TED2010
Stephen Wolfram: Computing a theory of all knowledge
史蒂芬•沃夫朗：計算一切的理論
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史蒂芬•沃夫朗是Mathematica的創始人，他所談的是關於他探究如何讓所有的知識都可計算－可搜尋，可處理，也可操控－的追尋之路。他的Wolfram Alpha搜索引擎之最終目的無非在於建構一個模型，可用來解說宇宙奠基的物理原則。
Stephen Wolfram - Scientist, inventor
Stephen Wolfram is the creator of Mathematica and Wolfram|Alpha, the author of A New Kind of Science, and the founder and CEO of Wolfram Research. Full bio
Stephen Wolfram is the creator of Mathematica and Wolfram|Alpha, the author of A New Kind of Science, and the founder and CEO of Wolfram Research. Full bio
Double-click the English transcript below to play the video.
00:16
So I want to talk today about an idea. It's a big idea.
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我今天要談的是一個想法，很大的想法
00:19
Actually, I think it'll eventually
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其實我認為這個想法
00:21
be seen as probably the single biggest idea
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終究會被視爲上個世紀
00:23
that's emerged in the past century.
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最具有意義的想法
00:25
It's the idea of computation.
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那就是計算的想法
00:27
Now, of course, that idea has brought us
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當然，這想法已為我們帶來
00:29
all of the computer technology we have today and so on.
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今日電腦科技上所有的成就等等
00:32
But there's actually a lot more to computation than that.
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但計算的想法其實並不止這些
00:35
It's really a very deep, very powerful, very fundamental idea,
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它實在很深入、很強又很基本
00:38
whose effects we've only just begun to see.
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我們才剛開始明白它的效應
00:41
Well, I myself have spent the past 30 years of my life
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我自己過去30年來
00:44
working on three large projects
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進行了三個大型計劃
00:46
that really try to take the idea of computation seriously.
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認真研究關於計算的想法
00:50
So I started off at a young age as a physicist
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早年我是物理學家
00:53
using computers as tools.
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把電腦當作工具使用
00:55
Then, I started drilling down,
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然後開始深入這個領域
00:57
thinking about the computations I might want to do,
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思考我想做的計算
00:59
trying to figure out what primitives they could be built up from
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試圖找出建構那些計算的基本要素
01:02
and how they could be automated as much as possible.
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以及如何盡量自動化那些計算
01:05
Eventually, I created a whole structure
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最後我創造出一個完整的架構
01:07
based on symbolic programming and so on
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建構在符號程式設計等等之上
01:09
that let me build Mathematica.
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這讓我建構了Mathematica
01:11
And for the past 23 years, at an increasing rate,
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其後23年來加快速度
01:13
we've been pouring more and more ideas
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將越來越多的想法和産能
01:15
and capabilities and so on into Mathematica,
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注入Mathematica
01:17
and I'm happy to say that that's led to many good things
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我很高興能說許多好東西由此産生
01:20
in R & D and education,
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應用到研發和教育方面
01:22
lots of other areas.
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以及其他許多領域上
01:24
Well, I have to admit, actually,
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我必須承認
01:26
that I also had a very selfish reason for building Mathematica:
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我建構Mathematica其實有個很自私的理由
01:29
I wanted to use it myself,
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我自己想利用它
01:31
a bit like Galileo got to use his telescope
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有點像四百年前
01:33
400 years ago.
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伽利略利用他的望遠鏡那樣
01:35
But I wanted to look not at the astronomical universe,
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但我並不想觀察天文的宇宙
01:38
but at the computational universe.
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而是想觀察計算的宇宙
01:41
So we normally think of programs as being
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通常我們認爲程式是
01:43
complicated things that we build
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我們爲了特定的目的
01:45
for very specific purposes.
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所建構出來的複雜東西
01:47
But what about the space of all possible programs?
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可是所有可能的程式之空間又如何呢？
01:50
Here's a representation of a really simple program.
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這裡有個極簡單的程式之代表式
01:53
So, if we run this program,
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如果跑這個程式
01:55
this is what we get.
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得到的就是這個結果
01:57
Very simple.
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很簡單
01:59
So let's try changing the rule
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那麽稍稍改變
02:01
for this program a little bit.
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這個程式的規則
02:03
Now we get another result,
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現在得到別的結果
02:05
still very simple.
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還是很簡單
02:07
Try changing it again.
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再改變一下看看
02:10
You get something a little bit more complicated.
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結果稍微複雜了一點
02:12
But if we keep running this for a while,
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但如果讓它再跑一陣子
02:14
we find out that although the pattern we get is very intricate,
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結果看來雖然錯綜複雜
02:17
it has a very regular structure.
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但具有很規律的結構
02:20
So the question is: Can anything else happen?
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那麼問題是：還能產生出別的東西嗎？
02:23
Well, we can do a little experiment.
