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

斯蒂芬.沃尔夫勒姆:计算万物的理论

Filmed:
1,811,819 views

斯蒂芬.沃尔夫勒姆,Mathematica的创始人,谈论了他试图通过搜索、处理及操作各种信息从而计算所有知识的构想。他的新一代搜索引擎,Wolfram Alpha,致力于对宇宙根本的物理问题进行建模和解释。
- 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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接下来,我今天想谈的是一个宏观理念。
00:19
Actually其实, I think it'll它会 eventually终于
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其实,我认为这个构想最终
00:21
be seen看到 as probably大概 the single biggest最大 idea理念
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会被视为上个世纪出现过的
00:23
that's emerged出现 in the past过去 century世纪.
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最伟大的理念
00:25
It's the idea理念 of computation计算.
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那就是计算的理念
00:27
Now, of course课程, that idea理念 has brought us
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现在,当然,这个理念已经带给我们
00:29
all of the computer电脑 technology技术 we have today今天 and so on.
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所有今天所拥有的电脑科技
00:32
But there's actually其实 a lot more to computation计算 than that.
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然而,除此之外,还有更多可以计算的事物。
00:35
It's really a very deep, very powerful强大, very fundamental基本的 idea理念,
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这真是个非常深刻,非常有用,非常基本的理念
00:38
whose谁的 effects效果 we've我们已经 only just begun开始 to see.
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而我们只是刚开始见证这个理念的作用
00:41
Well, I myself have spent花费 the past过去 30 years年份 of my life
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过去30年里,我致力于
00:44
working加工 on three large projects项目
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研究3个大型的项目
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that really try to take the idea理念 of computation计算 seriously认真地.
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这些项目认真地将计算的理念付诸实践
00:50
So I started开始 off at a young年轻 age年龄 as a physicist物理学家
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刚开始时我只是个年轻的物理学家
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using运用 computers电脑 as tools工具.
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运用电脑作为工具
00:55
Then, I started开始 drilling钻孔 down,
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然后,我开始深入
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thinking思维 about the computations计算 I might威力 want to do,
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思考我可能想做的计算
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trying to figure数字 out what primitives原语 they could be built内置 up from
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尝试找出可以加以演变的主数据类型
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and how they could be automated自动化 as much as possible可能.
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以及它们尽可能自动运行的方式
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Eventually终于, I created创建 a whole整个 structure结构体
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最终,我创立了整个架构
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based基于 on symbolic象征 programming程序设计 and so on
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基于符号编程等等
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that let me build建立 Mathematica数学.
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然后创造出了Mathematica
01:11
And for the past过去 23 years年份, at an increasing增加 rate,
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过去23年间,以逐年增长的态势
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we've我们已经 been pouring浇注 more and more ideas思路
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我们已经为Mathematica注入了
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and capabilities功能 and so on into Mathematica数学,
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越来越多的概念和性能
01:17
and I'm happy快乐 to say that that's led to many许多 good things
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而且我很高兴地说这带来了很多进步
01:20
in R & D and education教育,
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在研发和教育
01:22
lots of other areas.
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以及其他很多方面
01:24
Well, I have to admit承认, actually其实,
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当然,我必须承认,事实上
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that I also had a very selfish自私 reason原因 for building建造 Mathematica数学:
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我开发Mathematica也有个自私的原因
01:29
I wanted to use it myself,
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那就是我想要用它
01:31
a bit like Galileo伽利略 got to use his telescope望远镜
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就像伽利略在400年前
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400 years年份 ago.
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想要用望远镜一样
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But I wanted to look not at the astronomical天文 universe宇宙,
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但我想了解的不是天文宇宙
01:38
but at the computational计算 universe宇宙.
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而是可计算空间
01:41
So we normally一般 think of programs程式 as being存在
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通常我们觉得程序是
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complicated复杂 things that we build建立
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复杂的东西
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for very specific具体 purposes目的.
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我们编程有很多特定的目的
01:47
But what about the space空间 of all possible可能 programs程式?
