TED1994
Danny Hillis: Back to the future (of 1994)
丹尼 希利斯： 回到未來（1994）
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從那被放在TED很後面的檔案庫裡，丹尼 希利斯藉著將生命本身的演化和科技變化的腳步是如何且為什麼看似不斷的加速這兩點結合起來，然後簡單的論述了這個耐人尋味的看法。他所呈現的演說技巧或許看起來過時，但想法卻是相當切題且有意義的。
Danny Hillis - Computer theorist
Inventor, scientist, author, engineer -- over his broad career, Danny Hillis has turned his ever-searching brain on an array of subjects, with surprising results. Full bio
Inventor, scientist, author, engineer -- over his broad career, Danny Hillis has turned his ever-searching brain on an array of subjects, with surprising results. Full bio
Double-click the English transcript below to play the video.
00:15
Because I usually take the role
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由於我經常
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
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放入一個進化進程，
15:12
that takes place on the microsecond time scale.
356
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這個進程在微妙級別上就能發生。
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.
368
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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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3000
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.
372
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所有它們中沒有一個能完成。
15:53
But one of them, by luck,
373
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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
376
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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.
378
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保存這些。刪掉其他的。
16:06
And now let's reproduce
379
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現在來複製
16:08
the ones that sorted numbers the best.
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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）
ABOUT THE SPEAKER
Danny Hillis - Computer theoristInventor, scientist, author, engineer -- over his broad career, Danny Hillis has turned his ever-searching brain on an array of subjects, with surprising results.
Why you should listen
Danny Hillis is an inventor, scientist, author and engineer. While completing his doctorate at MIT, he pioneered the concept of parallel computers that is now the basis for graphics processors and cloud computing. He holds more than 300 US patents, covering parallel computers, disk arrays, forgery prevention methods, various electronic and mechanical devices, and the pinch-to-zoom display interface. He has recently been working on problems in medicine as well. He is also the designer of a 10,000-year mechanical clock, and he gave a TED Talk in 1994 that is practically prophetic. Throughout his career, Hillis has worked at places like Disney, and now MIT and Applied Invention, always looking for the next fascinating problem.
Danny Hillis | Speaker | TED.com