ABOUT THE SPEAKER
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.

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.

More profile about the speaker
Stephen Wolfram | Speaker | TED.com
TED2010

Stephen Wolfram: Computing a theory of all knowledge

史蒂芬•沃夫朗:計算一切的理論

Filmed:
1,811,819 views

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。
- 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

Double-click the English transcript below to play the video.

00:16
So I want to talk today今天 about an idea理念. It's a big idea理念.
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我今天要談的是一個想法,很大的想法
00:19
Actually其實, I think it'll它會 eventually終於
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其實我認為這個想法
00:21
be seen看到 as probably大概 the single biggest最大 idea理念
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終究會被視爲上個世紀
00:23
that's emerged出現 in the past過去 century世紀.
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最具有意義的想法
00:25
It's the idea理念 of computation計算.
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那就是計算的想法
00:27
Now, of course課程, that idea理念 has brought us
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當然,這想法已為我們帶來
00:29
all of the computer電腦 technology技術 we have today今天 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其實 a lot more to computation計算 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強大, very fundamental基本的 idea理念,
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它實在很深入、很強又很基本
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whose誰的 effects效果 we've我們已經 only just begun開始 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花費 the past過去 30 years年份 of my life
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我自己過去30年來
00:44
working加工 on three large projects項目
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進行了三個大型計劃
00:46
that really try to take the idea理念 of computation計算 seriously認真地.
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認真研究關於計算的想法
00:50
So I started開始 off at a young年輕 age年齡 as a physicist物理學家
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早年我是物理學家
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using運用 computers電腦 as tools工具.
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把電腦當作工具使用
00:55
Then, I started開始 drilling鑽孔 down,
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然後開始深入這個領域
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thinking思維 about the computations計算 I might威力 want to do,
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思考我想做的計算
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trying to figure數字 out what primitives原語 they could be built內置 up from
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試圖找出建構那些計算的基本要素
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and how they could be automated自動化 as much as possible可能.
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以及如何盡量自動化那些計算
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Eventually終於, I created創建 a whole整個 structure結構體
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最後我創造出一個完整的架構
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based基於 on symbolic象徵 programming程序設計 and so on
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建構在符號程式設計等等之上
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that let me build建立 Mathematica數學.
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這讓我建構了Mathematica
01:11
And for the past過去 23 years年份, at an increasing增加 rate,
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其後23年來加快速度
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we've我們已經 been pouring澆注 more and more ideas思路
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將越來越多的想法和産能
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and capabilities功能 and so on into Mathematica數學,
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注入Mathematica
01:17
and I'm happy快樂 to say that that's led to many許多 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教育,
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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承認, actually其實,
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我必須承認
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that I also had a very selfish自私 reason原因 for building建造 Mathematica數學:
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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伽利略 got to use his telescope望遠鏡
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有點像四百年前
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400 years年份 ago.
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伽利略利用他的望遠鏡那樣
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But I wanted to look not at the astronomical天文 universe宇宙,
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但我並不想觀察天文的宇宙
01:38
but at the computational計算 universe宇宙.
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而是想觀察計算的宇宙
01:41
So we normally一般 think of programs程式 as being存在
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通常我們認爲程式是
01:43
complicated複雜 things that we build建立
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我們爲了特定的目的
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for very specific具體 purposes目的.
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所建構出來的複雜東西
01:47
But what about the space空間 of all possible可能 programs程式?
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可是所有可能的程式之空間又如何呢?
01:50
Here's這裡的 a representation表示 of a really simple簡單 program程序.
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這裡有個極簡單的程式之代表式
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So, if we run this program程序,
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如果跑這個程式
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this is what we get.
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得到的就是這個結果
01:57
Very simple簡單.
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很簡單
01:59
So let's try changing改變 the rule規則
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那麽稍稍改變
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for this program程序 a little bit.
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這個程式的規則
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Now we get another另一個 result結果,
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現在得到別的結果
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still very simple簡單.
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還是很簡單
02:07
Try changing改變 it again.
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再改變一下看看
02:10
You get something a little bit more complicated複雜.
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結果稍微複雜了一點
02:12
But if we keep running賽跑 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雖然 the pattern模式 we get is very intricate錯綜複雜,
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結果看來雖然錯綜複雜
02:17
it has a very regular定期 structure結構體.
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但具有很規律的結構
02:20
So the question is: Can anything else其他 happen發生?
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那麼問題是:還能產生出別的東西嗎?
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Well, we can do a little experiment實驗.
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那麽來做個小小的實驗
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Let's just do a little mathematical數學的 experiment實驗, 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可能 programs程式
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我們來跑某種特殊類型
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of the particular特定 type類型 that we're looking at.