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然而所有程序的空间又有多少呢?
01:50
Here's这里的 a representation表示 of a really simple简单 program程序.
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这里有个非常简单的程序
01:53
So, if we run this program程序,
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所以呢,如果我们运行这个程序
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this is what we get.
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这就是我们得到的结果
01:57
Very simple简单.
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很简单
01:59
So let's try changing改变 the rule规则
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接下来,我们稍微修改一下
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for this program程序 a little bit.
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这个程序的规则
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Now we get another另一个 result结果,
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我们便得到了另一个结果
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still very simple简单.
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仍旧非常简单
02:07
Try changing改变 it again.
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再试着改一下
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You get something a little bit more complicated复杂.
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你就看到稍微复杂一点的东西
02:12
But if we keep running赛跑 this for a while,
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不过如果我们把这个程序继续运行下去
02:14
we find out that although虽然 the pattern模式 we get is very intricate错综复杂,
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我们将发现,尽管我们获得的图案十分复杂
02:17
it has a very regular定期 structure结构体.
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但它具有有规律的结构
02:20
So the question is: Can anything else其他 happen发生?
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接下来的问题是:还能发生什么?
02:23
Well, we can do a little experiment实验.
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好,我们可以做个小实验
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Let's just do a little mathematical数学的 experiment实验, try and find out.
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来做个小的数学实验,试着找出规律
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Let's just run all possible可能 programs程式
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运行我们所关注的特定总类的
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of the particular特定 type类型 that we're looking at.
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所有可能的程序
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They're called cellular细胞的 automata自动机.
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他们被称为单元自动机
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You can see a lot of diversity多样 in the behavior行为 here.
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你能看到这里有各种各样的图案模式
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Most of them do very simple简单 things,
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大多数都很简单
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but if you look along沿 all these different不同 pictures图片,
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但是,如果你注意所有不同的图片
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at rule规则 number 30,
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在30号规则上
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you start开始 to see something interesting有趣 going on.
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你开始看见一些有趣的东西出现
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So let's take a closer接近 look
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所以我们仔细看一下
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at rule规则 number 30 here.
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在30号规则这里
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So here it is.
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就在这里
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We're just following以下 this very simple简单 rule规则 at the bottom底部 here,
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我们只是按照底部非常简单的规律
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but we're getting得到 all this amazing惊人 stuff东东.
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然而我们得到了惊人的结果
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It's not at all what we're used to,
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这与我们过去习惯的事物完全不同
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and I must必须 say that, when I first saw this,
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而且,我必须说,当我第一次看见它的时候
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it came来了 as a huge巨大 shock休克 to my intuition直觉.
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它让我直觉为之震惊
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And, in fact事实, to understand理解 it,
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实际上,为了理解它
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I eventually终于 had to create创建
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我们最终不得不建立
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a whole整个 new kind of science科学.
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一套全新的科学
03:11
(Laughter笑声)
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(笑声)
03:13
This science科学 is different不同, more general一般,
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这套科学是与众不同的,并且更加广义的
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than the mathematics-based数学基础 science科学 that we've我们已经 had
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比起已经存在的基于数学的其他科学来说
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for the past过去 300 or so years年份.
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在过去300年甚至更久的时间内
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You know, it's always seemed似乎 like a big mystery神秘:
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你知道的,它总是看似神秘
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how nature性质, seemingly似乎 so effortlessly毫不费力,
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自然毫不费力地
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manages管理 to produce生产 so much
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制造出如此多的东西
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that seems似乎 to us so complex复杂.
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让我们觉得如此复杂
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Well, I think we've我们已经 found发现 its secret秘密:
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于是,我觉得我们已经发现了其中的奥秘
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It's just sampling采样 what's out there in the computational计算 universe宇宙
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这只是我们能探索的计算空间的一个样本
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and quite相当 often经常 getting得到 things like Rule规则 30
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它们都像30号规则
03:40
or like this.
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或者像这个
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And knowing会心 that starts启动 to explain说明
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在知道这件事后,我们可以开始解释
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a lot of long-standing由来已久 mysteries奥秘 in science科学.
