Danny Hillis: Back to the future (of 1994)

80,742 views ・ 2012-02-03

TED


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翻译人员: YANGYANG HU 校对人员: Angelia King
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
05:54
had a particular combination of chemicals in them
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里面含有特殊的化学构成
05:56
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
06:32
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.
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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
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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,
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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.
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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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当然,随机的指令序列
15:49
are very unlikely to sort numbers,
371
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非常不可能分类资料,
15:51
so none of them will really do it.
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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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我说,“电脑,
15:59
would you please now take the 10 percent
376
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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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完成得最好的。
16:04
Save those. Kill off the rest.
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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."
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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,
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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.
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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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(指令)说,“请重复这个过程。”
16:32
Score them again.
390
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再做一次。
16:34
Introduce some mutations perhaps.
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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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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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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
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结束时,我得到
16:54
that are absolutely perfect at sorting numbers.
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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)
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