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那麽來做個小小的實驗
02:25
Let's just do a little mathematical experiment, try and find out.
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小小的數學實驗－試試看就知道
02:29
Let's just run all possible programs
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我們來跑某種特殊類型
02:32
of the particular type that we're looking at.
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可能的所有程式
02:34
They're called cellular automata.
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此類程式叫細胞自動機
02:36
You can see a lot of diversity in the behavior here.
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這兒可看到許多不同的行爲表現
02:38
Most of them do very simple things,
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大多只能做出很簡單的東西
02:40
but if you look along all these different pictures,
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但逐一檢視所有這些圖片
02:42
at rule number 30,
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在規則30上可以看到
02:44
you start to see something interesting going on.
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開始發生有趣的情況
02:46
So let's take a closer look
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那麼仔細看看
02:48
at rule number 30 here.
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在規則30這裡
02:50
So here it is.
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就在這裡
02:52
We're just following this very simple rule at the bottom here,
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程式跑的是底下這個很簡單的規則
02:55
but we're getting all this amazing stuff.
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得到的可是如此驚人的東西
02:57
It's not at all what we're used to,
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這不是平常看得到的東西
02:59
and I must say that, when I first saw this,
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我必須說我第一次看到時
03:01
it came as a huge shock to my intuition.
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它對我的直覺造成很大的震撼
03:04
And, in fact, to understand it,
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事實上要理解這東西
03:06
I eventually had to create
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我最後不得不
03:08
a whole new kind of science.
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創造一個嶄新的科學
03:11
(Laughter)
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（笑聲）
03:13
This science is different, more general,
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這個科學如果有所不同
03:16
than the mathematics-based science that we've had
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那就是比起我們300年來
03:18
for the past 300 or so years.
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在數學基礎上建構的科學更為通泛
03:21
You know, it's always seemed like a big mystery:
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這向來有如謎團
03:23
how nature, seemingly so effortlessly,
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大自然怎麼會如此輕鬆
03:26
manages to produce so much
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自如地產出那麼多
03:28
that seems to us so complex.
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看來如此複雜的東西
03:31
Well, I think we've found its secret:
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我想我們已經找到其中的奧秘
03:34
It's just sampling what's out there in the computational universe
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只要在計算空間裡進行採樣
03:37
and quite often getting things like Rule 30
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往往就會找到像規則30
03:40
or like this.
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那樣的東西或像這樣的東西
03:44
And knowing that starts to explain
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瞭解到這一點
03:46
a lot of long-standing mysteries in science.
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便可開始解釋許多長久以來的科學謎題
03:49
It also brings up new issues, though,
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但這也帶來新的問題
03:51
like computational irreducibility.
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比方說計算上的不可分解性
03:54
I mean, we're used to having science let us predict things,
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我是說我們向來利用科學做預測
03:57
but something like this
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但是像這樣的東西
03:59
is fundamentally irreducible.
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基本上是不可分解的
04:01
The only way to find its outcome
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要看到結果的唯一辦法
04:03
is, effectively, just to watch it evolve.
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只能是看著它演化下去
04:06
It's connected to, what I call,
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它關係到我稱為
04:08
the principle of computational equivalence,
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「計算的等價」這個原則：
04:10
which tells us that even incredibly simple systems
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也就是，即便是極其簡單的系統
04:13
can do computations as sophisticated as anything.
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也能做出極其複雜的計算
04:16
It doesn't take lots of technology or biological evolution
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並不需要許多生物演化科技
04:19
to be able to do arbitrary computation;
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方能進行任意無常的計算
04:21
just something that happens, naturally,
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就這樣自自然然地
04:23
all over the place.
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到處發生了
04:25
Things with rules as simple as these can do it.
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具有這麼簡單規則的東西就行了
04:29
Well, this has deep implications
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這對於科學的極限
04:31
about the limits of science,
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具有深沉的暗示意涵
04:33
about predictability and controllability
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對於像是生物演化過程
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of things like biological processes or economies,
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或經濟的可預測及可控制性
04:38
about intelligence in the universe,
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對於宇宙中的智識
04:40
about questions like free will
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對於自由意志問題
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and about creating technology.
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以及對科技的創造都有暗示意涵
04:45
You know, in working on this science for many years,
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研究這門科學多年
04:47
I kept wondering,
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我始終有個異想
04:49
"What will be its first killer app?"
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應用這門科學能有何等驚人之舉？
04:51
Well, ever since I was a kid,
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打從孩提時代開始
04:53
I'd been thinking about systematizing knowledge
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我便想把知識系統化
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and somehow making it computable.
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將它化為可計算
04:57
People like Leibniz had wondered about that too
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三百年前
04:59
300 years earlier.
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萊布尼茲也有這個異想
05:01
But I'd always assumed that to make progress,
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但我原來的假設若要得到進展
05:03
I'd essentially have to replicate a whole brain.