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很多科学中长期以来的谜团
03:49
It also brings带来 up new issues问题, though虽然,
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不过,它也带来新的问题
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like computational计算 irreducibility不可约.
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就像计算的不可化归性
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I mean, we're used to having science科学 let us predict预测 things,
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我的意思是我们曾习惯让科学帮我们预测一些事情
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but something like this
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但是像这样的事情
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is fundamentally从根本上 irreducible束缚.
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是根本不可简化的
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The only way to find its outcome结果
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发现它结果的唯一方法
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is, effectively有效, just to watch it evolve发展.
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实际上就是看着它演化
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It's connected连接的 to, what I call,
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与之相关的便是我所谓的
04:08
the principle原理 of computational计算 equivalence等价,
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计算等价性原则
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which哪一个 tells告诉 us that even incredibly令人难以置信 simple简单 systems系统
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它告诉我们即使超级简单的系统
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can do computations计算 as sophisticated复杂的 as anything.
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也能做极端复杂的计算
04:16
It doesn't take lots of technology技术 or biological生物 evolution演化
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不需要多先进的技术或是生物进化过程
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to be able能够 to do arbitrary随意 computation计算;
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就能使得它能够做任意的计算
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just something that happens发生, naturally自然,
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这就是自然发生的事情
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all over the place地点.
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随处可见
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Things with rules规则 as simple简单 as these can do it.
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有如此简单规则的东西能达此目的
04:29
Well, this has deep implications启示
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而且,这件事有深刻的意义
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about the limits范围 of science科学,
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涉及科学的极限
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about predictability预测 and controllability可控性
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概率论和控制论等
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of things like biological生物 processes流程 or economies经济,
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在生物进程或者经济方面发挥作用
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about intelligence情报 in the universe宇宙,
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还有关于宇宙中的智能
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about questions问题 like free自由 will
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关于自由意志
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and about creating创建 technology技术.
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以及创新技术的问题
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You know, in working加工 on this science科学 for many许多 years年份,
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从事这些科学工作很多年后
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I kept不停 wondering想知道,
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我开始思考
04:49
"What will be its first killer凶手 app应用?"
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第一个令人震惊的应用程序是什么?
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Well, ever since以来 I was a kid孩子,
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恩,甚至我还是孩子时
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I'd been thinking思维 about systematizing系统化 knowledge知识
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我就想过关于知识系统化的问题
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and somehow不知何故 making制造 it computable可计算.
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以及怎么让它变得可计算
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People like Leibniz莱布尼茨 had wondered想知道 about that too
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莱布尼兹之辈也已经想过这个问题
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300 years年份 earlier.
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在300年前
05:01
But I'd always assumed假定 that to make progress进展,
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但是我总是假设,为了进步,
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I'd essentially实质上 have to replicate复制 a whole整个 brain.
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我不得不克隆出整个大脑
05:06
Well, then I got to thinking思维:
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而现在,我想的是
05:08
This scientific科学 paradigm范例 of mine suggests提示 something different不同 --
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我的科学模式意味着不一样的东西。
05:11
and, by the way, I've now got
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并且,顺便提一下,我已经
05:13
huge巨大 computation计算 capabilities功能 in Mathematica数学,
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使Mathematica具备了超强的计算能力
05:16
and I'm a CEOCEO with some worldly世俗 resources资源
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并且,我是公司的首席执行官,拥有大量的资源
05:19
to do large, seemingly似乎 crazy, projects项目 --
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来做大型的,看似疯狂的项目。
05:22
So I decided决定 to just try to see
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所以,我决定尝试知道
05:24
how much of the systematic系统的 knowledge知识 that's out there in the world世界
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在这世界上,有多少系统化的知识
05:27
we could make computable可计算.
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是我们能够计算的
05:29
So, it's been a big, very complex复杂 project项目,
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所以,这是个大型、复杂的项目,
05:31
which哪一个 I was not sure was going to work at all.
