Danny Hillis: Back to the future (of 1994)

80,708 views ・ 2012-02-03

TED


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譯者: yinxi zhang 審譯者: Zoe Chen
00:15
Because I usually take the role
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由於我經常
00:18
of trying to explain to people
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向人們解釋
00:20
how wonderful the new technologies
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即將到來的新科技
00:23
that are coming along are going to be,
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將會多麼的美妙
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們一起在這
00:28
I would tell you what I really think
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就讓我來說說我真正的想法
00:32
and try to look back and try to understand
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並試著回顧和理解
00:34
what is really going on here
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這到底是如何發生的
00:37
with these amazing jumps in technology
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有了這些科技上的驚人進步。
00:42
that seem so fast that we can barely keep on top of it.
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科技的進步似乎快到我們根本無法趕上它的腳步。
00:45
So I'm going to start out
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讓我先從這開始
00:47
by showing just one very boring technology slide.
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一頁很無趣的科技幻燈片。
00:50
And then, so if you can just turn on the slide that's on.
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然後現在可以放幻燈片了。(對工作人員說)
00:56
This is just a random slide
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這只是我從我的文件中
00:58
that I picked out of my file.
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隨機挑選出的一張。
01:00
What I want to show you is not so much the details of the slide,
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我想要你們看的並不是它的細節,
01:03
but the general form of it.
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而是它的總體形式。
01:05
This happens to be a slide of some analysis that we were doing
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這個是我們做的
01:08
about the power of RISC microprocessors
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關於RISC微處理器功率
01:11
versus the power of local area networks.
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與本地網路功率分析的幻燈片。
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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這頁幻燈片
01:18
like so many technology slides that we're used to,
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就像很多我們所熟悉的幻燈片一樣,
01:21
is a sort of a straight line
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是半對數曲線圖
01:23
on a semi-log curve.
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上的一條直線。
01:25
In other words, every step here
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也就是這裡的每一層,
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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大小的一級。
01:31
And this is a new thing
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在半對數曲線圖上
01:33
that we talk about technology
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討論科技,
01:35
on semi-log curves.
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這很新鮮。
01:37
Something really weird is going on here.
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這其中有點奇特。
01:39
And that's basically what I'm going to be talking about.
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這基本上是我接下來要說的。
01:42
So, if you could bring up the lights.
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(對工作人員)麻煩開一下燈。
01:47
If you could bring up the lights higher,
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請把燈開亮點,
01:49
because I'm just going to use a piece of paper here.
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因為我要用張紙。
01:52
Now why do we draw technology curves
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為什麼我們要用對數曲線
01:54
in semi-log curves?
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描繪科技曲線呢?
01:56
Well the answer is, if I drew it on a normal curve
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嗯,答案是,如果我用普通曲線畫,
01:59
where, let's say, this is years,
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我們說,這是年份,
02:01
this is time of some sort,
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這是某個時間,
02:03
and this is whatever measure of the technology
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這是我準備畫的
02:06
that I'm trying to graph,
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科技的某種測量值,
02:09
the graphs look sort of silly.
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這圖看起來有點傻。
02:12
They sort of go like this.
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就有點像是這樣。
02:15
And they don't tell us much.
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而且並沒有提供什麼資訊。
02:18
Now if I graph, for instance,
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現在,如果我畫,比如說,
02:21
some other technology, say transportation technology,
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另一種技術,像是交通運輸,
02:23
on a semi-log curve,
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在半對數曲線上,
02:25
it would look very stupid, it would look like a flat line.
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它看起來很蠢,會像條很平的線。
02:28
But when something like this happens,
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但是如果出現像這種
02:30
things are qualitatively changing.
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質變的情況。
02:32
So if transportation technology
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如果交通運輸技術
02:34
was moving along as fast as microprocessor technology,
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進步地像微處理器業一樣快的話,
02:37
then the day after tomorrow,
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那,後天
02:39
I would be able to get in a taxi cab
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我就能搭量計程車
02:41
and be in Tokyo in 30 seconds.
