedented
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在科技发展历史中
02:47
in the history of technology development
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也没有任何
02:49
of this kind of self-feeding growth
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这种自给自足，
02:51
where you go by orders of magnitude every few years.
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每几年程度翻倍增长的先例。
02:54
Now the question that I'd like to ask is,
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现在我想要问的是，
02:57
if you look at these exponential curves,
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如果你观察这些指数曲线，
03:00
they don't go on forever.
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它们并非永远的持续下去。
03:03
Things just can't possibly keep changing
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事物不可能一直
03:06
as fast as they are.
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改变得那么快。
03:08
One of two things is going to happen.
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两件事会发生，
03:11
Either it's going to turn into a sort of classical S-curve like this,
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要么它会变成像这样典型的S曲线
03:15
until something totally different comes along,
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直到完全不同的情况出现。
03:19
or maybe it's going to do this.
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或是会变成这样。
03:21
That's about all it can do.
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这就是所有可能。
03:23
Now I'm an optimist,
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现在我是个乐观主义者，
03:25
so I sort of think it's probably going to do something like that.
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所以我觉得它很有可能就会变成这样。
03:28
If so, that means that what we're in the middle of right now
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如果是这样，意味着我们目前所在的
03:31
is a transition.
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是过渡阶段。
03:33
We're sort of on this line
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我们似乎在这条线上，
03:35
in a transition from the way the world used to be
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在世界从过去
03:37
to some new way that the world is.
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到将来的转变中。
03:40
And so what I'm trying to ask, what I've been asking myself,
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所有我要问的，我一直在问自己的，
03:43
is what's this new way that the world is?
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就是这世界未来道路在哪？
03:46
What's that new state that the world is heading toward?
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它趋向的新时代是什么样的？
03:49
Because the transition seems very, very confusing
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由于这个变化似乎非常，非常迷惑人，
03:52
when we're right in the middle of it.
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当我们正处于其中时。
03:54
Now when I was a kid growing up,
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我小时候，在长大过程中
03:57
the future was kind of the year 2000,
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未来就像是2000年，
04:00
and people used to talk about what would happen in the year 2000.
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人们都在讨论2000年将会发生什么。
04:04
Now here's a conference
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现在这个会议上，
04:06
in which people talk about the future,
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大家在讨论未来，
04:08
and you notice that the future is still at about the year 2000.
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而且你能发现这未来指的还是那个“2000年”。
04:11
It's about as far as we go out.
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这就是我们能达到的程度。
04:13
So in other words, the future has kind of been shrinking
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换句话说，在我一生中
04:16
one year per year
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未来正在
04:19
for my whole lifetime.
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逐年缩短。
04:22
Now I think that the reason
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我想原因是
04:24
is because we all feel
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我们都感觉到
04:26
that something's happening there.
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正在发生些什么。
04:28
That transition is happening. We can all sense it.
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变化正在发生。我们都能察觉到。
04:30
And we know that it just doesn't make too much sense
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我们知道去考虑那未来的三、五十年
04:32
to think out 30, 50 years
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已经没什么意义了，
04:34
because everything's going to be so different
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因为每件事都将如此不同
04:37
that a simple extrapolation of what we're doing
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以至于推测将来
04:39
just doesn't make any sense at all.
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不再有意义。
04:42
So what I would like to talk about
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所以我要聊聊
04:44
is what that could be,
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那会是怎样，
04:46
what that transition could be that we're going through.
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我们正在经历的转变会是怎样。
04:49
Now in order to do that
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为达到这个目的，
04:52
I'm going to have to talk about a bunch of stuff
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我得介绍一堆东西
04:54
that really has nothing to do
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它们与
04:56
with technology and computers.
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科技和电脑完全无关。
04:58
Because I think the only way to understand this
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因为我决定理解这个的唯一方法
05:00
is to really step back
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就是回顾过去
05:02
and take a long time scale look at things.
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拉长时间轴去看。
05:04
So the time scale that I would like to look at this on
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而我所要看的时间轴
05:07
is the time scale of life on Earth.
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是以地球上生命的时间跨度来看。
05:13
So I think this picture makes sense
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我想这幅图合理了
05:15
if you look at it a few billion years at a time.