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那根本就必須複製整個大腦
05:06
Well, then I got to thinking:
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我現在的想法是
05:08
This scientific paradigm of mine suggests something different --
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我這個科學思維隱含著不同的東西
05:11
and, by the way, I've now got
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另外順道一提
05:13
huge computation capabilities in Mathematica,
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Mathematica現在具有龐大的計算能力
05:16
and I'm a CEO with some worldly resources
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我是執行長，擁有世界上的一些資源
05:19
to do large, seemingly crazy, projects --
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可以用來進行看似瘋狂的大型計劃
05:22
So I decided to just try to see
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因此我決定試看看
05:24
how much of the systematic knowledge that's out there in the world
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外間世界到底有多少系統化的知識
05:27
we could make computable.
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可以被轉化成能夠計算
05:29
So, it's been a big, very complex project,
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這是一個很複雜的大計劃
05:31
which I was not sure was going to work at all.
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我原本也不確定是否可行
05:34
But I'm happy to say it's actually going really well.
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不過我很高興這個計劃進行得不錯
05:37
And last year we were able
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去年我們已經達到可以
05:39
to release the first website version
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公布第一個網站版的
05:41
of Wolfram Alpha.
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Wolfram Alpha
05:43
Its purpose is to be a serious knowledge engine
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其目的是要成為嚴肅的知識引擎
05:46
that computes answers to questions.
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能計算出解答，有求必應
05:49
So let's give it a try.
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那麼我們來試試看
05:51
Let's start off with something really easy.
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先從極為簡單的開始
05:53
Hope for the best.
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但願不會出糗
05:55
Very good. Okay.
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很好，可以
05:57
So far so good.
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到目前為止還順利
05:59
(Laughter)
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（笑聲）
06:02
Let's try something a little bit harder.
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再試一下稍微困難的
06:05
Let's do
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那麼...
06:07
some mathy thing,
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做點數學上的東西吧
06:10
and with luck it'll work out the answer
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運氣好的話會有解答
06:13
and try and tell us some interesting things
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試試看能不能告訴我們一些有趣的東西
06:15
things about related math.
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關於與數學相關的東西
06:17
We could ask it something about the real world.
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我們可以提問真實世界的東西
06:20
Let's say -- I don't know --
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比方說－隨便提問－
06:22
what's the GDP of Spain?
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西班牙的國內生產毛額是多少？
06:25
And it should be able to tell us that.
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這應該還能告訴我們
06:27
Now we could compute something related to this,
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也可以計算與此相關的東西
06:29
let's say ... the GDP of Spain
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比方說西班牙的國內生產毛額
06:31
divided by, I don't know,
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除以－隨便舉例－
06:33
the -- hmmm ...
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嗯...就說
06:35
let's say the revenue of Microsoft.
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除以微軟的營業額
06:37
(Laughter)
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（笑聲）
06:39
The idea is that we can just type this in,
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不管對問題有何想法
06:41
this kind of question in, however we think of it.
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重點是，想提什麼問題都可以輸入
06:44
So let's try asking a question,
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那麼試試看提個問題
06:46
like a health related question.
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比方說與醫療保健相關的問題
06:48
So let's say we have a lab finding that ...
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那麼比方說化驗室發現
06:51
you know, we have an LDL level of 140
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一位50歲男子
06:53
for a male aged 50.
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低密度脂蛋白水平達140
06:56
So let's type that in, and now Wolfram Alpha
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我們把這輸入Wolfram Alpha
06:58
will go and use available public health data
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搜尋所有公共醫療的資料
07:00
and try and figure out
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然後嘗試弄清楚
07:02
what part of the population that corresponds to and so on.
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哪部分人口符合這個情況等等
07:05
Or let's try asking about, I don't know,
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或是試試看－隨便舉例－
07:08
the International Space Station.
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比方說國際太空站
07:10
And what's happening here is that
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這裡發生的是
07:12
Wolfram Alpha is not just looking up something;
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Wolfram Alpha不只查出東西
07:14
it's computing, in real time,
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還計算出，實時計算出
07:17
where the International Space Station is right now at this moment,
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太空站目前所在的位置，現在的位置
07:20
how fast it's going, and so on.
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你們看它計算得多快
07:24
So Wolfram Alpha knows about lots and lots of kinds of things.
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Wolfram Alpha知道許許多多種東西
07:27
It's got, by now,
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目前涵蓋已經相當廣泛
07:29
pretty good coverage of everything you might find
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你可能查找的所有東西
07:31
in a standard reference library.
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全都在標準的參考資料庫裡
07:34
But the goal is to go much further
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但目標還在更遠的地方
07:36
and, very broadly, to democratize
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而且更廣泛地說就是要
07:39
all of this knowledge,
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民主化所有的這類知識
07:42
and to try and be an authoritative
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試圖在所有的領域中
07:44
source in all areas.