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可能的所有程式
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They're called cellular細胞的 automata自動機.
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此類程式叫細胞自動機
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You can see a lot of diversity多樣 in the behavior行為 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簡單 things,
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大多只能做出很簡單的東西
02:40
but if you look along沿 all these different不同 pictures圖片,
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但逐一檢視所有這些圖片
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at rule規則 number 30,
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在規則30上可以看到
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you start開始 to see something interesting有趣 going on.
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開始發生有趣的情況
02:46
So let's take a closer接近 look
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那麼仔細看看
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at rule規則 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
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We're just following以下 this very simple簡單 rule規則 at the bottom底部 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得到 all this amazing驚人 stuff東東.
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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必須 say that, when I first saw this,
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我必須說我第一次看到時
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it came來了 as a huge巨大 shock休克 to my intuition直覺.
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它對我的直覺造成很大的震撼
03:04
And, in fact事實, to understand理解 it,
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事實上要理解這東西
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I eventually終於 had to create創建
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我最後不得不
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a whole整個 new kind of science科學.
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創造一個嶄新的科學
03:11
(Laughter笑聲)
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(笑聲)
03:13
This science科學 is different不同, more general一般,
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這個科學如果有所不同
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than the mathematics-based數學基礎 science科學 that we've我們已經 had
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那就是比起我們300年來
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for the past過去 300 or so years年份.
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在數學基礎上建構的科學更為通泛
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You know, it's always seemed似乎 like a big mystery神秘:
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這向來有如謎團
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how nature性質, seemingly似乎 so effortlessly毫不費力,
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大自然怎麼會如此輕鬆
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manages管理 to produce生產 so much
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自如地產出那麼多
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that seems似乎 to us so complex複雜.
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看來如此複雜的東西
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Well, I think we've我們已經 found發現 its secret秘密:
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我想我們已經找到其中的奧秘
03:34
It's just sampling採樣 what's out there in the computational計算 universe宇宙
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只要在計算空間裡進行採樣
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and quite相當 often經常 getting得到 things like Rule規則 30
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往往就會找到像規則30
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or like this.
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那樣的東西或像這樣的東西
03:44
And knowing會心 that starts啟動 to explain說明
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瞭解到這一點
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a lot of long-standing由來已久 mysteries奧秘 in science科學.
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便可開始解釋許多長久以來的科學謎題
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It also brings帶來 up new issues問題, though雖然,
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但這也帶來新的問題
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like computational計算 irreducibility不可約.
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比方說計算上的不可分解性
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I mean, we're used to having science科學 let us predict預測 things,
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我是說我們向來利用科學做預測
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but something like this
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但是像這樣的東西
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is fundamentally從根本上 irreducible束縛.
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基本上是不可分解的
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The only way to find its outcome結果
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要看到結果的唯一辦法
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is, effectively有效, just to watch it evolve發展.
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只能是看著它演化下去
04:06
It's connected連接的 to, what I call,
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它關係到我稱為
04:08
the principle原理 of computational計算 equivalence等價,
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「計算的等價」這個原則:
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which哪一個 tells告訴 us that even incredibly令人難以置信 simple簡單 systems系統
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也就是,即便是極其簡單的系統
04:13
can do computations計算 as sophisticated複雜的 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技術 or biological生物 evolution演化
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並不需要許多生物演化科技
04:19
to be able能夠 to do arbitrary隨意 computation計算;
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方能進行任意無常的計算
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just something that happens發生, naturally自然,
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就這樣自自然然地
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all over the place地點.
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到處發生了
04:25
Things with rules規則 as simple簡單 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啟示
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這對於科學的極限
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about the limits範圍 of science科學,
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具有深沉的暗示意涵
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about predictability預測 and controllability可控性
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對於像是生物演化過程
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of things like biological生物 processes流程 or economies經濟,
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或經濟的可預測及可控制性
04:38
about intelligence情報 in the universe宇宙,
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對於宇宙中的智識
04:40
about questions問題 like free自由 will
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對於自由意志問題
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and about creating創建 technology技術.
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以及對科技的創造都有暗示意涵
04:45
You know, in working加工 on this science科學 for many許多 years年份,
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研究這門科學多年
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I kept不停 wondering想知道,
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我始終有個異想
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"What will be its first killer兇手 app應用?"
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應用這門科學能有何等驚人之舉?
04:51
Well, ever since以來 I was a kid孩子,
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打從孩提時代開始
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I'd been thinking思維 about systematizing系統化 knowledge知識
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我便想把知識系統化
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and somehow不知何故 making製造 it computable可計算.