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然後在30秒內到東京。
02:43
It's not moving like that.
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但它並沒有進步得那麼快。
02:45
And there's nothing precedented
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在科技發展歷史中
02:47
in the history of technology development
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也沒有任何
02:49
of this kind of self-feeding growth
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這種自給自足,
02:51
where you go by orders of magnitude every few years.
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每幾年程度翻倍增長的先例。
02:54
Now the question that I'd like to ask is,
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現在我想要問的是,
02:57
if you look at these exponential curves,
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如果你觀察這些指數曲線,
03:00
they don't go on forever.
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他們並非永遠的持續下去。
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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改變得那麼快。
03:08
One of two things is going to happen.
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兩件事會發生,
03:11
Either it's going to turn into a sort of classical S-curve like this,
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要麼它會變成像這樣典型的S曲線
03:15
until something totally different comes along,
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直到完全不同的情況出現。
03:19
or maybe it's going to do this.
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或是會變成這樣。
03:21
That's about all it can do.
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這就是所有可能。
03:23
Now I'm an optimist,
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現在我是個樂觀主義者,
03:25
so I sort of think it's probably going to do something like that.
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所以我覺得它很有可能就會變成這樣。
03:28
If so, that means that what we're in the middle of right now
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如果是這樣,意味著我們目前所在的
03:31
is a transition.
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是過渡階段。
03:33
We're sort of on this line
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我們似乎在這條線上,
03:35
in a transition from the way the world used to be
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在世界從過去
03:37
to some new way that the world is.
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到將來的轉變中。
03:40
And so what I'm trying to ask, what I've been asking myself,
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所有我要問的,我一直在問自己的,
03:43
is what's this new way that the world is?
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就是這世界未來道路在哪?
03:46
What's that new state that the world is heading toward?
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它趨向的新時代是什麼樣的?
03:49
Because the transition seems very, very confusing
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由於這個變化似乎非常,非常迷惑人,
03:52
when we're right in the middle of it.
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當我們正處在其中時。
03:54
Now when I was a kid growing up,
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我小時候,長大過程中
03:57
the future was kind of the year 2000,
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未來就像是2000年,
04:00
and people used to talk about what would happen in the year 2000.
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人們都在討論2000年將會發生什麼。
04:04
Now here's a conference
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現在這個會議上,
04:06
in which people talk about the future,
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大家在談論未來,
04:08
and you notice that the future is still at about the year 2000.
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而且你能發現這未來指的還是那個"2000年"。
04:11
It's about as far as we go out.
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這就是我們能達到的程度。
04:13
So in other words, the future has kind of been shrinking
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換句話說,未來正在縮水,
04:16
one year per year
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一生中
04:19
for my whole lifetime.
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每年縮短一年。
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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我們都感覺到
04:26
that something's happening there.
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正在發生些什麼。
04:28
That transition is happening. We can all sense it.
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變化正在發生。我們都能查覺到。
04:30
And we know that it just doesn't make too much sense
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我們知道去考慮那未來的三,五十年
04:32
to think out 30, 50 years
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已經沒什麼意義了,
04:34
because everything's going to be so different
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因為每件事都將如此不同
04:37
that a simple extrapolation of what we're doing
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以至於推測將來
04:39
just doesn't make any sense at all.
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不再有意義。
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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那會是怎樣,
04:46
what that transition could be that we're going through.
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我們正在經歷的轉變會是怎樣。
04:49
Now in order to do that
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為達到這個目的,
04:52
I'm going to have to talk about a bunch of stuff
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我得介紹一堆東西
04:54
that really has nothing to do
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它們與
04:56
with technology and computers.
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科技和電腦完全無關。
04:58
Because I think the only way to understand this
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因為我決定理解這個的唯一方法
05:00
is to really step back
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就是回顧過去
05:02
and take a long time scale look at things.
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拉長時間軸去看。
05:04
So the time scale that I would like to look at this on
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而我所要看的時間軸
05:07
is the time scale of life on Earth.