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我不完全确定它是否可行
05:34
But I'm happy快乐 to say it's actually其实 going really well.
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但是我很高兴地说,它现在进行的不错
05:37
And last year we were able能够
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就在去年
05:39
to release发布 the first website网站 version
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我们发布了第一个网络版本的
05:41
of Wolfram AlphaΑ.
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Wolfram Alpha
05:43
Its purpose目的 is to be a serious严重 knowledge知识 engine发动机
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目的是提供一个专业的知识搜索引擎
05:46
that computes单位计算 answers答案 to questions问题.
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它为提问计算答案
05:49
So let's give it a try.
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所以呢,我们来试试看
05:51
Let's start开始 off with something really easy简单.
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让我们先试试简单的东西
05:53
Hope希望 for the best最好.
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希望没问题
05:55
Very good. Okay.
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非常好,没问题
05:57
So far so good.
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到目前为止,不错
05:59
(Laughter笑声)
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(笑声)
06:02
Let's try something a little bit harder更难.
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让我们试试难一点的东西
06:05
Let's do
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比如
06:07
some mathymathy thing,
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我们做点数学
06:10
and with luck运气 it'll它会 work out the answer回答
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希望它能幸运的计算出结果
06:13
and try and tell us some interesting有趣 things
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并且试着告诉我们一些
06:15
things about related有关 math数学.
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关于数学的有趣的事
06:17
We could ask it something about the real真实 world世界.
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我们可以问他一些现实生活的事情
06:20
Let's say -- I don't know --
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比如,--- 让我想想 -----
06:22
what's the GDPGDP of Spain西班牙?
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西班牙的国民生产总值是多少?
06:25
And it should be able能够 to tell us that.
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它应该能告诉我们
06:27
Now we could compute计算 something related有关 to this,
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现在我们能计算和它相关的事
06:29
let's say ... the GDPGDP of Spain西班牙
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比如西班牙的国民生产总值
06:31
divided分为 by, I don't know,
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除以, 让我想想
06:33
the -- hmmm ...
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06:35
let's say the revenue收入 of Microsoft微软.
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比如微软公司的收入
06:37
(Laughter笑声)
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(笑声)
06:39
The idea理念 is that we can just type类型 this in,
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想法就是我们输入一些好奇的问题
06:41
this kind of question in, however然而 we think of it.
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不论是什么奇怪的问题
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So let's try asking a question,
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所以,我们提个问题
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like a health健康 related有关 question.
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比如有关健康的问题
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So let's say we have a lab实验室 finding发现 that ...
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比如,跟据实验室数据
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you know, we have an LDLLDL level水平 of 140
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你知道的,有低密度脂蛋白浓度值是140的数据
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for a male aged 50.
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这是针对50多岁的男性
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So let's type类型 that in, and now Wolfram AlphaΑ
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我们输入这个,然后Wolfram Alpha
06:58
will go and use available可得到 public上市 health健康 data数据
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就会使用存在的公共健康数据库
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and try and figure数字 out
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来试着分析出
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what part部分 of the population人口 that corresponds对应 to and so on.
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这组数据对应哪部分人群等等
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Or let's try asking about, I don't know,
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或者我们可以问,让我想想
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the International国际 Space空间 Station.
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国际空间站的问题
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And what's happening事件 here is that
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结果就是
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Wolfram AlphaΑ is not just looking up something;
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Wolfram Alpha不仅在查找信息
07:14
it's computing计算, in real真实 time,
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它是在实时计算
07:17
where the International国际 Space空间 Station is right now at this moment时刻,
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国际空间站现在此刻的位置
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how fast快速 it's going, and so on.
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它运行的速度等等
07:24
So Wolfram AlphaΑ knows知道 about lots and lots of kinds of things.
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所以呢,Wolfram Alpha知道很多很多不同的事情
07:27
It's got, by now,
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到现在为止
07:29
pretty漂亮 good coverage覆盖 of everything you might威力 find
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它几乎可以很好的涵盖了你能在
07:31
in a standard标准 reference参考 library图书馆.