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將它化為可計算
04:57
People like Leibniz萊布尼茨 had wondered想知道 about that too
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三百年前
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300 years年份 earlier.
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萊布尼茲也有這個異想
05:01
But I'd always assumed假定 that to make progress進展,
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但我原來的假設若要得到進展
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I'd essentially實質上 have to replicate複製 a whole整個 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思維:
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我現在的想法是
05:08
This scientific科學 paradigm範例 of mine suggests提示 something different不同 --
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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巨大 computation計算 capabilities功能 in Mathematica數學,
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Mathematica現在具有龐大的計算能力
05:16
and I'm a CEOCEO with some worldly世俗 resources資源
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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似乎 crazy, projects項目 --
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可以用來進行看似瘋狂的大型計劃
05:22
So I decided決定 to just try to see
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因此我決定試看看
05:24
how much of the systematic系統的 knowledge知識 that's out there in the world世界
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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可計算.
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可以被轉化成能夠計算
05:29
So, it's been a big, very complex複雜 project項目,
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這是一個很複雜的大計劃
05:31
which哪一個 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快樂 to say it's actually其實 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能夠
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去年我們已經達到可以
05:39
to release發布 the first website網站 version
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公布第一個網站版的
05:41
of Wolfram AlphaΑ.
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Wolfram Alpha
05:43
Its purpose目的 is to be a serious嚴重 knowledge知識 engine發動機
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其目的是要成為嚴肅的知識引擎
05:46
that computes單位計算 answers答案 to questions問題.
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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開始 off with something really easy簡單.
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先從極為簡單的開始
05:53
Hope希望 for the best最好.
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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笑聲)
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(笑聲)
06:02
Let's try something a little bit harder更難.
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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 mathymathy thing,
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做點數學上的東西吧
06:10
and with luck運氣 it'll它會 work out the answer回答
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運氣好的話會有解答
06:13
and try and tell us some interesting有趣 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有關 math數學.
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關於與數學相關的東西
06:17
We could ask it something about the real真實 world世界.
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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 GDPGDP of Spain西班牙?
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西班牙的國內生產毛額是多少?
06:25
And it should be able能夠 to tell us that.
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這應該還能告訴我們
06:27
Now we could compute計算 something related有關 to this,
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也可以計算與此相關的東西
06:29
let's say ... the GDPGDP of Spain西班牙
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比方說西班牙的國內生產毛額
06:31
divided分為 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收入 of Microsoft微軟.
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除以微軟的營業額
06:37
(Laughter笑聲)
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(笑聲)
06:39
The idea理念 is that we can just type類型 this in,
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不管對問題有何想法
06:41
this kind of question in, however然而 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健康 related有關 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實驗室 finding發現 that ...
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那麼比方說化驗室發現
06:51
you know, we have an LDLLDL level水平 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類型 that in, and now Wolfram AlphaΑ
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我們把這輸入Wolfram Alpha
06:58
will go and use available可得到 public上市 health健康 data數據
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搜尋所有公共醫療的資料
07:00
and try and figure數字 out
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然後嘗試弄清楚
07:02
what part部分 of the population人口 that corresponds對應 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國際 Space空間 Station.
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比方說國際太空站
07:10
And what's happening事件 here is that
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這裡發生的是
07:12
Wolfram AlphaΑ is not just looking up something;
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Wolfram Alpha不只查出東西
07:14
it's computing計算, in real真實 time,
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還計算出,實時計算出
07:17
where the International國際 Space空間 Station is right now at this moment時刻,
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太空站目前所在的位置,現在的位置
07:20
how fast快速 it's going, and so on.
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你們看它計算得多快
07:24
So Wolfram AlphaΑ knows知道 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漂亮 good coverage覆蓋 of everything you might威力 find
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你可能查找的所有東西
07:31
in a standard標準 reference參考 library圖書館.
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全都在標準的參考資料庫裡
07:34
But the goal目標 is to go much further進一步
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但目標還在更遠的地方
07:36
and, very broadly寬廣地, to democratize民主化
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而且更廣泛地說就是要
07:39
all of this knowledge知識,
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民主化所有的這類知識
07:42
and to try and be an authoritative權威性
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試圖在所有的領域中
07:44
source資源 in all areas.
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成為權威
07:46
To be able能夠 to compute計算 answers答案 to specific具體 questions問題 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搜索 what other people
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這並不是去搜尋
07:51
may可能 have written書面 down before,
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別人寫過的東西
07:53
but by using運用 built內置 in knowledge知識
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而是利用內建的知識
07:55
to compute計算 fresh新鮮 new answers答案 to specific具體 questions問題.