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成為權威
07:46
To be able to compute answers to specific questions that people have,
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為人們所提的特定問題計算出解答
07:49
not by searching what other people
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這並不是去搜尋
07:51
may have written down before,
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別人寫過的東西
07:53
but by using built in knowledge
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而是利用內建的知識
07:55
to compute fresh new answers to specific questions.
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為特定的問題計算出嶄新的解答
07:58
Now, of course, Wolfram Alpha
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當然，Wolfram Alpha是一個
08:00
is a monumentally huge, long-term project
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龐然大物的長期計劃
08:02
with lots and lots of challenges.
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會遭遇到許許多多的挑戰
08:04
For a start, one has to curate a zillion
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首先必須張羅極大量的
08:07
different sources of facts and data,
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不同的事實與資料的來源
08:10
and we built quite a pipeline of Mathematica automation
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我們為Mathematica建構相當強大的自動化安排
08:13
and human domain experts for doing this.
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還有人文領域的專家處理這方面問題
08:16
But that's just the beginning.
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但這只是開始而已
08:18
Given raw facts or data
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有了原始事實或資料
08:20
to actually answer questions,
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要真正回答問題
08:22
one has to compute:
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還必須進行計算
08:24
one has to implement all those methods and models
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必須建構所有那些方法和模型
08:26
and algorithms and so on
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以及演算式等等
08:28
that science and other areas have built up over the centuries.
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幾個世紀以來科學和其他領域所建構的東西
08:31
Well, even starting from Mathematica,
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即使以Mathematica為基礎開始
08:34
this is still a huge amount of work.
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也還是很大量的工作
08:36
So far, there are about 8 million lines
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至今約有八百萬行的
08:38
of Mathematica code in Wolfram Alpha
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Mathematica編碼用在Wolfram Alpha裡
08:40
built by experts from many, many different fields.
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由許許多多領域的專家所建構
08:43
Well, a crucial idea of Wolfram Alpha
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Wolfram Alpha有一個關鍵性的想法
08:46
is that you can just ask it questions
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那就是你可以隨興
08:48
using ordinary human language,
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使用人類的語言提問
08:51
which means that we've got to be able to take
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那是說我們必須能夠解讀
08:53
all those strange utterances that people type into the input field
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人們輸入的所有那些奇怪的言語
08:56
and understand them.
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還要明白意思
08:58
And I must say that I thought that step
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我必須說我原本
09:00
might just be plain impossible.
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以為可能無法做到那個地步
09:04
Two big things happened:
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其間發生兩件重大的事
09:06
First, a bunch of new ideas about linguistics
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第一件是在進行計算宇宙的研究中
09:09
that came from studying the computational universe;
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我們取得了大量語言學上的見解
09:12
and second, the realization that having actual computable knowledge
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第二件是實現了
09:15
completely changes how one can
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擁有實際可計算的知識
09:17
set about understanding language.
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便會徹底改變人對語言理解的態度
09:20
And, of course, now
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當然，現在
09:22
with Wolfram Alpha actually out in the wild,
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Wolfram Alpha已經問世了
09:24
we can learn from its actual usage.
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我們能在實際使用中學習
09:26
And, in fact, there's been
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在Wolfram Alpha
09:28
an interesting coevolution that's been going on
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及其人類使用者之間
09:30
between Wolfram Alpha
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實際上存在著有趣的
09:32
and its human users,
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相輔相成的互動演進
09:34
and it's really encouraging.
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這很令人振奮
09:36
Right now, if we look at web queries,
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若此時看網站上的查詢
09:38
more than 80 percent of them get handled successfully the first time.
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80%以上在首次查詢就順利得到解答
09:41
And if you look at things like the iPhone app,
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若再較之於Phone之類的應用
09:43
the fraction is considerably larger.
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這個百分比已可說相當大了
09:45
So, I'm pretty pleased with it all.
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因此我對此感到相當欣慰
09:47
But, in many ways,
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不過在許多方面
09:49
we're still at the very beginning with Wolfram Alpha.
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我們還在Wolfram Alpha的開端
09:52
I mean, everything is scaling up very nicely
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我是說一切都在順利進展之中
09:54
and we're getting more confident.
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我們越來越有信心
09:56
You can expect to see Wolfram Alpha technology
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Wolfram Alpha的科技指日可待
09:58
showing up in more and more places,
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會在越來越多的地方出現
10:00
working both with this kind of public data, like on the website,
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利用像網站上的這類資料
10:03
and with private knowledge
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也會利用私有的知識
10:05
for people and companies and so on.
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為個人和公司等等進行工作
10:08
You know, I've realized that Wolfram Alpha actually gives one
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我實現了讓Wolfram Alpha真正
10:11
a whole new kind of computing
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給人嶄新的一種計算
10:13
that one can call knowledge-based computing,
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可稱之為以知識為基的計算
10:15
in which one's starting not just from raw computation,
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這種計算不僅從原始的計算開始
10:18
but from a vast amount of built-in knowledge.