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是以地球上生命的時間尺來看。
05:13
So I think this picture makes sense
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我想這幅圖合理了
05:15
if you look at it a few billion years at a time.
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如果你一次從幾十億年來看。
05:19
So if you go back
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如果回溯/所以如果你回溯個
05:21
about two and a half billion years,
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大概25億年,
05:23
the Earth was this big, sterile hunk of rock
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地球這麼大,貧瘠的大塊石頭
05:26
with a lot of chemicals floating around on it.
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上面浮著些化學物質。
05:29
And if you look at the way
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要是觀察
05:31
that the chemicals got organized,
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這些化學物質怎樣組合的,
05:33
we begin to get a pretty good idea of how they do it.
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我們開始弄明白它們怎麼形成的。
05:36
And I think that there's theories that are beginning to understand
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我想有些理論是從理解
05:39
about how it started with RNA,
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生命怎樣從核糖核酸演變開始,
05:41
but I'm going to tell a sort of simple story of it,
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但是我想講一個生命簡單的故事,
05:44
which is that, at that time,
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就是,在那個時候,
05:46
there were little drops of oil floating around
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有一滴滴的油四處浮動,
05:49
with all kinds of different recipes of chemicals in them.
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裡面有各種不同化學成分組合。
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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裡面含有特殊的化學構成
05:56
which caused them to incorporate chemicals from the outside
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這導致它們可以從外界聚集化學物質
05:59
and grow the drops of oil.
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並慢慢變大。
06:02
And those that were like that
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像這樣的油滴
06:04
started to split and divide.
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又開始分化,分離。
06:06
And those were the most primitive forms of cells in a sense,
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最原始的那些在某種程度上形成了細胞,
06:09
those little drops of oil.
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這些小小的油滴。
06:11
But now those drops of oil weren't really alive, as we say it now,
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但目前為止這些油滴不是真的活的,在我們現在看來,
06:14
because every one of them
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因為每一個
06:16
was a little random recipe of chemicals.
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都是化學物質的隨機合成。
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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都不是平均分佈
06:23
of the chemicals within them.
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內部的化學物。
06:25
And so every drop was a little bit different.
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所以每個油滴都有點不同。
06:28
In fact, the drops that were different in a way
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實際上,油滴不同的方式
06:30
that caused them to be better
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是讓它們能更好地
06:32
at incorporating chemicals around them,
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集成周圍的化合物,
06:34
grew more and incorporated more chemicals and divided more.
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長的更大,吸收更多,分裂更多。
06:37
So those tended to live longer,
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所以它們會活的更長,
06:39
get expressed more.
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表現的更多。
06:42
Now that's sort of just a very simple
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這就有點像個很簡單的
06:45
chemical form of life,
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生命的化學形式,
06:47
but when things got interesting
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但過程變得有趣
06:50
was when these drops
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是當這些油滴
06:52
learned a trick about abstraction.
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學會了一個提取資訊的技巧時。
06:55
Somehow by ways that we don't quite understand,
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不知怎麼用我們不能完全理解的方式,
06:58
these little drops learned to write down information.
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這些小油滴學會了記錄資訊。
07:01
They learned to record the information
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它們學會把
07:03
that was the recipe of the cell
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細胞形成的秘訣
07:05
onto a particular kind of chemical
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記錄到一種特殊物質上,
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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也就是說,它們想出了,
07:11
in this mindless sort of evolutionary way,
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以這種隨性的進化方式,
07:14
a form of writing that let them write down what they were,
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可以寫下它們是什麼的記錄方式,
07:17
so that that way of writing it down could get copied.
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以便這種記錄方式能被複製。
07:20
The amazing thing is that that way of writing
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驚奇的是這種記錄方式
07:23
seems to have stayed steady
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似乎可以保持穩定
07:25
since it evolved two and a half billion years ago.
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由於它25億年前演化出來的。
07:27
In fact the recipe for us, our genes,
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實際上我們,我們的基因的組成
07:30
is exactly that same code and that same way of writing.