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如果你每次从几十亿年跨度来看。
05:19
So if you go back
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所以如果你回溯个
05:21
about two and a half billion years,
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大概25亿年，
05:23
the Earth was this big, sterile hunk of rock
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地球这么大，贫瘠的大块石头
05:26
with a lot of chemicals floating around on it.
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上面浮着些化学物质。
05:29
And if you look at the way
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要是观察
05:31
that the chemicals got organized,
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这些化学物质怎样组合的，
05:33
we begin to get a pretty good idea of how they do it.
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我们开始弄明白它们怎么形成的。
05:36
And I think that there's theories that are beginning to understand
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我想有些理论是从理解
05:39
about how it started with RNA,
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生命怎样从核糖核酸演变开始，
05:41
but I'm going to tell a sort of simple story of it,
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但是我想讲一个生命的简单故事，
05:44
which is that, at that time,
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就是，在那个时候，
05:46
there were little drops of oil floating around
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有一滴滴的油四处浮动，
05:49
with all kinds of different recipes of chemicals in them.
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里面有各种不同化学成分组合。
05:52
And some of those drops of oil
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有些油滴
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had a particular combination of chemicals in them
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里面含有特殊的化学构成
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which caused them to incorporate chemicals from the outside
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这导致它们可以从外界聚集化学物质
05:59
and grow the drops of oil.
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并慢慢变大。
06:02
And those that were like that
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像这样的油滴
06:04
started to split and divide.
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又开始分化，分离。
06:06
And those were the most primitive forms of cells in a sense,
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最原始的那些在某种程度上形成了细胞，
06:09
those little drops of oil.
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这些小小的油滴。
06:11
But now those drops of oil weren't really alive, as we say it now,
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但目前为止这些油滴不是真正活着的，在我们现在看来，
06:14
because every one of them
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因为每一个
06:16
was a little random recipe of chemicals.
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都是化学物质的随机合成。
06:18
And every time it divided,
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每分裂一次，
06:20
they got sort of unequal division
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都不是平均分布
06:23
of the chemicals within them.
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内部的化学物。
06:25
And so every drop was a little bit different.
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所以每个油滴都有点不同。
06:28
In fact, the drops that were different in a way
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实际上，油滴不同的方式
06:30
that caused them to be better
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是让它们能更好地
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at incorporating chemicals around them,
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集成周围的化合物，
06:34
grew more and incorporated more chemicals and divided more.
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长得更大，吸收更多，分裂更多。
06:37
So those tended to live longer,
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所以它们会活得更长，
06:39
get expressed more.
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表现得更多。
06:42
Now that's sort of just a very simple
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这就有点像个很简单的
06:45
chemical form of life,
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生命的化学形式，
06:47
but when things got interesting
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但过程变得有趣
06:50
was when these drops
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是当这些油滴
06:52
learned a trick about abstraction.
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学会了一个提供资讯的技巧时。
06:55
Somehow by ways that we don't quite understand,
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不知怎么用我们不能完全理解的方式，
06:58
these little drops learned to write down information.
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这些小油滴学会了记录资讯。
07:01
They learned to record the information
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它们学会把
07:03
that was the recipe of the cell
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细胞形成的秘诀
07:05
onto a particular kind of chemical
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记录到一种特殊物质上，
07:07
called DNA.
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叫做去氧核糖核酸。
07:09
So in other words, they worked out,
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也就是说，它们想出了，
07:11
in this mindless sort of evolutionary way,
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以这种随性的进化方式，
07:14
a form of writing that let them write down what they were,
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可以写下它们基因信息的记录方式，
07:17
so that that way of writing it down could get copied.
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以便这种记录方式能被复制。
07:20
The amazing thing is that that way of writing
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惊奇的是这种记录方式
07:23
seems to have stayed steady
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似乎可以保持稳定
07:25
since it evolved two and a half billion years ago.
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由于它25亿年前演化出来的。
07:27
In fact the recipe for us, our genes,
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实际上我们，我们基因的组成
07:30
is exactly that same code and that same way of writing.
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就是完全一样的代码，一样的记录方式。
07:33
In fact, every living creature is written
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实际上，任何生物都是
07:36
in exactly the same set of letters and the same code.
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用完全一样的字母和代码记录下来的。
07:38
In fact, one of the things that I did
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实际上，我所做的
07:40
just for amusement purposes
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仅是为了娱乐效果的一件事
07:42
is we can now write things in this code.