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也從大量的內建知識開始進行
10:21
And when one does that, one really changes
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若是如此則會實際改變
10:23
the economics of delivering computational things,
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計算結果交付的經濟表現
10:26
whether it's on the web or elsewhere.
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無論是在網上或在其它地方
10:28
You know, we have a fairly interesting situation right now.
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各位知道，我們目前有一個蠻有趣的情況
10:31
On the one hand, we have Mathematica,
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在一方面，我們有Mathematica
10:33
with its sort of precise, formal language
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它使用精確的形式語言
10:36
and a huge network
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還有一個龐大的網絡
10:38
of carefully designed capabilities
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具有經過仔細設計的能力
10:40
able to get a lot done in just a few lines.
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能在極少行的編碼內做許多事
10:43
Let me show you a couple of examples here.
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讓大家看這裡的幾個例子
10:47
So here's a trivial piece of Mathematica programming.
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這是Mathematica的一個趣味雅程式設計
10:51
Here's something where we're sort of
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在這裡頭可以說
10:53
integrating a bunch of different capabilities here.
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我們融入了許多不同的能力
10:56
Here we'll just create, in this line,
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就在這行編碼裡，我們創造了一個
10:59
a little user interface that allows us to
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小小的使用者介面，讓我們能做出
11:02
do something fun there.
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一點好玩的事
11:05
If you go on, that's a slightly more complicated program
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若再仔細看看，那是稍微
11:07
that's now doing all sorts of algorithmic things
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複雜些的程式－用來處理所有的演算
11:10
and creating user interface and so on.
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並用來建構使用者介面等等
11:12
But it's something that is very precise stuff.
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但它是很精確的東西
11:15
It's a precise specification with a precise formal language
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是一個用精確形式語言表達的精確指示
11:18
that causes Mathematica to know what to do here.
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讓Mathematica知道在此該做什麼
11:21
Then on the other hand, we have Wolfram Alpha,
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然後在另一方面，我們有Wolfram Alpha
11:24
with all the messiness of the world
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內建了世上的各式各樣紛亂
11:26
and human language and so on built into it.
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以及人類語言等等
11:28
So what happens when you put these things together?
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那麼把這些東西放在一起會發生什麼呢？
11:31
I think it's actually rather wonderful.
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我認為這其實是很美妙的
11:33
With Wolfram Alpha inside Mathematica,
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把Wolfram Alpha放到Mathematica裡
11:35
you can, for example, make precise programs
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就能做出精確的程式－比方說－
11:37
that call on real world data.
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用來調用真實世界的資料
11:39
Here's a real simple example.
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這兒有個簡單的實例
11:44
You can also just sort of give vague input
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這可以輸入不清晰的表述
11:47
and then try and have Wolfram Alpha
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然後嘗試讓Wolfram Alpha
11:49
figure out what you're talking about.
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弄清楚你說的是什麼
11:51
Let's try this here.
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試試看這個
11:53
But actually I think the most exciting thing about this
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但其實我認為在這頂上最令人興奮的
11:56
is that it really gives one the chance
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是它真的給予
11:58
to democratize programming.
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程式設計一個民主化的機會
12:01
I mean, anyone will be able to say what they want in plain language.
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我是說誰都可用平常語言說出他們所要的
12:04
Then, the idea is that Wolfram Alpha will be able to figure out
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然後－我們的想法是－Wolfram Alpha就能弄清楚
12:07
what precise pieces of code
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確實是哪一段編碼
12:09
can do what they're asking for
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能做到被要求做到的事情
12:11
and then show them examples that will let them pick what they need
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然後舉例讓使用者選擇他們所要的
12:14
to build up bigger and bigger, precise programs.
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以便逐步建構越來越大的精確程式
12:17
So, sometimes, Wolfram Alpha
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那麼，有時Wolfram Alpha
12:19
will be able to do the whole thing immediately
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可能馬上什麼都做好了
12:21
and just give back a whole big program that you can then compute with.
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回應出整個能用來計算的大型程式
12:24
Here's a big website
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那麼，這兒是個大網站
12:26
where we've been collecting lots of educational
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我們在這兒一直收集著許多教育性質的
12:29
and other demonstrations about lots of kinds of things.
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和其它許許多多種東西的示範
12:32
I'll show you one example here.
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那麼－隨便舉個例子－就這個好了
12:36
This is just an example of one of these computable documents.
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這只是可計算之文件例子中的一個
12:39
This is probably a fairly small
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這可能是一段相當短的
12:41
piece of Mathematica code
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能放在這兒跑的
12:43
that's able to be run here.
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Mathematica編碼
12:47
Okay. Let's zoom out again.
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好，把它縮小吧
12:50
So, given our new kind of science,
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那麼，有了的新科學
12:52
is there a general way to use it to make technology?