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一个标准图书馆中找到的知识
07:34
But the goal目标 is to go much further进一步
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不过,我们的目标远不止这些
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and, very broadly宽广地, to democratize民主化
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概括地说
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all of this knowledge知识,
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是要使所有的知识民主化
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and to try and be an authoritative权威性
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并且试着提供
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source资源 in all areas.
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所有领域中的权威资料
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To be able能够 to compute计算 answers答案 to specific具体 questions问题 that people have,
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使它能够计算人们特定问题的答案
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not by searching搜索 what other people
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不是靠搜索其他人
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may可能 have written书面 down before,
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之前可能写下的资料
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but by using运用 built内置 in knowledge知识
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而是使用内建知识
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to compute计算 fresh新鲜 new answers答案 to specific具体 questions问题.
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来对特定问题计算新的答案
07:58
Now, of course课程, Wolfram AlphaΑ
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现在,当然,Wolfram Alpha
08:00
is a monumentally身世 huge巨大, long-term长期 project项目
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是一个非常大型、长远的项目
08:02
with lots and lots of challenges挑战.
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面临着众多挑战
08:04
For a start开始, one has to curate策划 a zillion无数
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开始的时候,我们要收集数以万计的
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different不同 sources来源 of facts事实 and data数据,
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不同的事实来源和数据
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and we built内置 quite相当 a pipeline管道 of Mathematica数学 automation自动化
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而且,我们建立了Mathematica自动化流水线
08:13
and human人的 domain experts专家 for doing this.
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还有知识领域专家来做这件事
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But that's just the beginning开始.
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不过,这只是开始
08:18
Given特定 raw生的 facts事实 or data数据
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对于运用一些没有处理的事实和数据
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to actually其实 answer回答 questions问题,
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来解答实际问题
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one has to compute计算:
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一方面要计算
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one has to implement实行 all those methods方法 and models楷模
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另一方面要执行所有的方法、模型
08:26
and algorithms算法 and so on
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以及算法等等
08:28
that science科学 and other areas have built内置 up over the centuries百年.
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而科学以及其他领域于此已发展了数个世纪
08:31
Well, even starting开始 from Mathematica数学,
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甚至从Mathematica开始
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this is still a huge巨大 amount of work.
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这仍然是一项浩大工程
08:36
So far, there are about 8 million百万 lines线
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至今为止,有8百万行
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of Mathematica数学 code in Wolfram AlphaΑ
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Mathematica的代码写在Wolfram Alpha里
08:40
built内置 by experts专家 from many许多, many许多 different不同 fields领域.
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这些代码由很多来自不同领域的专家构建
08:43
Well, a crucial关键 idea理念 of Wolfram AlphaΑ
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Wolfram Alpha中的一个最重要的想法
08:46
is that you can just ask it questions问题
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是你可以问它问题
08:48
using运用 ordinary普通 human人的 language语言,
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使用普通人类语言
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which哪一个 means手段 that we've我们已经 got to be able能够 to take
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这意味着我们必须能够接受
08:53
all those strange奇怪 utterances话语 that people type类型 into the input输入 field领域
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人们输入所有的奇怪的文字
08:56
and understand理解 them.
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并理解它们
08:58
And I must必须 say that I thought that step
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我必须说我曾觉得做到那一步
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might威力 just be plain impossible不可能.
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相当不可能
09:04
Two big things happened发生:
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后来有了两大重要进步
09:06
First, a bunch of new ideas思路 about linguistics语言学
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首先是语言学上的很多新想法
09:09
that came来了 from studying研究 the computational计算 universe宇宙;
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来自于对计算空间的研究
09:12
and second第二, the realization实现 that having actual实际 computable可计算 knowledge知识
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其次,可计算知识的实现
09:15
completely全然 changes变化 how one can
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完全地改变了如何一个人能够
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set about understanding理解 language语言.
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开始理解语言
09:20
And, of course课程, now
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当然,现在
09:22
with Wolfram AlphaΑ actually其实 out in the wild野生,
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在浩瀚的网络中有了Wolfram Alpha
09:24
we can learn学习 from its actual实际 usage用法.