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為特定的問題計算出嶄新的解答
07:58
Now, of course課程, Wolfram AlphaΑ
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當然,Wolfram Alpha是一個
08:00
is a monumentally身世 huge巨大, long-term長期 project項目
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龐然大物的長期計劃
08:02
with lots and lots of challenges挑戰.
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會遭遇到許許多多的挑戰
08:04
For a start開始, one has to curate策劃 a zillion無數
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首先必須張羅極大量的
08:07
different不同 sources來源 of facts事實 and data數據,
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不同的事實與資料的來源
08:10
and we built內置 quite相當 a pipeline管道 of Mathematica數學 automation自動化
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我們為Mathematica建構相當強大的自動化安排
08:13
and human人的 domain experts專家 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開始.
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但這只是開始而已
08:18
Given特定 raw生的 facts事實 or data數據
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有了原始事實或資料
08:20
to actually其實 answer回答 questions問題,
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要真正回答問題
08:22
one has to compute計算:
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還必須進行計算
08:24
one has to implement實行 all those methods方法 and models楷模
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必須建構所有那些方法和模型
08:26
and algorithms算法 and so on
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以及演算式等等
08:28
that science科學 and other areas have built內置 up over the centuries百年.
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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開始 from Mathematica數學,
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即使以Mathematica為基礎開始
08:34
this is still a huge巨大 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百萬 lines
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至今約有八百萬行的
08:38
of Mathematica數學 code in Wolfram AlphaΑ
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Mathematica編碼用在Wolfram Alpha裡
08:40
built內置 by experts專家 from many許多, many許多 different不同 fields領域.
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由許許多多領域的專家所建構
08:43
Well, a crucial關鍵 idea理念 of Wolfram AlphaΑ
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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問題
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那就是你可以隨興
08:48
using運用 ordinary普通 human人的 language語言,
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使用人類的語言提問
08:51
which哪一個 means手段 that we've我們已經 got to be able能夠 to take
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那是說我們必須能夠解讀
08:53
all those strange奇怪 utterances話語 that people type類型 into the input輸入 field領域
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人們輸入的所有那些奇怪的言語
08:56
and understand理解 them.
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還要明白意思
08:58
And I must必須 say that I thought that step
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我必須說我原本
09:00
might威力 just be plain impossible不可能.
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以為可能無法做到那個地步
09:04
Two big things happened發生:
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其間發生兩件重大的事
09:06
First, a bunch of new ideas思路 about linguistics語言學
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第一件是在進行計算宇宙的研究中
09:09
that came來了 from studying研究 the computational計算 universe宇宙;
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我們取得了大量語言學上的見解
09:12
and second第二, the realization實現 that having actual實際 computable可計算 knowledge知識
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第二件是實現了
09:15
completely全然 changes變化 how one can
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擁有實際可計算的知識
09:17
set about understanding理解 language語言.
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便會徹底改變人對語言理解的態度
09:20
And, of course課程, now
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當然,現在
09:22
with Wolfram AlphaΑ actually其實 out in the wild野生,
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Wolfram Alpha已經問世了
09:24
we can learn學習 from its actual實際 usage用法.
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我們能在實際使用中學習
09:26
And, in fact事實, there's been
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在Wolfram Alpha
09:28
an interesting有趣 coevolution協同進化 that's been going on
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及其人類使用者之間
09:30
between之間 Wolfram AlphaΑ
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實際上存在著有趣的
09:32
and its human人的 users用戶,
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相輔相成的互動演進
09:34
and it's really encouraging鼓舞人心的.
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這很令人振奮
09:36
Right now, if we look at web捲筒紙 queries查詢,
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若此時看網站上的查詢
09:38
more than 80 percent百分 of them get handled處理 successfully順利 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蘋果手機 app應用,
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若再較之於Phone之類的應用
09:43
the fraction分數 is considerably相當 larger.
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這個百分比已可說相當大了
09:45
So, I'm pretty漂亮 pleased滿意 with it all.
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因此我對此感到相當欣慰
09:47
But, in many許多 ways方法,
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不過在許多方面
09:49
we're still at the very beginning開始 with Wolfram AlphaΑ.
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我們還在Wolfram Alpha的開端
09:52
I mean, everything is scaling縮放 up very nicely很好
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我是說一切都在順利進展之中
09:54
and we're getting得到 more confident信心.