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就會有通泛的方法來建構科技嗎？
12:55
So, with physical materials,
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那麼，我們一向利用
12:57
we're used to going around the world
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物理材料來處理事物
12:59
and discovering that particular materials
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然後發現特殊的材料
13:01
are useful for particular
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有助於達到
13:03
technological purposes.
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特殊的科技目的等等
13:05
Well, it turns out we can do very much the same kind of thing
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結果發現在計算的空間裡
13:07
in the computational universe.
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我們也可以做到同樣的事
13:09
There's an inexhaustible supply of programs out there.
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那兒有取之不盡、用之不竭的程式
13:12
The challenge is to see how to
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挑戰則在於如何駕馭它們
13:14
harness them for human purposes.
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以達到人想要達到的目的
13:16
Something like Rule 30, for example,
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比方說規則30這樣的東西
13:18
turns out to be a really good randomness generator.
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真是個不錯的隨機產生器
13:20
Other simple programs are good models
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其它簡單的程式是不錯的模型
13:22
for processes in the natural or social world.
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用於處理自然世界或社群生活的事物
13:25
And, for example, Wolfram Alpha and Mathematica
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又比方說Wolfram Alpha和Mathematica
13:27
are actually now full of algorithms
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現今已充滿著演算式
13:29
that we discovered by searching the computational universe.
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都是在計算空間裡搜尋得來的
13:33
And, for example, this -- if we go back here --
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又比方說這個－我們回到這兒－
13:37
this has become surprisingly popular
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這個在作曲者之間
13:39
among composers
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已經意外地大受歡迎
13:41
finding musical forms by searching the computational universe.
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搜尋計算空間，以便找到音樂形式
13:45
In a sense, we can use the computational universe
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在某種意義上是
13:47
to get mass customized creativity.
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利用計算空間取得大量客製化的創造力
13:50
I'm hoping we can, for example,
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我希望甚至能夠－比方說－
13:52
use that even to get Wolfram Alpha
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利用它使Wolfram Alpha
13:54
to routinely do invention and discovery on the fly,
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能利用套式快速地進行發明與發現
13:57
and to find all sorts of wonderful stuff
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並找到各種美妙的事物
13:59
that no engineer
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這不是任何工程師
14:01
and no process of incremental evolution would ever come up with.
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任何逐步演化的流程所能做到的
14:05
Well, so, that leads to kind of an ultimate question:
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那麼，最終的問題是：
14:08
Could it be that someplace out there in the computational universe
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我們有可能在計算空間的某處
14:11
we might find our physical universe?
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找到我們的物理宇宙嗎？
14:14
Perhaps there's even some quite simple rule,
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也許我們的宇宙甚至有
14:16
some simple program for our universe.
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某種相當簡單的規則、相當簡單的程式
14:19
Well, the history of physics would have us believe
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然而，物理的歷史讓我們
14:21
that the rule for the universe must be pretty complicated.
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以為宇宙的規則肯定是相當複雜的
14:24
But in the computational universe,
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但在計算的空間裡
14:26
we've now seen how rules that are incredibly simple
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我們已經看到簡單得難以置信的規則
14:29
can produce incredibly rich and complex behavior.
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也能產出難以置信的豐富又複雜的行為
14:32
So could that be what's going on with our whole universe?
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我們整個宇宙莫非不也是如此產生的嗎？
14:36
If the rules for the universe are simple,
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如果宇宙的規則是簡單的
14:38
it's kind of inevitable that they have to be
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那麼無可避免地必須是
14:40
very abstract and very low level;
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很抽象也很低層次的規則
14:42
operating, for example, far below
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操作在－例如－遠低於
14:44
the level of space or time,
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空間或時間的層次之下
14:46
which makes it hard to represent things.
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這使得事物不容易表示
14:48
But in at least a large class of cases,
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但至少在某大類的情況下
14:50
one can think of the universe as being
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可以把宇宙想像為
14:52
like some kind of network,
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像是某種網絡那樣的東西
14:54
which, when it gets big enough,
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只要大到足夠的程度
14:56
behaves like continuous space
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其表現就會像是連綿的空間
14:58
in much the same way as having lots of molecules
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如同許多分子聚合在一起
15:00
can behave like a continuous fluid.
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就會表現得像是不間斷的流體
15:02
Well, then the universe has to evolve by applying
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那麼，宇宙的演進必須通過
15:05
little rules that progressively update this network.
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應用小小的規則逐步更新這個網絡
15:08
And each possible rule, in a sense,
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而每個可能的規則，某種意義上
15:10
corresponds to a candidate universe.
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相當於一個候選的宇宙
15:12
Actually, I haven't shown these before,
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其實，我以前還沒有展示過這些
15:16
but here are a few of the candidate universes
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不過請看我已經檢視過的
15:19
that I've looked at.