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我们就能学习它的使用方法
09:26
And, in fact事实, there's been
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实际上,一直都有
09:28
an interesting有趣 coevolution协同进化 that's been going on
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一个有趣的共同进化
09:30
between之间 Wolfram AlphaΑ
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发生在Wolfram Alpha
09:32
and its human人的 users用户,
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和用户之间
09:34
and it's really encouraging鼓舞人心的.
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并且,这相当鼓舞人心
09:36
Right now, if we look at web卷筒纸 queries查询,
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现在,对于任意网络搜索
09:38
more than 80 percent百分 of them get handled处理 successfully顺利 the first time.
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超过百分之80的搜索在第一时间就被成功处理。
09:41
And if you look at things like the iPhone苹果手机 app应用,
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如果你看看类似iPhone应用程序的东西
09:43
the fraction分数 is considerably相当 larger.
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那被成功搜索部分就相当大了
09:45
So, I'm pretty漂亮 pleased满意 with it all.
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所以我对此很满意
09:47
But, in many许多 ways方法,
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但是,从很多角度看
09:49
we're still at the very beginning开始 with Wolfram AlphaΑ.
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我们仍然处于Wolfram Alpha开发的初级阶段。
09:52
I mean, everything is scaling缩放 up very nicely很好
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我的意思是,每件事情的规模都在扩大
09:54
and we're getting得到 more confident信心.
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我们也变得更有信心
09:56
You can expect期望 to see Wolfram AlphaΑ technology技术
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你能期待看到Wolfram Alpha技术
09:58
showing展示 up in more and more places地方,
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在越来越多的地方使用
10:00
working加工 both with this kind of public上市 data数据, like on the website网站,
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既能使用公共数据,比如网站
10:03
and with private私人的 knowledge知识
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又能使用私人数据
10:05
for people and companies公司 and so on.
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给个人和公司等等提供服务
10:08
You know, I've realized实现 that Wolfram AlphaΑ actually其实 gives one
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我觉得Wolfram Alpha其实是一个
10:11
a whole整个 new kind of computing计算
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全新的计算方法
10:13
that one can call knowledge-based以知识为基础 computing计算,
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我们可以称之基于知识的计算
10:15
in which哪一个 one's那些 starting开始 not just from raw生的 computation计算,
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这种计算方法,不仅可以使用原始数据
10:18
but from a vast广大 amount of built-in内建的 knowledge知识.
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还能使用大量的内建知识
10:21
And when one does that, one really changes变化
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而且,一个能做这样计算的工具真的能够改变
10:23
the economics经济学 of delivering交付 computational计算 things,
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传递可计算事物的理论
10:26
whether是否 it's on the web卷筒纸 or elsewhere别处.
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无论在网络上或者是其他地方
10:28
You know, we have a fairly相当 interesting有趣 situation情况 right now.
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我们现在处于一个很有意思的状态
10:31
On the one hand, we have Mathematica数学,
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一方面,我们拥有Mathematica这个软件
10:33
with its sort分类 of precise精确, formal正式 language语言
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它有精确性,正规性
10:36
and a huge巨大 network网络
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以及大规模
10:38
of carefully小心 designed设计 capabilities功能
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设计仔细的功能网络
10:40
able能够 to get a lot doneDONE in just a few少数 lines线.
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用几行代码就能做很多事情
10:43
Let me show显示 you a couple一对 of examples例子 here.
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我来展示几个例子
10:47
So here's这里的 a trivial不重要的 piece of Mathematica数学 programming程序设计.
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这是Mathematica编程中很小的一段代码
10:51
Here's这里的 something where we're sort分类 of
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这里是我们整合
10:53
integrating整合 a bunch of different不同 capabilities功能 here.
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大量不同的功能
10:56
Here we'll just create创建, in this line线,
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这行,我们就能建立
10:59
a little user用户 interface接口 that allows允许 us to
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一个简单的用户界面
11:02
do something fun开玩笑 there.
