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譯者: Wenjer Leuschel
審譯者: Zhu Jie
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
04:35
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
04:42
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
04:55
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
15:49
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
16:04
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)
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