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就是我们能用这个代码记录事件。
07:44
And I've got here a little 100 micrograms of white powder,
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我这有100微克的白粉，
07:50
which I try not to let the security people see at airports.
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我尽力不让机场安检人员发现它们。
07:54
(Laughter)
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（笑声）
07:56
But this has in it --
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不过这里面有代码
07:58
what I did is I took this code --
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我所做的是我拿着这代码
08:00
the code has standard letters that we use for symbolizing it --
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它里面有我们用来标记它的标准字母，
08:03
and I wrote my business card onto a piece of DNA
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然后我把我的名片写到一条去氧核糖核酸上
08:06
and amplified it 10 to the 22 times.
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再放大10到22倍。
08:09
So if anyone would like a hundred million copies of my business card,
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所以如果有人需要数百万份我的名片，
08:12
I have plenty for everyone in the room,
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我有足够多份给在座每个人，
08:14
and, in fact, everyone in the world,
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甚至是全世界每个人，
08:16
and it's right here.
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就在这。
08:19
(Laughter)
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（笑声）
08:26
If I had really been a egotist,
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要是我是个自大的人，
08:28
I would have put it into a virus and released it in the room.
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我就会把它放到病毒里散布到屋子中。
08:31
(Laughter)
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（笑声）
08:39
So what was the next step?
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所以下一步是什么？
08:41
Writing down the DNA was an interesting step.
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记录去氧核糖核酸是有趣的一步。
08:43
And that caused these cells --
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它导致了细胞的形成——
08:45
that kept them happy for another billion years.
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让它们又高兴了几十亿年。
08:47
But then there was another really interesting step
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不过还有个很有趣的环节
08:49
where things became completely different,
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事情开始变得完全不同，
08:52
which is these cells started exchanging and communicating information,
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那就是这些细胞开始交换和交流资讯，
08:55
so that they began to get communities of cells.
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从而形成细胞团体。
08:57
I don't know if you know this,
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我不知道你们是否知道这个，
08:59
but bacteria can actually exchange DNA.
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细菌实际上就可以交换去氧核糖核酸。
09:01
Now that's why, for instance,
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这就是为什么，比如，
09:03
antibiotic resistance has evolved.
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演变出抗菌免疫。
09:05
Some bacteria figured out how to stay away from penicillin,
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有些细菌知道怎么远离青霉素，
09:08
and it went around sort of creating its little DNA information
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然后它创造它这点去氧核糖核酸资讯，
09:11
with other bacteria,
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并在别的细菌中到处游走，
09:13
and now we have a lot of bacteria that are resistant to penicillin,
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现在我们有很多对青霉素免疫的细菌了，
09:16
because bacteria communicate.
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因为细菌会交流资讯。
09:18
Now what this communication allowed
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这样，这些交流致使
09:20
was communities to form
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群落的形成，
09:22
that, in some sense, were in the same boat together;
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在某种意义上，它们在同一条船上了；
09:24
they were synergistic.
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它们是协作的。
09:26
So they survived
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因此它们一起幸存下来
09:28
or they failed together,
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或者一起死去，
09:30
which means that if a community was very successful,
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也就是说如果一个群落成功了，
09:32
all the individuals in that community
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所有群落里的个体
09:34
were repeated more
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都能复制更多，
09:36
and they were favored by evolution.
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进化得更有利。
09:39
Now the transition point happened
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于是，转换点到了，
09:41
when these communities got so close
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当这些族群很亲近时，
09:43
that, in fact, they got together
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事实上，它们聚集到一起
09:45
and decided to write down the whole recipe for the community
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并决定在一条去氧核糖核酸上
09:48
together on one string of DNA.
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写下整个族群的成分谱。
09:51
And so the next stage that's interesting in life
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生命中下一个有趣的阶段
09:53
took about another billion years.
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又要几十亿年。
09:55
And at that stage,
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在这个时期，
09:57
we have multi-cellular communities,
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有多细胞族群，
09:59
communities of lots of different types of cells,
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就是有很多种不同细胞的群落，
10:01
working together as a single organism.
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作为有机体一起合作。
10:03
And in fact, we're such a multi-cellular community.
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实际上，我们就是这样的多细胞族群。
10:06
We have lots of cells
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我们有很多细胞，
10:08
that are not out for themselves anymore.