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就是完全一樣的代碼,一樣的記錄方式。
07:33
In fact, every living creature is written
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事實上,任何生物都是
07:36
in exactly the same set of letters and the same code.
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用完全一樣的字母和代碼記錄下來的。
07:38
In fact, one of the things that I did
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實際上,我所做的
07:40
just for amusement purposes
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僅是為了娛樂效果的一件事
07:42
is we can now write things in this code.
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就是我們能用這個代碼記錄事件。
07:44
And I've got here a little 100 micrograms of white powder,
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我這有100微克的白粉,
07:50
which I try not to let the security people see at airports.
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我盡力不讓機場安檢人員發現它們。
07:54
(Laughter)
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(笑聲)
07:56
But this has in it --
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不過這裡面有代碼
07:58
what I did is I took this code --
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我所做的是我拿著這代碼
08:00
the code has standard letters that we use for symbolizing it --
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它裡面有我們用來標記它的標準字母,
08:03
and I wrote my business card onto a piece of DNA
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然後我把我的名片寫到一條去氧核糖核酸上
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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所以如果有人需要數百萬我的名片,
08:12
I have plenty for everyone in the room,
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我有足夠多分給在座每個人,
08:14
and, in fact, everyone in the world,
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甚至是全世界每個人,
08:16
and it's right here.
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就在這。
08:19
(Laughter)
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(笑聲)
08:26
If I had really been a egotist,
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要是我是個自大的人,
08:28
I would have put it into a virus and released it in the room.
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我就會把它放大病毒裡散步到屋子中。
08:31
(Laughter)
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(笑聲)
08:39
So what was the next step?
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所以下一步是什麼?
08:41
Writing down the DNA was an interesting step.
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記錄去氧核糖核酸是有趣的一步。
08:43
And that caused these cells --
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它導致了細胞的形成——
08:45
that kept them happy for another billion years.
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讓它們又高興了幾十億年。
08:47
But then there was another really interesting step
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不過還有個很有趣的環節
08:49
where things became completely different,
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事情開始變得完全不同,
08:52
which is these cells started exchanging and communicating information,
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那就是這些細胞開始交換和交流資訊,
08:55
so that they began to get communities of cells.
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從而形成細胞團體。
08:57
I don't know if you know this,
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我不知道你們是否知道這個,
08:59
but bacteria can actually exchange DNA.
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細菌實際上就可以交換去氧核糖核酸。
09:01
Now that's why, for instance,
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這就是為什麼,比如,
09:03
antibiotic resistance has evolved.
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演變出抗菌免疫。
09:05
Some bacteria figured out how to stay away from penicillin,
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有些細菌知道怎麼遠離青黴素,
09:08
and it went around sort of creating its little DNA information
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然後它創造它這點去氧核糖核酸資訊,
09:11
with other bacteria,
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並在別的細菌中到處遊走,
09:13
and now we have a lot of bacteria that are resistant to penicillin,
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現在我們有很多對青黴素免疫的細菌了,
09:16
because bacteria communicate.
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因為細菌會交流資訊。
09:18
Now what this communication allowed
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這樣,這些交流致使
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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在某種意義上,它們在同一條船上了;
09:24
they were synergistic.
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它們是協作的。
09:26
So they survived
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因此它們一起倖存下來
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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也就是說如果一個群落成功了,
09:32
all the individuals in that community
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所有群落裡的個體
09:34
were repeated more
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都能複製更多,
09:36
and they were favored by evolution.
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在進化更有利。
09:39
Now the transition point happened
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於是,轉捩點到了,
09:41
when these communities got so close
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當這些族群很親近時,
09:43
that, in fact, they got together
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事實上,它們聚集到一起
09:45
and decided to write down the whole recipe for the community
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並決定一起在一條去氧核糖核酸上
09:48
together on one string of DNA.
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寫下整個族群的成分譜。
09:51
And so the next stage that's interesting in life
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生命中下一個有趣的階段
09:53
took about another billion years.
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又要幾十億年。
09:55
And at that stage,
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在這個時期,
09:57
we have multi-cellular communities,
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有多細胞族群,
09:59
communities of lots of different types of cells,
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就是有很多種不同細胞的群落,
10:01
working together as a single organism.