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我們越來越有信心
09:56
You can expect期望 to see Wolfram AlphaΑ technology技術
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Wolfram Alpha的科技指日可待
09:58
showing展示 up in more and more places地方,
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會在越來越多的地方出現
10:00
working加工 both with this kind of public上市 data數據, like on the website網站,
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利用像網站上的這類資料
10:03
and with private私人的 knowledge知識
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也會利用私有的知識
10:05
for people and companies公司 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實現 that Wolfram AlphaΑ actually其實 gives one
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我實現了讓Wolfram Alpha真正
10:11
a whole整個 new kind of computing計算
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給人嶄新的一種計算
10:13
that one can call knowledge-based以知識為基礎 computing計算,
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可稱之為以知識為基的計算
10:15
in which哪一個 one's那些 starting開始 not just from raw生的 computation計算,
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這種計算不僅從原始的計算開始
10:18
but from a vast廣大 amount of built-in內建的 knowledge知識.
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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變化
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若是如此則會實際改變
10:23
the economics經濟學 of delivering交付 computational計算 things,
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計算結果交付的經濟表現
10:26
whether是否 it's on the web捲筒紙 or elsewhere別處.
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無論是在網上或在其它地方
10:28
You know, we have a fairly相當 interesting有趣 situation情況 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數學,
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在一方面,我們有Mathematica
10:33
with its sort分類 of precise精確, formal正式 language語言
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它使用精確的形式語言
10:36
and a huge巨大 network網絡
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還有一個龐大的網絡
10:38
of carefully小心 designed設計 capabilities功能
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具有經過仔細設計的能力
10:40
able能夠 to get a lot doneDONE in just a few少數 lines.
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能在極少行的編碼內做許多事
10:43
Let me show顯示 you a couple一對 of examples例子 here.
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讓大家看這裡的幾個例子
10:47
So here's這裡的 a trivial不重要的 piece of Mathematica數學 programming程序設計.
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這是Mathematica的一個趣味雅程式設計
10:51
Here's這裡的 something where we're sort分類 of
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在這裡頭可以說
10:53
integrating整合 a bunch of different不同 capabilities功能 here.
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我們融入了許多不同的能力
10:56
Here we'll just create創建, 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用戶 interface接口 that allows允許 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開玩笑 there.
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一點好玩的事
11:05
If you go on, that's a slightly more complicated複雜 program程序
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若再仔細看看,那是稍微
11:07
that's now doing all sorts排序 of algorithmic算法 things
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複雜些的程式-用來處理所有的演算
11:10
and creating創建 user用戶 interface接口 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精確 stuff東東.
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但它是很精確的東西
11:15
It's a precise精確 specification規範 with a precise精確 formal正式 language語言
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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原因 Mathematica數學 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 AlphaΑ,
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然後在另一方面,我們有Wolfram Alpha
11:24
with all the messiness雜亂 of the world世界
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內建了世上的各式各樣紛亂
11:26
and human人的 language語言 and so on built內置 into it.
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以及人類語言等等
11:28
So what happens發生 when you put these things together一起?
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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其實 rather wonderful精彩.
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我認為這其實是很美妙的
11:33
With Wolfram AlphaΑ inside Mathematica數學,
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把Wolfram Alpha放到Mathematica裡
11:35
you can, for example, make precise精確 programs程式
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就能做出精確的程式-比方說-
11:37
that call on real真實 world世界 data數據.
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用來調用真實世界的資料
11:39
Here's這裡的 a real真實 simple簡單 example.
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這兒有個簡單的實例
11:44
You can also just sort分類 of give vague模糊 input輸入
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這可以輸入不清晰的表述
11:47
and then try and have Wolfram AlphaΑ
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然後嘗試讓Wolfram Alpha
11:49
figure數字 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其實 I think the most exciting扣人心弦 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機會
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是它真的給予
11:58
to democratize民主化 programming程序設計.
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程式設計一個民主化的機會
12:01
I mean, anyone任何人 will be able能夠 to say what they want in plain language語言.
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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理念 is that Wolfram AlphaΑ will be able能夠 to figure數字 out
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然後-我們的想法是-Wolfram Alpha就能弄清楚
12:07
what precise精確 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顯示 them examples例子 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建立 up bigger and bigger, precise精確 programs程式.
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以便逐步建構越來越大的精確程式
12:17
So, sometimes有時, Wolfram AlphaΑ
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那麼,有時Wolfram Alpha
12:19
will be able能夠 to do the whole整個 thing immediately立即
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可能馬上什麼都做好了
12:21
and just give back a whole整個 big program程序 that you can then compute計算 with.
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回應出整個能用來計算的大型程式
12:24
Here's這裡的 a big website網站
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那麼,這兒是個大網站
12:26
where we've我們已經 been collecting蒐集 lots of educational教育性
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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示威 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顯示 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可計算 documents文件.
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這只是可計算之文件例子中的一個
12:39
This is probably大概 a fairly相當 small
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這可能是一段相當短的
12:41
piece of Mathematica數學 code
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能放在這兒跑的
12:43
that's able能夠 to be run here.