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它们不再是只为自己存活。
10:10
Your skin cell is really useless
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皮肤细胞根本没用，
10:13
without a heart cell, muscle cell,
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要是没有心脏细胞，肌肉细胞，
10:15
a brain cell and so on.
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脑细胞等等。
10:17
So these communities began to evolve
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所以这些族群开始进化
10:19
so that the interesting level on which evolution was taking place
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这样发生有趣的进化的
10:22
was no longer a cell,
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不再仅仅是单一细胞。
10:24
but a community which we call an organism.
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而是我们称为有机体的族群。
10:28
Now the next step that happened
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接下来发生
10:30
is within these communities.
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就是在这些族群中。
10:32
These communities of cells,
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这些细胞群落，
10:34
again, began to abstract information.
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再次，开始提取资讯。
10:36
And they began building very special structures
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它们开始构建非常特别的
10:39
that did nothing but process information within the community.
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专门处理群落内资讯的结构。
10:42
And those are the neural structures.
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这些就是神经结构。
10:44
So neurons are the information processing apparatus
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所以神经元是
10:47
that those communities of cells built up.
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这些细胞群建立的资讯处理仪器。
10:50
And in fact, they began to get specialists in the community
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实际上，群落里开始出现专家
10:52
and special structures
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以及特殊结构
10:54
that were responsible for recording,
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负责记录，
10:56
understanding, learning information.
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理解，学习资讯。
10:59
And that was the brains and the nervous system
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这就是这些细胞群的
11:01
of those communities.
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大脑和神经系统。
11:03
And that gave them an evolutionary advantage.
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这给了它们进化的有利条件。
11:05
Because at that point,
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因为这样的话，
11:08
an individual --
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对每个个体——
11:11
learning could happen
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学习可以发生
11:13
within the time span of a single organism,
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在单个有机体的时间跨度内，
11:15
instead of over this evolutionary time span.
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而不是整个进化时间跨度。
11:18
So an organism could, for instance,
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所以一个有机体能够，比如说，
11:20
learn not to eat a certain kind of fruit
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学会不吃某种水果
11:22
because it tasted bad and it got sick last time it ate it.
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因为它不好吃而且上次吃的觉得恶心。
11:26
That could happen within the lifetime of a single organism,
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这可以发生在一个单个有机体的一生中，
11:29
whereas before they'd built these special information processing structures,
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然后在这种特殊信息处理结构建成前，
11:33
that would have had to be learned evolutionarily
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这得要进化学习
11:35
over hundreds of thousands of years
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千万年，
11:38
by the individuals dying off that ate that kind of fruit.
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通过吃了这种水果前仆后继死去的个体。
11:41
So that nervous system,
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所以神经系统，
11:43
the fact that they built these special information structures,
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生物组建这种特殊结构的事实，
11:46
tremendously sped up the whole process of evolution.
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极大地加速了进化的进程。
11:49
Because evolution could now happen within an individual.
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因为至此进化可以在个体中发生了。
11:52
It could happen in learning time scales.
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它能发生在学习的时间跨度内。
11:55
But then what happened
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但是接下来发生的
11:57
was the individuals worked out,
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是每个个体发现了，
11:59
of course, tricks of communicating.
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当然，交流的秘诀。
12:01
And for example,
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比如说，
12:03
the most sophisticated version that we're aware of is human language.
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我们所知道的最精密的版本就是人类语言。
12:06
It's really a pretty amazing invention if you think about it.
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想想看，这真是个奇妙的发明。
12:09
Here I have a very complicated, messy,
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我脑子里有个很复杂，混乱，
12:11
confused idea in my head.
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疑惑的想法。
12:14
I'm sitting here making grunting sounds basically,
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我坐在这，基本上就是吐字发声，
12:17
and hopefully constructing a similar messy, confused idea in your head
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希望在你们头脑里建立一个类似的混乱
12:20
that bears some analogy to it.
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跟它有点类似的想法。
12:22
But we're taking something very complicated,
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但是我们正在把很复杂的东西
12:24
turning it into sound, sequences of sounds,
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转化成声音，一连串的声音，
12:27
and producing something very complicated in your brain.
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并在你们大脑产生很复杂的东西。
12:31
So this allows us now
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所以现在这推动我们
12:33
to begin to start functioning
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开始运作
12:35
as a single organism.