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作為有機體一起合作。
10:03
And in fact, we're such a multi-cellular community.
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實際上,我們就是這樣的多細胞族群。
10:06
We have lots of cells
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我們有很多細胞,
10:08
that are not out for themselves anymore.
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它們不再是是只為自己存活。
10:10
Your skin cell is really useless
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皮膚細胞根本沒用,
10:13
without a heart cell, muscle cell,
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要是沒有心臟細胞,肌肉細胞,
10:15
a brain cell and so on.
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腦細胞等等。
10:17
So these communities began to evolve
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所以這些族群開始進化
10:19
so that the interesting level on which evolution was taking place
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這樣發生有趣的進化的
10:22
was no longer a cell,
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不再僅僅是單一細胞。
10:24
but a community which we call an organism.
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而是我們稱為機體的族群。
10:28
Now the next step that happened
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接下來發生
10:30
is within these communities.
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就是在這些族群中。
10:32
These communities of cells,
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這些細胞群落,
10:34
again, began to abstract information.
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再次,開始提取資訊。
10:36
And they began building very special structures
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它們開始構建非常特別的
10:39
that did nothing but process information within the community.
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專門處理群落內資訊的結構。
10:42
And those are the neural structures.
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這些就是神經結構。
10:44
So neurons are the information processing apparatus
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所以神經元是
10:47
that those communities of cells built up.
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這些細胞群建立的資訊處理儀器。
10:50
And in fact, they began to get specialists in the community
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實際上,群落裡開始出現專家
10:52
and special structures
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以及特殊結構
10:54
that were responsible for recording,
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負責記錄,
10:56
understanding, learning information.
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理解,學習資訊。
10:59
And that was the brains and the nervous system
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這就是這些細胞群的
11:01
of those communities.
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大腦和神經系統。
11:03
And that gave them an evolutionary advantage.
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這給了它們進化的有力條件。
11:05
Because at that point,
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因為這樣的話,
11:08
an individual --
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對每個個體——
11:11
learning could happen
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學習可以發生
11:13
within the time span of a single organism,
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在單個機體的時間範圍內,
11:15
instead of over this evolutionary time span.
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而不是整個進化時間跨度。
11:18
So an organism could, for instance,
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所以一個機體能夠,比如說,
11:20
learn not to eat a certain kind of fruit
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學會不吃某種水果
11:22
because it tasted bad and it got sick last time it ate it.
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因為它不好吃而且上次吃的覺得噁心。
11:26
That could happen within the lifetime of a single organism,
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這可以發生在一個機體的一生中,
11:29
whereas before they'd built these special information processing structures,
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然後在這種特殊信心處理結構建成前,
11:33
that would have had to be learned evolutionarily
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這得要進化學習
11:35
over hundreds of thousands of years
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千萬年,
11:38
by the individuals dying off that ate that kind of fruit.
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通過吃了這種水果前赴後繼死去的個體。
11:41
So that nervous system,
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所以神經系統,
11:43
the fact that they built these special information structures,
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生物組建這種特殊結構的事實,
11:46
tremendously sped up the whole process of evolution.
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極大地加速了進化的進程。
11:49
Because evolution could now happen within an individual.
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因為至此進化可以在個體中發生了。
11:52
It could happen in learning time scales.
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它能發生在學習的時間刻度內。
11:55
But then what happened
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但是接下來發生的
11:57
was the individuals worked out,
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是每個個體發現了,
11:59
of course, tricks of communicating.
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當然,交流的秘訣。
12:01
And for example,
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比如說,
12:03
the most sophisticated version that we're aware of is human language.
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我們所知道的最精密的版本就是人類語言。
12:06
It's really a pretty amazing invention if you think about it.
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想想看,這真是個奇妙的發明。
12:09
Here I have a very complicated, messy,
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我腦子裡有個很複雜,混亂,
12:11
confused idea in my head.