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Mathematica編碼
12:47
Okay. Let's zoom放大 out again.
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好,把它縮小吧
12:50
So, given特定 our new kind of science科學,
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那麼,有了的新科學
12:52
is there a general一般 way to use it to make technology技術?
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就會有通泛的方法來建構科技嗎?
12:55
So, with physical物理 materials物料,
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那麼,我們一向利用
12:57
we're used to going around the world世界
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物理材料來處理事物
12:59
and discovering發現 that particular特定 materials物料
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然後發現特殊的材料
13:01
are useful有用 for particular特定
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有助於達到
13:03
technological技術性 purposes目的.
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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相同 kind of thing
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結果發現在計算的空間裡
13:07
in the computational計算 universe宇宙.
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我們也可以做到同樣的事
13:09
There's an inexhaustible取之不盡,用之不竭 supply供應 of programs程式 out there.
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那兒有取之不盡、用之不竭的程式
13:12
The challenge挑戰 is to see how to
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挑戰則在於如何駕馭它們
13:14
harness馬俱 them for human人的 purposes目的.
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以達到人想要達到的目的
13:16
Something like Rule規則 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隨機性 generator發電機.
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真是個不錯的隨機產生器
13:20
Other simple簡單 programs程式 are good models楷模
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其它簡單的程式是不錯的模型
13:22
for processes流程 in the natural自然 or social社會 world世界.
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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 AlphaΑ and Mathematica數學
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又比方說Wolfram Alpha和Mathematica
13:27
are actually其實 now full充分 of algorithms算法
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現今已充滿著演算式
13:29
that we discovered發現 by searching搜索 the computational計算 universe宇宙.
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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成為 surprisingly出奇 popular流行
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這個在作曲者之間
13:39
among其中 composers作曲家
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已經意外地大受歡迎
13:41
finding發現 musical音樂 forms形式 by searching搜索 the computational計算 universe宇宙.
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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計算 universe宇宙
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在某種意義上是
13:47
to get mass customized定制 creativity創造力.
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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希望 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 AlphaΑ
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利用它使Wolfram Alpha
13:54
to routinely常規 do invention發明 and discovery發現 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排序 of wonderful精彩 stuff東東
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並找到各種美妙的事物
13:59
that no engineer工程師
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這不是任何工程師
14:01
and no process處理 of incremental增加的 evolution演化 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引線 to kind of an ultimate最終 question:
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那麼,最終的問題是:
14:08
Could it be that someplace某個地方 out there in the computational計算 universe宇宙
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我們有可能在計算空間的某處
14:11
we might威力 find our physical物理 universe宇宙?
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找到我們的物理宇宙嗎?
14:14
Perhaps也許 there's even some quite相當 simple簡單 rule規則,
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也許我們的宇宙甚至有
14:16
some simple簡單 program程序 for our universe宇宙.
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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歷史 of physics物理 would have us believe
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然而,物理的歷史讓我們
14:21
that the rule規則 for the universe宇宙 must必須 be pretty漂亮 complicated複雜.
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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計算 universe宇宙,
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但在計算的空間裡
14:26
we've我們已經 now seen看到 how rules規則 that are incredibly令人難以置信 simple簡單
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我們已經看到簡單得難以置信的規則
14:29
can produce生產 incredibly令人難以置信 rich豐富 and complex複雜 behavior行為.
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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整個 universe宇宙?
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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規則 for the universe宇宙 are simple簡單,
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如果宇宙的規則是簡單的
14:38
it's kind of inevitable必然 that they have to be
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那麼無可避免地必須是
14:40
very abstract抽象 and very low level水平;
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很抽象也很低層次的規則
14:42
operating操作, for example, far below下面
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操作在-例如-遠低於
14:44
the level水平 of space空間 or time,
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空間或時間的層次之下
14:46
which哪一個 makes品牌 it hard to represent代表 things.
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這使得事物不容易表示
14:48
But in at least最小 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宇宙 as being存在
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可以把宇宙想像為
14:52
like some kind of network網絡,
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像是某種網絡那樣的東西
14:54
which哪一個, when it gets得到 big enough足夠,
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只要大到足夠的程度
14:56
behaves的行為 like continuous連續 space空間
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其表現就會像是連綿的空間
14:58
in much the same相同 way as having lots of molecules分子
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如同許多分子聚合在一起
15:00
can behave表現 like a continuous連續 fluid流體.
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就會表現得像是不間斷的流體
15:02
Well, then the universe宇宙 has to evolve發展 by applying應用
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那麼,宇宙的演進必須通過
15:05
little rules規則 that progressively逐步 update更新 this network網絡.