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作为单个有机体。
12:38
And so, in fact, what we've done
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所以，实际上，我们已经完成的
12:41
is we, humanity,
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就是我们，人类，
12:43
have started abstracting out.
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开始抽离出来。
12:45
We're going through the same levels
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我们正在经历多细胞有机体经历的
12:47
that multi-cellular organisms have gone through --
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相同的阶段——
12:49
abstracting out our methods of recording,
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提取我们记录，
12:52
presenting, processing information.
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展示，处理资讯的方式。
12:54
So for example, the invention of language
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比如说，语言的发明
12:56
was a tiny step in that direction.
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就是这个方向上很小一步。
12:59
Telephony, computers,
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电话，电脑，
13:01
videotapes, CD-ROMs and so on
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影碟，光碟等等
13:04
are all our specialized mechanisms
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都是我们的特殊机制，
13:06
that we've now built within our society
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我们正在社会里构建
13:08
for handling that information.
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用来处理资讯的机制。
13:10
And it all connects us together
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这些都是把我们联系在一起，
13:13
into something
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变的
13:15
that is much bigger
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比我们之前
13:17
and much faster
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更大，
13:19
and able to evolve
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更快，
13:21
than what we were before.
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更有能力进化。
13:23
So now, evolution can take place
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所以，现在进化可以发生在
13:25
on a scale of microseconds.
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微妙的时间跨度级上。
13:27
And you saw Ty's little evolutionary example
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你们看过泰伊的那个进化的小例子
13:29
where he sort of did a little bit of evolution
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它好像就在你们眼前的卷积程式上
13:31
on the Convolution program right before your eyes.
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展现了一点进化了。
13:34
So now we've speeded up the time scales once again.
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所以现在我们再次加快时间跨度。
13:37
So the first steps of the story that I told you about
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我讲的故事的第一步
13:39
took a billion years a piece.
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每一步花费了几十亿年。
13:41
And the next steps,
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下一步，
13:43
like nervous systems and brains,
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像神经系统和大脑，
13:45
took a few hundred million years.
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消耗几百万年。
13:47
Then the next steps, like language and so on,
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再接下来，像语言等等，
13:50
took less than a million years.
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需要不到一百万年。
13:52
And these next steps, like electronics,
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再下一步，像电子器件，
13:54
seem to be taking only a few decades.
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仿佛只要几十年。
13:56
The process is feeding on itself
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这个过程是自给自足，
13:58
and becoming, I guess, autocatalytic is the word for it --
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并且变成，我猜，应该自我催化描述更合适——
14:01
when something reinforces its rate of change.
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当事物加快改变的速度。
14:04
The more it changes, the faster it changes.
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变化越多，变化就越快。
14:07
And I think that that's what we're seeing here in this explosion of curve.
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我想这就是我们在这看到的激增曲线。
14:10
We're seeing this process feeding back on itself.
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我们看到这个过程回馈到自己。
14:13
Now I design computers for a living,
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我现在工作就是自己设计电脑，
14:16
and I know that the mechanisms
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我知道用来设计电脑的
14:18
that I use to design computers
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这些机制
14:21
would be impossible
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不可能存在，
14:23
without recent advances in computers.
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要是没有近期电脑的进步。
14:25
So right now, what I do
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现在，我做的
14:27
is I design objects at such complexity
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是设计复杂到
14:30
that it's really impossible for me to design them in the traditional sense.
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不可能从传统意义上设计的物体。
14:33
I don't know what every transistor in the connection machine does.
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我不知道连接机器上每个电晶体的作用。
14:37
There are billions of them.
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有几十亿电晶体。
14:39
Instead, what I do
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实际上，我所做的
14:41
and what the designers at Thinking Machines do
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思考机器的设计师们做的，
14:44
is we think at some level of abstraction
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我们认为是为某种程度的资讯抽取，
14:46
and then we hand it to the machine
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然后把它传给机器
14:48
and the machine takes it beyond what we could ever do,
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而机器把它运用到超出我们所能做的范围，
14:51
much farther and faster than we could ever do.
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而且比我们从前所做的更深远更快。
14:54
And in fact, sometimes it takes it by methods
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实际上，有时候它采用的方法
14:56
that we don't quite even understand.
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我们并不很懂。
14:59
One method that's particularly interesting
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有个尤其有趣
15:01
that I've been using a lot lately
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我最近一直在用的
15:04
is evolution itself.