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疑惑的的想法。
12:14
I'm sitting here making grunting sounds basically,
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我坐在這,基本上就是吐字發聲,
12:17
and hopefully constructing a similar messy, confused idea in your head
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希望在你們頭腦裡建立一個類似的混亂
12:20
that bears some analogy to it.
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跟它有點類似的想法。
12:22
But we're taking something very complicated,
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但是我們正在把很複雜的東西
12:24
turning it into sound, sequences of sounds,
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轉化成聲音,一連串的聲音,
12:27
and producing something very complicated in your brain.
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並在你們大腦產生很複雜的東西。
12:31
So this allows us now
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所以現在這推動我們
12:33
to begin to start functioning
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開始運作,
12:35
as a single organism.
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作為單個機體。
12:38
And so, in fact, what we've done
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所以,實際上,我們已經完成的
12:41
is we, humanity,
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就是我們,人類,
12:43
have started abstracting out.
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開始抽離出來。
12:45
We're going through the same levels
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我們正在經歷多細胞機體經歷的
12:47
that multi-cellular organisms have gone through --
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相同的階段——
12:49
abstracting out our methods of recording,
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提取我們記錄,
12:52
presenting, processing information.
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展示,處理資訊的方式。
12:54
So for example, the invention of language
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比如說,語言的發明
12:56
was a tiny step in that direction.
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就是這個方向上很小一步。
12:59
Telephony, computers,
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電話,電腦,
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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都是我們的特殊機制,
13:06
that we've now built within our society
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我們正在社會裡構建
13:08
for handling that information.
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用來處理資訊的機制。
13:10
And it all connects us together
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這些都是把我們聯繫在一起,
13:13
into something
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變的
13:15
that is much bigger
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比我們之前
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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更有能力進化。
13:23
So now, evolution can take place
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所以,現在進化可以發生在
13:25
on a scale of microseconds.
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微秒的數量級上。
13:27
And you saw Ty's little evolutionary example
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你們看過泰伊的那個的進化的小例子
13:29
where he sort of did a little bit of evolution
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他好像就在你們眼前在卷積程式上
13:31
on the Convolution program right before your eyes.
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展現了一點進化了。
13:34
So now we've speeded up the time scales once again.
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所以現在我們再次加快時間跨度。
13:37
So the first steps of the story that I told you about
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我講的故事的第一步
13:39
took a billion years a piece.
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每一塊花費了幾十億年。
13:41
And the next steps,
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下一步,
13:43
like nervous systems and brains,
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像神經系統和大腦,
13:45
took a few hundred million years.
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消耗幾百萬年。
13:47
Then the next steps, like language and so on,
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再接下來,像語言等等,
13:50
took less than a million years.
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需要不到一百萬年。
13:52
And these next steps, like electronics,
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再下一步,像電子器件,
13:54
seem to be taking only a few decades.
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仿佛只要幾十年。
13:56
The process is feeding on itself
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這個過程是自給自足,
13:58
and becoming, I guess, autocatalytic is the word for it --
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並且變成,我猜,應該自我催化描述更合適——
14:01
when something reinforces its rate of change.
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當事物加快改變的速度。
14:04
The more it changes, the faster it changes.
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變化越多,變化就越快。
14:07
And I think that that's what we're seeing here in this explosion of curve.
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我想這就是我們在這看到的激增曲線。
14:10
We're seeing this process feeding back on itself.
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我們看到這個過程回饋到自己。
14:13
Now I design computers for a living,
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我現在工作就是自己設計電腦,
14:16
and I know that the mechanisms
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我知道用來設計電腦的
14:18
that I use to design computers
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這些機制
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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要是沒有近期電腦的進步。
14:25
So right now, what I do
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現在,我做的
14:27
is I design objects at such complexity
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是設計複雜到
14:30
that it's really impossible for me to design them in the traditional sense.
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不可能從傳統意義上設計的物體。
14:33
I don't know what every transistor in the connection machine does.
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我不知道連接機器上每個電晶體的作用。
14:37
There are billions of them.