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這一些候選的宇宙
15:21
Some of these are hopeless universes,
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這些宇宙中有些毫無發展希望
15:23
completely sterile,
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完全沒有繁衍能力
15:25
with other kinds of pathologies like no notion of space,
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因為帶有他類的病因：
15:27
no notion of time, no matter,
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不具備空間或時間概念
15:30
other problems like that.
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不含有物質、其它問題等等
15:32
But the exciting thing that I've found in the last few years
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但我最近幾年發現最令人興奮的是
15:35
is that you actually don't have to go very far
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是：其實不必深遠
15:37
in the computational universe
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進入計算的空間
15:39
before you start finding candidate universes
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便會開始找到一些候選的宇宙
15:41
that aren't obviously not our universe.
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它們並不顯然不是我們的宇宙
15:44
Here's the problem:
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這裡有個問題：
15:46
Any serious candidate for our universe
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任何可嚴重考慮為我們的宇宙之候選者
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is inevitably full of computational irreducibility.
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無可避免地會充滿計算上的不可分解性
15:52
Which means that it is irreducibly difficult
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即是要弄清楚它的行為確切會是如何
15:55
to find out how it will really behave,
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以及它是否符合我們的
15:57
and whether it matches our physical universe.
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物理宇宙，這將會是無解的困難
16:01
A few years ago, I was pretty excited to discover
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幾年前，我相當興奮地發現
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that there are candidate universes with incredibly simple rules
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有些候選的宇宙具有難以置信的簡單規則
16:07
that successfully reproduce special relativity,
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它們成功地複製了狹義相對論
16:09
and even general relativity and gravitation,
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甚至複製了廣義相對論和重力現象
16:12
and at least give hints of quantum mechanics.
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還至少提示了量子力學的物理原則
16:15
So, will we find the whole of physics?
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那麼，我們會發現整個物理嗎？
16:17
I don't know for sure,
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這我還不能確定
16:19
but I think at this point it's sort of
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但我認為在這個節骨眼上
16:21
almost embarrassing not to at least try.
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如果連試都不試，那就太不好意思了
16:23
Not an easy project.
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這是不容易的計劃
16:25
One's got to build a lot of technology.
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必須建構出大量的科技
16:27
One's got to build a structure that's probably
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可能必須至少建構出
16:29
at least as deep as existing physics.
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像現有的物理那樣深入的結構
16:31
And I'm not sure what the best way to organize the whole thing is.
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我還不確定如何妥善組織這一切
16:34
Build a team, open it up, offer prizes and so on.
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組織團隊、對外開放、提供獎金等等
16:37
But I'll tell you, here today,
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但我現在就可以告訴各位
16:39
that I'm committed to seeing this project done,
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我決心投入實現這個計劃
16:41
to see if, within this decade,
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要看我們能否在這十年內
16:44
we can finally hold in our hands
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終於將我們的宇宙的規則
16:46
the rule for our universe
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掌握在手中
16:48
and know where our universe lies
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並得知我們的宇宙位於
16:50
in the space of all possible universes ...
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所有可能宇宙的空間中的何處
16:52
and be able to type into Wolfram Alpha, "the theory of the universe,"
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也能將宇宙的理論輸入Wolfram Alpha
16:55
and have it tell us.
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讓它來告訴我們
16:57
(Laughter)
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（笑聲）
17:00
So I've been working on the idea of computation
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那麼，我研究計算的想法
17:02
now for more than 30 years,
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至今已經超過30年
17:04
building tools and methods and turning intellectual ideas
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建構著工具和方法，並將心智思想
17:07
into millions of lines of code
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化為幾百萬行的程式編碼
17:09
and grist for server farms and so on.
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以及強力的伺服器聯合場等等
17:11
With every passing year,
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每過一個年
17:13
I realize how much more powerful
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我就越明白計算的想法
17:15
the idea of computation really is.
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實在有多麼強大
17:17
It's taken us a long way already,
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它已經帶領著我們走過長長的道路
17:19
but there's so much more to come.
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但是還會有許許多多事情發生
17:21
From the foundations of science
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從科學的基礎
17:23
to the limits of technology
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到科技的極限
17:25
to the very definition of the human condition,
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到人類狀況的精確定義
17:27
I think computation is destined to be
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我認為計算註定會是
17:29
the defining idea of our future.
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定義著我們的未來之想法
17:31
Thank you.
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謝謝大家聆聽
17:33
(Applause)
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（喝彩）
17:47
Chris Anderson: That was astonishing.
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克里斯•安德森：太令人驚訝了
17:49
Stay here. I've got a question.
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請留步，我有個問題請教
17:51
(Applause)
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（喝彩）
17:57
So, that was, fair to say, an astonishing talk.
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必須老實說，這場演講太令人驚訝了
18:01
Are you able to say in a sentence or two
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您是否能用一兩句話說明
18:04
how this type of thinking
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如何能在某一個點上
18:07
could integrate at some point
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將這種想法融入像弦理論
18:09
to things like string theory or the kind of things that people think of
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或人們所想的那些東西
18:11
as the fundamental explanations of the universe?