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就是进化本身。
15:06
So what we do
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我们做的就是
15:08
is we put inside the machine
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在机器里
15:10
a process of evolution
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放入一个进化进程，
15:12
that takes place on the microsecond time scale.
356
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这个进程在微妙时间跨度上就能发生。
15:14
So for example,
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比如，
15:16
in the most extreme cases,
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大部分极端情况下，
15:18
we can actually evolve a program
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我们实际上能
15:20
by starting out with random sequences of instructions.
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通过从随机的指令序列开始进化一个程式。
15:24
Say, "Computer, would you please make
361
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（就像）说“电脑，请你产生
15:26
a hundred million random sequences of instructions.
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一亿随机指令序列。
15:29
Now would you please run all of those random sequences of instructions,
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现在请你运行所有这些随机指令列，
15:32
run all of those programs,
364
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运行所有程式，
15:34
and pick out the ones that came closest to doing what I wanted."
365
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并选出最接近我想要的。”
15:37
So in other words, I define what I wanted.
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也就是说，我定义我要什么。
15:39
Let's say I want to sort numbers,
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假设我需要分类资料，
15:41
as a simple example I've done it with.
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这是个我用它试验过的简单例子。
15:43
So find the programs that come closest to sorting numbers.
369
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3000
找到最接近资料分类的程式。
15:46
So of course, random sequences of instructions
370
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3000
当然，随机的指令序列
15:49
are very unlikely to sort numbers,
371
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2000
非常不可能分类资料，
15:51
so none of them will really do it.
372
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所以它们中没有一个能完成。
15:53
But one of them, by luck,
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但是中间有一个，运气很好，
15:55
may put two numbers in the right order.
374
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可能会把两个数按顺序排列。
15:57
And I say, "Computer,
375
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2000
我说，“电脑，
15:59
would you please now take the 10 percent
376
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3000
请你现在选出序列中百分之十
16:02
of those random sequences that did the best job.
377
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2000
完成得最好的。
16:04
Save those. Kill off the rest.
378
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保存这些。删掉其他的。
16:06
And now let's reproduce
379
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现在来复制
16:08
the ones that sorted numbers the best.
380
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资料分类得最好的这些。
16:10
And let's reproduce them by a process of recombination
381
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以类似交配的重组过程
16:13
analogous to sex."
382
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来复制它们。
16:15
Take two programs and they produce children
383
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取两个程式
16:18
by exchanging their subroutines,
384
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交换它们的副程式让它们产生子女，
16:20
and the children inherit the traits of the subroutines of the two programs.
385
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3000
这些子女继承了两个程式副程式的特征。
16:23
So I've got now a new generation of programs
386
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所以我得到新一代的
16:26
that are produced by combinations
387
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由组合做的比较好的程式
16:28
of the programs that did a little bit better job.
388
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而产生的程式。
16:30
Say, "Please repeat that process."
389
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2000
（指令）说，“请重复这个过程。”
16:32
Score them again.
390
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2000
再做一次。
16:34
Introduce some mutations perhaps.
391
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2000
可能引入一些突变。
16:36
And try that again and do that for another generation.
392
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再试一次并用在新的一代上。
16:39
Well every one of those generations just takes a few milliseconds.
393
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这一代上每个程式只需要几毫秒。
16:42
So I can do the equivalent
394
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所以我在电脑上用几分钟
16:44
of millions of years of evolution on that
395
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能做等同于
16:46
within the computer in a few minutes,
396
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3000
几百万年的进化过程，
16:49
or in the complicated cases, in a few hours.
397
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2000
或者，情况复杂时，在几小时内完成。
16:51
At the end of that, I end up with programs
398
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结束时，我得到
16:54
that are absolutely perfect at sorting numbers.
399
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绝对完美的分类资料的程式。
16:56
In fact, they are programs that are much more efficient
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实际上，这些程式比我手写的
16:59
than programs I could have ever written by hand.
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任何程式都要有效率。
17:01
Now if I look at those programs,
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现在，如果我读这些程式，
17:03
I can't tell you how they work.
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我说不出它们怎么工作的。
17:05
I've tried looking at them and telling you how they work.
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我尝试过阅读并且解释它们如何工作的。
17:07
They're obscure, weird programs.
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它们很抽象，奇怪。
17:09
But they do the job.