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有幾十億電晶體。
14:39
Instead, what I do
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實際上,我所做的
14:41
and what the designers at Thinking Machines do
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思考機器的設計師們做的,
14:44
is we think at some level of abstraction
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我們認為是在某種程度的資訊抽取,
14:46
and then we hand it to the machine
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然後把它傳給機器
14:48
and the machine takes it beyond what we could ever do,
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而機器把它運用到超出我們所能做的範圍,
14:51
much farther and faster than we could ever do.
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而且比我們從前所做的更遠更快。
14:54
And in fact, sometimes it takes it by methods
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實際上,有時候他採用的方法
14:56
that we don't quite even understand.
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我們並不很懂。
14:59
One method that's particularly interesting
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有個尤其有趣
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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就是進化本身。
15:06
So what we do
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我們做的就是
15:08
is we put inside the machine
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在機器裡
15:10
a process of evolution
355
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放入一個進化進程,
15:12
that takes place on the microsecond time scale.
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這個進程在微妙級別上就能發生。
15:14
So for example,
357
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比如,
15:16
in the most extreme cases,
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大部分極端情況下,
15:18
we can actually evolve a program
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我們實際上能
15:20
by starting out with random sequences of instructions.
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通過從隨機的指令序列開始進化一個程式。
15:24
Say, "Computer, would you please make
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(就像)說“電腦,請你產生
15:26
a hundred million random sequences of instructions.
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一億隨機指令序列。
15:29
Now would you please run all of those random sequences of instructions,
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現在請你運行所有這些隨機指令列,
15:32
run all of those programs,
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運行所有程式,
15:34
and pick out the ones that came closest to doing what I wanted."
365
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並選出最接近我想要的。”
15:37
So in other words, I define what I wanted.
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也就是說,我定義我要什麼。
15:39
Let's say I want to sort numbers,
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假設我需要分類資料,
15:41
as a simple example I've done it with.
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這是個我用它試驗過的簡單例子。
15:43
So find the programs that come closest to sorting numbers.
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找到最接近資料分類的程式。
15:46
So of course, random sequences of instructions
370
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當然,隨機的指令序列
15:49
are very unlikely to sort numbers,
371
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很不可能分類資料,
15:51
so none of them will really do it.
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所有它們中沒有一個能完成。
15:53
But one of them, by luck,
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但是中間有一個,運氣很好,
15:55
may put two numbers in the right order.
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可能會把兩個數按順序排列。
15:57
And I say, "Computer,
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我說,“電腦,
15:59
would you please now take the 10 percent
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請你現在選出序列中百分之十
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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保存這些。刪掉其他的。
16:06
And now let's reproduce
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現在來複製
16:08
the ones that sorted numbers the best.
380
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資料分類得最好的這些。
16:10
And let's reproduce them by a process of recombination
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以類似交配的重組過程
16:13
analogous to sex."
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來複製他們。”
16:15
Take two programs and they produce children
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取兩個程式
16:18
by exchanging their subroutines,
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交換他們的副程式讓它們產生子女,
16:20
and the children inherit the traits of the subroutines of the two programs.
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這些子女繼承了兩個程式副程式的特徵。
16:23
So I've got now a new generation of programs
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所以我得到新一代的
16:26
that are produced by combinations
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由組合做的比較好的程式
16:28
of the programs that did a little bit better job.
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而產生的程式。
16:30
Say, "Please repeat that process."
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(指令)說,“請重複這個過程。”
16:32
Score them again.
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再做一次。
16:34
Introduce some mutations perhaps.
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可能引入一些突變。
16:36
And try that again and do that for another generation.
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再試一次並用在新的一代上。
16:39
Well every one of those generations just takes a few milliseconds.
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這一代上每個程式只需要幾毫秒。
16:42
So I can do the equivalent
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所以我在電腦上用幾分鐘
16:44
of millions of years of evolution on that
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能做等同於
16:46
within the computer in a few minutes,
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幾百萬年的進化過程,
16:49
or in the complicated cases, in a few hours.