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使它成為能夠解釋宇宙的基礎呢？
18:14
Stephen Wolfram: Well, the parts of physics
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史蒂芬•沃夫朗：嗯
18:16
that we kind of know to be true,
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我們所知為真的那部分物理
18:18
things like the standard model of physics:
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比方說物理的標準模型
18:20
what I'm trying to do better reproduce the standard model of physics
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我試圖改善的是複製物理的標準模型
18:23
or it's simply wrong.
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或者說，錯的是
18:25
The things that people have tried to do in the last 25 years or so
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大約近25年來人們試圖
18:27
with string theory and so on
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利用弦理論等等所做的研究
18:29
have been an interesting exploration
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都是很有趣的探討
18:31
that has tried to get back to the standard model,
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那樣的研究試圖回歸到標準模型
18:34
but hasn't quite gotten there.
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但是並沒有達到理想
18:36
My guess is that some great simplifications of what I'm doing
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我想我正在做的，若加以大大簡化
18:39
may actually have considerable resonance
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實際上可能與弦理論裡所做的
18:42
with what's been done in string theory,
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會有相當的共鳴
18:44
but that's a complicated math thing
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不過那是很複雜的數學東西
18:47
that I don't yet know how it's going to work out.
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我還不知道它會達到怎樣的地步
18:50
CA: Benoit Mandelbrot is in the audience.
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克•安：貝諾特•曼德爾博特就在聽眾裡
18:52
He also has shown how complexity
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他也曾經演示如何從簡單的開始
18:54
can arise out of a simple start.
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發展出複雜的東西
18:56
Does your work relate to his?
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您的研究和他的有些相關嗎？
18:58
SW: I think so.
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史•沃：我想是有的
19:00
I view Benoit Mandelbrot's work
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我看過曼德爾博特的著作
19:02
as one of the founding contributions
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他的著作可以說是開創這個領域
19:05
to this kind of area.
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研究的奠基著作之一
19:08
Benoit has been particularly interested
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貝諾特對套疊式模式
19:10
in nested patterns, in fractals and so on,
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對不規則碎片等等特別有興趣
19:12
where the structure is something
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那種結構有點像
19:14
that's kind of tree-like,
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樹的分叉結構
19:16
and where there's sort of a big branch that makes little branches
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而且有那種大枝分成小枝
19:18
and even smaller branches and so on.
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又甚至分成更細的小枝等等
19:21
That's one of the ways
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那是逐步達到
19:23
that you get towards true complexity.
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真正複雜的一種方法
19:26
I think things like the Rule 30 cellular automaton
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我認為規則30那樣的細胞自動機
19:29
get us to a different level.
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把我們帶到一個不同的層次上
19:31
In fact, in a very precise way, they get us to a different level
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事實上，此類規則確實把我們帶到不同的層次上
19:34
because they seem to be things that are
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因為它們顯然有
19:37
capable of complexity
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繼續發展到極其複雜的能力
19:40
that's sort of as great as complexity can ever get ...
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那是複雜到不能再複雜的程度 ...
19:44
I could go on about this at great length, but I won't. (Laughter) (Applause)
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這點我還可以談很久，不過先到此為止了
19:47
CA: Stephen Wolfram, thank you.
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克•安：史蒂夫•沃夫朗，謝謝您
19:49
(Applause)
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（喝彩）
ABOUT THE SPEAKER
Stephen Wolfram - Scientist, inventorStephen Wolfram is the creator of Mathematica and Wolfram|Alpha, the author of A New Kind of Science, and the founder and CEO of Wolfram Research.
Why you should listen
Stephen Wolfram published his first scientific paper at the age of 15, and received his PhD in theoretical physics from Caltech by the age of 20. Having started to use computers in 1973, Wolfram rapidly became a leader in the emerging field of scientific computing.
In 1981 Wolfram became the youngest recipient of a MacArthur Prize Fellowship. He then set out on an ambitious new direction in science aimed at understanding the origins of complexity in nature. Wolfram's first key idea was to use computer experiments to study the behavior of simple computer programs known as cellular automata. This allowed him to make a series of startling discoveries about the origins of complexity.
Wolfram founded the first research center and the first journal in the field, Complex Systems, and began the development of Mathematica. Wolfram Research soon became a world leader in the software industry -- widely recognized for excellence in both technology and business.
Following the release of Mathematica Version 2 in 1991, Wolfram began to divide his time between Mathematica development and scientific research. Building on his work from the mid-1980s, and now with Mathematica as a tool, Wolfram made a rapid succession of major new discoveries, which he described in his book, A New Kind of Science.
Building on Mathematica, A New Kind of Science, and the success of Wolfram Research, Wolfram recently launched Wolfram|Alpha -- an ambitious, long-term project to make as much of the world's knowledge as possible computable, and accessible to everyone.
Stephen Wolfram | Speaker | TED.com