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但是它们能完成任务。
17:11
And in fact, I know, I'm very confident that they do the job
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实际上，我知道，我很有信心，它们能完成任务
17:14
because they come from a line
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因为它们来自于一行
17:16
of hundreds of thousands of programs that did the job.
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上千万能完成认为的程式。
17:18
In fact, their life depended on doing the job.
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事实上，它们的生命就是靠着这工作。
17:21
(Laughter)
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（笑声）
17:26
I was riding in a 747
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我曾经有一次
17:28
with Marvin Minsky once,
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和马文·明斯基一起坐747，
17:30
and he pulls out this card and says, "Oh look. Look at this.
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他拿出一张卡，说，“看，看这。
17:33
It says, 'This plane has hundreds of thousands of tiny parts
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这上面说，‘本飞机有很多精密部件
17:37
working together to make you a safe flight.'
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协作，保障你飞行安全。’
17:41
Doesn't that make you feel confident?"
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这是不是让你很有信心？”
17:43
(Laughter)
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（笑声）
17:45
In fact, we know that the engineering process doesn't work very well
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事实上，我们知道工程过程复杂化
17:48
when it gets complicated.
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并不能很好工作。
17:50
So we're beginning to depend on computers
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所以我们开始依赖电脑
17:52
to do a process that's very different than engineering.
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来做与工程有很大不同的一个过程。
17:56
And it lets us produce things of much more complexity
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它能让我们生产出
17:59
than normal engineering lets us produce.
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比普通工程能生产的更复杂的东西。
18:01
And yet, we don't quite understand the options of it.
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然而，我们还不明白它的选择。
18:04
So in a sense, it's getting ahead of us.
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从某种意义上说，电脑比我们超前。
18:06
We're now using those programs
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我们现在正用这些程式
18:08
to make much faster computers
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创造更快的电脑
18:10
so that we'll be able to run this process much faster.
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以便能更快地运行这个进程。
18:13
So it's feeding back on itself.
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所以它是自我回馈的。
18:16
The thing is becoming faster
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这正变得更快，
18:18
and that's why I think it seems so confusing.
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这也是为什么我觉得电脑似乎很让人摸不清。
18:20
Because all of these technologies are feeding back on themselves.
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由于所有这些技术都回馈给自己。
18:23
We're taking off.
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我们正在起飞。
18:25
And what we are is we're at a point in time
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我们正是在时间的某一点，
18:28
which is analogous to when single-celled organisms
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这一点类似于单细胞有机体
18:30
were turning into multi-celled organisms.
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正转变成多细胞机体的时刻。
18:33
So we're the amoebas
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我们就像变形虫。
18:35
and we can't quite figure out what the hell this thing is we're creating.
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搞不清自己正在创造的是什么东西。
18:38
We're right at that point of transition.
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我们正在转折点上。
18:40
But I think that there really is something coming along after us.
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不过我认为一定有跟随着我们的东西。
18:43
I think it's very haughty of us
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我想它是很崇拜我们的，
18:45
to think that we're the end product of evolution.
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认为我们是进化的终极产物。
18:48
And I think all of us here
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我认为我们这所有人
18:50
are a part of producing
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都是繁衍的一部分，
18:52
whatever that next thing is.
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无论下一步是什么。
18:54
So lunch is coming along,
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午饭时间快到了，
18:56
and I think I will stop at that point,
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趁我还没被选走，
18:58
before I get selected out.
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我想我就在这里结束。
19:00
(Applause)
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（掌声）
ABOUT THE SPEAKER
Danny Hillis - Computer theoristInventor, scientist, author, engineer -- over his broad career, Danny Hillis has turned his ever-searching brain on an array of subjects, with surprising results.
Why you should listen
Danny Hillis is an inventor, scientist, author and engineer. While completing his doctorate at MIT, he pioneered the concept of parallel computers that is now the basis for graphics processors and cloud computing. He holds more than 300 US patents, covering parallel computers, disk arrays, forgery prevention methods, various electronic and mechanical devices, and the pinch-to-zoom display interface. He has recently been working on problems in medicine as well. He is also the designer of a 10,000-year mechanical clock, and he gave a TED Talk in 1994 that is practically prophetic. Throughout his career, Hillis has worked at places like Disney, and now MIT and Applied Invention, always looking for the next fascinating problem.
Danny Hillis | Speaker | TED.com