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或者,情況複雜時,在幾小時內完成。
16:51
At the end of that, I end up with programs
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結束時,我得到
16:54
that are absolutely perfect at sorting numbers.
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絕對完美地分類資料的程式。
16:56
In fact, they are programs that are much more efficient
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實際上,這些程式比我手寫的
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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現在,如果我讀這些程式,
17:03
I can't tell you how they work.
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我說不出他們怎麼工作的。
17:05
I've tried looking at them and telling you how they work.
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我嘗試過閱讀並且解釋他們如何工作的。
17:07
They're obscure, weird programs.
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他們很抽象,奇怪。
17:09
But they do the job.
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但是他們能完成任務。
17:11
And in fact, I know, I'm very confident that they do the job
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實際上,我知道,我很有信心他們能完成任務
17:14
because they come from a line
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因為他們來自于一行
17:16
of hundreds of thousands of programs that did the job.
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上千萬能完成認為的程式。
17:18
In fact, their life depended on doing the job.
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事實上,他們的生命就是靠著這工作。
17:21
(Laughter)
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(笑聲)
17:26
I was riding in a 747
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我曾經有一次
17:28
with Marvin Minsky once,
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和馬文明斯基一起坐747,
17:30
and he pulls out this card and says, "Oh look. Look at this.
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他拿出一張卡,說,“看,看這。
17:33
It says, 'This plane has hundreds of thousands of tiny parts
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這上面說“本飛機有很多精密部件
17:37
working together to make you a safe flight.'
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協作,保障您飛行安全。”
17:41
Doesn't that make you feel confident?"
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這是不是讓你很有信心?”
17:43
(Laughter)
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(笑聲)
17:45
In fact, we know that the engineering process doesn't work very well
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事實上,我們知道工程過程複雜化
17:48
when it gets complicated.
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並不能很好工作。
17:50
So we're beginning to depend on computers
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所以我們開始依賴電腦
17:52
to do a process that's very different than engineering.
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來做與工程有很大不同的一個過程。
17:56
And it lets us produce things of much more complexity
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它能讓我們生產出
17:59
than normal engineering lets us produce.
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比普通工程能生產的更複雜的東西。
18:01
And yet, we don't quite understand the options of it.
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然而,我們還不明白他的選擇。
18:04
So in a sense, it's getting ahead of us.
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從某種意義上說,它比我們超前。
18:06
We're now using those programs
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我們現在正用這些程式
18:08
to make much faster computers
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創造更快的電腦
18:10
so that we'll be able to run this process much faster.
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以便能更快的運行這個進程。
18:13
So it's feeding back on itself.
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所以它是自我回饋的。
18:16
The thing is becoming faster
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這正變得更快,
18:18
and that's why I think it seems so confusing.
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這也是為什麼我覺得它似乎很讓人摸不清。
18:20
Because all of these technologies are feeding back on themselves.
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由於所有這些技術都回饋到自己。
18:23
We're taking off.
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我們正在起飛。
18:25
And what we are is we're at a point in time
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我們正是在時間的某一點,
18:28
which is analogous to when single-celled organisms
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這一點類似於單細胞機體
18:30
were turning into multi-celled organisms.
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正轉變成多細胞機體的時刻。
18:33
So we're the amoebas
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我們就像變形蟲,
18:35
and we can't quite figure out what the hell this thing is we're creating.
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搞不清自己正在創造的是什麼東西。
18:38
We're right at that point of transition.
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我們正在轉捩點上。
18:40
But I think that there really is something coming along after us.
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不過我認為一定有跟隨著我們的東西。
18:43
I think it's very haughty of us
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我想它是很崇拜我們的,
18:45
to think that we're the end product of evolution.
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認為我們是進化的終級產物。
18:48
And I think all of us here
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我認為我們這所有人
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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無論下一步是什麼。
18:54
So lunch is coming along,
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午飯時間快到了,
18:56
and I think I will stop at that point,
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趁我還沒被選走,
18:58
before I get selected out.
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我就在這停下。/我想我就在這裡結束。
19:00
(Applause)
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(掌聲)
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