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應用小小的規則逐步更新這個網絡
15:08
And each possible可能 rule規則, in a sense,
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而每個可能的規則,某種意義上
15:10
corresponds對應 to a candidate候選人 universe宇宙.
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相當於一個候選的宇宙
15:12
Actually其實, I haven't沒有 shown顯示 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少數 of the candidate候選人 universes宇宙
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不過請看我已經檢視過的
15:19
that I've looked看著 at.
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這一些候選的宇宙
15:21
Some of these are hopeless絕望 universes宇宙,
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這些宇宙中有些毫無發展希望
15:23
completely全然 sterile無菌,
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完全沒有繁衍能力
15:25
with other kinds of pathologies病理 like no notion概念 of space空間,
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因為帶有他類的病因:
15:27
no notion概念 of time, no matter,
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不具備空間或時間概念
15:30
other problems問題 like that.
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不含有物質、其它問題等等
15:32
But the exciting扣人心弦 thing that I've found發現 in the last few少數 years年份
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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其實 don't have to go very far
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是:其實不必深遠
15:37
in the computational計算 universe宇宙
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進入計算的空間
15:39
before you start開始 finding發現 candidate候選人 universes宇宙
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便會開始找到一些候選的宇宙
15:41
that aren't obviously明顯 not our universe宇宙.
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它們並不顯然不是我們的宇宙
15:44
Here's這裡的 the problem問題:
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這裡有個問題:
15:46
Any serious嚴重 candidate候選人 for our universe宇宙
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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必將 full充分 of computational計算 irreducibility不可約.
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無可避免地會充滿計算上的不可分解性
15:52
Which哪一個 means手段 that it is irreducibly不可還原 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表現,
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以及它是否符合我們的
15:57
and whether是否 it matches火柴 our physical物理 universe宇宙.
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物理宇宙,這將會是無解的困難
16:01
A few少數 years年份 ago, I was pretty漂亮 excited興奮 to discover發現
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幾年前,我相當興奮地發現
16:04
that there are candidate候選人 universes宇宙 with incredibly令人難以置信 simple簡單 rules規則
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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順利 reproduce複製 special特別 relativity相對論,
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它們成功地複製了狹義相對論
16:09
and even general一般 relativity相對論 and gravitation引力,
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甚至複製了廣義相對論和重力現象
16:12
and at least最小 give hints提示 of quantum量子 mechanics機械學.
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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整個 of physics物理?
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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分類 of
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但我認為在這個節骨眼上
16:21
almost幾乎 embarrassing尷尬 not to at least最小 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簡單 project項目.
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這是不容易的計劃
16:25
One's那些 got to build建立 a lot of technology技術.
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必須建構出大量的科技
16:27
One's那些 got to build建立 a structure結構體 that's probably大概
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可能必須至少建構出
16:29
at least最小 as deep as existing現有 physics物理.
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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最好 way to organize組織 the whole整個 thing is.
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我還不確定如何妥善組織這一切
16:34
Build建立 a team球隊, open打開 it up, offer提供 prizes獎品 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今天,
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但我現在就可以告訴各位
16:39
that I'm committed提交 to seeing眼看 this project項目 doneDONE,
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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最後 hold保持 in our hands
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終於將我們的宇宙的規則
16:46
the rule規則 for our universe宇宙
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掌握在手中
16:48
and know where our universe宇宙 lies
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並得知我們的宇宙位於
16:50
in the space空間 of all possible可能 universes宇宙 ...
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所有可能宇宙的空間中的何處
16:52
and be able能夠 to type類型 into Wolfram AlphaΑ, "the theory理論 of the universe宇宙,"
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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笑聲)
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(笑聲)
17:00
So I've been working加工 on the idea理念 of computation計算
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那麼,我研究計算的想法
17:02
now for more than 30 years年份,
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至今已經超過30年
17:04
building建造 tools工具 and methods方法 and turning車削 intellectual知識分子 ideas思路
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建構著工具和方法,並將心智思想
17:07
into millions百萬 of lines of code
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化為幾百萬行的程式編碼
17:09
and grist穀物 for server服務器 farms農場 and so on.
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以及強力的伺服器聯合場等等
17:11
With every一切 passing通過 year,
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每過一個年
17:13
I realize實現 how much more powerful強大
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我就越明白計算的想法
17:15
the idea理念 of computation計算 really is.