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它允许我们做一些有趣的事情
11:05
If you go on, that's a slightly more complicated复杂 program程序
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如果你继续的话,那就出现一些更复杂的程序
11:07
that's now doing all sorts排序 of algorithmic算法 things
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这些程序在运行算法之类的程序
11:10
and creating创建 user用户 interface接口 and so on.
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并且建立用户界面等等
11:12
But it's something that is very precise精确 stuff东东.
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不过,这是非常精准的东西
11:15
It's a precise精确 specification规范 with a precise精确 formal正式 language语言
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它精准的命令需要精准的正式编程语言
11:18
that causes原因 Mathematica数学 to know what to do here.
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才能让Mathematica知道要干什么
11:21
Then on the other hand, we have Wolfram AlphaΑ,
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另一方面,我们拥有Wolfram Alpha
11:24
with all the messiness杂乱 of the world世界
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包含了世界上所有杂乱无章的东西
11:26
and human人的 language语言 and so on built内置 into it.
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以及人类语言等内建的知识体系
11:28
So what happens发生 when you put these things together一起?
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如果把他们放一起,会发生什么呢?
11:31
I think it's actually其实 rather wonderful精彩.
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我觉得真是非常棒
11:33
With Wolfram AlphaΑ inside Mathematica数学,
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Mathematica里有Wolfram Alpha,
11:35
you can, for example, make precise精确 programs程式
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你就能编写精准的程序
11:37
that call on real真实 world世界 data数据.
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来接触真实世界的数据
11:39
Here's这里的 a real真实 simple简单 example.
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这里有个很简单的例子
11:44
You can also just sort分类 of give vague模糊 input输入
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你可以只是输入模棱两可的话语
11:47
and then try and have Wolfram AlphaΑ
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试着让Wolfram Alpha
11:49
figure数字 out what you're talking about.
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来分析出你想研究的内容
11:51
Let's try this here.
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我们在这儿试试看
11:53
But actually其实 I think the most exciting扣人心弦 thing about this
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不过事实上我想最激动人心的事是
11:56
is that it really gives one the chance机会
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它给了我们一个机会
11:58
to democratize民主化 programming程序设计.
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来全民编程
12:01
I mean, anyone任何人 will be able能够 to say what they want in plain language语言.
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我的意思是,任何人都能用日常用语说话
12:04
Then, the idea理念 is that Wolfram AlphaΑ will be able能够 to figure数字 out
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关键在于,Wolfram Alpha能分析出
12:07
what precise精确 pieces of code
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什么样的精准代码
12:09
can do what they're asking for
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能符合人们要求的事情
12:11
and then show显示 them examples例子 that will let them pick what they need
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然后显示出样例来帮助人们找到想要的答案
12:14
to build建立 up bigger and bigger, precise精确 programs程式.
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由此建立越来越多的精准程序
12:17
So, sometimes有时, Wolfram AlphaΑ
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所以,有时候,Wolfram Alpha
12:19
will be able能够 to do the whole整个 thing immediately立即
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能够立即处理整个问题
12:21
and just give back a whole整个 big program程序 that you can then compute计算 with.
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然后仅仅回馈你能用来计算的整个大程序
12:24
Here's这里的 a big website网站
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这里有个大网站
12:26
where we've我们已经 been collecting搜集 lots of educational教育性
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这里,我们收集了很多关于教育等
12:29
and other demonstrations示威 about lots of kinds of things.
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各种事物的样例
12:32
I'll show显示 you one example here.
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我来展示一个例子,例如这个
12:36
This is just an example of one of these computable可计算 documents文件.
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这只是可计算文档的其中一个样例
12:39
This is probably大概 a fairly相当 small
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它是相当小的
12:41
piece of Mathematica数学 code
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一段Mathematica代码
12:43
that's able能够 to be run here.
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能在这里运行
12:47
Okay. Let's zoom放大 out again.
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我们再缩小一下
12:50
So, given特定 our new kind of science科学,
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所以,有了这个新版科学
12:52
is there a general一般 way to use it to make technology技术?