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實在有多麼強大
17:17
It's taken採取 us a long way already已經,
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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基金會 of science科學
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從科學的基礎
17:23
to the limits範圍 of technology技術
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到科技的極限
17:25
to the very definition定義 of the human人的 condition條件,
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到人類狀況的精確定義
17:27
I think computation計算 is destined注定 to be
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我認為計算註定會是
17:29
the defining確定 idea理念 of our future未來.
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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掌聲)
427
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(喝彩)
17:47
Chris克里斯 Anderson安德森: That was astonishing驚人.
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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掌聲)
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(喝彩)
17:57
So, that was, fair公平 to say, an astonishing驚人 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能夠 to say in a sentence句子 or two
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您是否能用一兩句話說明
18:04
how this type類型 of thinking思維
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如何能在某一個點上
18:07
could integrate整合 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理論 or the kind of things that people think of
435
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或人們所想的那些東西
18:11
as the fundamental基本的 explanations說明 of the universe宇宙?
436
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使它成為能夠解釋宇宙的基礎呢?
18:14
Stephen斯蒂芬 Wolfram: Well, the parts部分 of physics物理
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史蒂芬•沃夫朗:嗯
18:16
that we kind of know to be true真正,
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我們所知為真的那部分物理
18:18
things like the standard標準 model模型 of physics物理:
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比方說物理的標準模型
18:20
what I'm trying to do better reproduce複製 the standard標準 model模型 of physics物理
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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只是 wrong錯誤.
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或者說,錯的是
18:25
The things that people have tried試著 to do in the last 25 years年份 or so
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大約近25年來人們試圖
18:27
with string theory理論 and so on
443
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利用弦理論等等所做的研究
18:29
have been an interesting有趣 exploration勘探
444
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都是很有趣的探討
18:31
that has tried試著 to get back to the standard標準 model模型,
445
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那樣的研究試圖回歸到標準模型
18:34
but hasn't有沒有 quite相當 gotten得到 there.
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但是並沒有達到理想
18:36
My guess猜測 is that some great simplifications簡化 of what I'm doing
447
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我想我正在做的,若加以大大簡化
18:39
may可能 actually其實 have considerable大量 resonance諧振
448
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實際上可能與弦理論裡所做的
18:42
with what's been doneDONE in string theory理論,
449
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會有相當的共鳴
18:44
but that's a complicated複雜 math數學 thing
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不過那是很複雜的數學東西
18:47
that I don't yet然而 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
CACA: Benoit伯努瓦 Mandelbrot曼德爾布羅 is in the audience聽眾.
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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顯示 how complexity複雜
453
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他也曾經演示如何從簡單的開始
18:54
can arise出現 out of a simple簡單 start開始.
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發展出複雜的東西
18:56
Does your work relate涉及 to his?
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您的研究和他的有些相關嗎?
18:58
SWSW: I think so.
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史•沃:我想是有的
19:00
I view視圖 Benoit伯努瓦 Mandelbrot's曼德爾布羅的 work
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我看過曼德爾博特的著作
19:02
as one of the founding創建 contributions捐款
458
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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.
459
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研究的奠基著作之一
19:08
Benoit伯努瓦 has been particularly尤其 interested有興趣
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貝諾特對套疊式模式
19:10
in nested嵌套 patterns模式, in fractals分形 and so on,
461
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對不規則碎片等等特別有興趣
19:12
where the structure結構體 is something
462
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那種結構有點像
19:14
that's kind of tree-like樹狀,
463
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樹的分叉結構
19:16
and where there's sort分類 of a big branch that makes品牌 little branches分支機構
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而且有那種大枝分成小枝
19:18
and even smaller branches分支機構 and so on.
465
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又甚至分成更細的小枝等等
19:21
That's one of the ways方法
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那是逐步達到
19:23
that you get towards true真正 complexity複雜.
467
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真正複雜的一種方法
19:26
I think things like the Rule規則 30 cellular細胞的 automaton自動機
468
1151000
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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不同 level水平.
469
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把我們帶到一個不同的層次上
19:31
In fact事實, in a very precise精確 way, they get us to a different不同 level水平
470
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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似乎 to be things that are
471
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因為它們顯然有
19:37
capable of complexity複雜
472
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繼續發展到極其複雜的能力
19:40
that's sort分類 of as great as complexity複雜 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長度, but I won't慣於. (Laughter笑聲) (Applause掌聲)
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這點我還可以談很久,不過先到此為止了
19:47
CACA: Stephen斯蒂芬 Wolfram, thank you.
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克•安:史蒂夫•沃夫朗,謝謝您
19:49
(Applause掌聲)
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(喝彩)
Translated by Wenjer Leuschel
Reviewed by Zhu Jie

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ABOUT THE SPEAKER
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.

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.

More profile about the speaker
Stephen Wolfram | Speaker | TED.com