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存在一个通用的办法来用它革新技术吗?
12:55
So, with physical物理 materials物料,
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使用物理材料
12:57
we're used to going around the world世界
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我们过去常常遍步世界
12:59
and discovering发现 that particular特定 materials物料
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并发现特定材料
13:01
are useful有用 for particular特定
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用于特定的
13:03
technological技术性 purposes目的.
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技术目的等等。
13:05
Well, it turns out we can do very much the same相同 kind of thing
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结果,我们可以做很多差不多的事情
13:07
in the computational计算 universe宇宙.
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在这个可计算的世界中。
13:09
There's an inexhaustible取之不尽,用之不竭 supply供应 of programs程式 out there.
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有无穷无尽的程序资源在那儿。
13:12
The challenge挑战 is to see how to
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面临的挑战是如何
13:14
harness马具 them for human人的 purposes目的.
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让它们供人类使用
13:16
Something like Rule规则 30, for example,
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举个例子,一些像30号规则的东西
13:18
turns out to be a really good randomness随机性 generator发电机.
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结果可以是很好的随机生成器。
13:20
Other simple简单 programs程式 are good models楷模
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其他简单的程序是很好的模型
13:22
for processes流程 in the natural自然 or social社会 world世界.
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来处理自然世界或者社交活动的问题
13:25
And, for example, Wolfram AlphaΑ and Mathematica数学
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再比如,Wolfram Alpha和Mathematica
13:27
are actually其实 now full充分 of algorithms算法
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确实包含很多算法
13:29
that we discovered发现 by searching搜索 the computational计算 universe宇宙.
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我们通过搜索计算空间找到它们
13:33
And, for example, this -- if we go back here --
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再比如,我们返回到这里
13:37
this has become成为 surprisingly出奇 popular流行
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这个已经变成相当的流行
13:39
among其中 composers作曲家
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在作曲家间
13:41
finding发现 musical音乐 forms形式 by searching搜索 the computational计算 universe宇宙.
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通过搜索计算空间来找出音乐模式
13:45
In a sense, we can use the computational计算 universe宇宙
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某种意义上说,我们可以使用计算空间
13:47
to get mass customized定制 creativity创造力.
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来获得大量的个性化创造。
13:50
I'm hoping希望 we can, for example,
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我希望我们能够
13:52
use that even to get Wolfram AlphaΑ
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使用Wolfram Alpha
13:54
to routinely常规 do invention发明 and discovery发现 on the fly,
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来运行常规的发明和发现的过程
13:57
and to find all sorts排序 of wonderful精彩 stuff东东
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并且来找出所有令人惊讶的事情
13:59
that no engineer工程师
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这些事情没有一个工程师
14:01
and no process处理 of incremental增加的 evolution演化 would ever come up with.
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也没有一个渐进式演化的过程能够找出
14:05
Well, so, that leads引线 to kind of an ultimate最终 question:
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这些最终导向一个终极问题
14:08
Could it be that someplace某个地方 out there in the computational计算 universe宇宙
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有没有可能使这个计算空间
14:11
we might威力 find our physical物理 universe宇宙?
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与我们的物理世界相融合?
14:14
Perhaps也许 there's even some quite相当 simple简单 rule规则,
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也许存在简单的规则
14:16
some simple简单 program程序 for our universe宇宙.
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一些简单的程序,对于我们的物理世界来说。
14:19
Well, the history历史 of physics物理 would have us believe
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物理的历史让我们相信
14:21
that the rule规则 for the universe宇宙 must必须 be pretty漂亮 complicated复杂.
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宇宙的内部规则一定是很复杂的
14:24
But in the computational计算 universe宇宙,
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但是在计算空间中
14:26
we've我们已经 now seen看到 how rules规则 that are incredibly令人难以置信 simple简单
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我们已经看到那些规则惊人的简单
14:29
can produce生产 incredibly令人难以置信 rich丰富 and complex复杂 behavior行为.
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却能够产生非常丰富和复杂的结果
14:32
So could that be what's going on with our whole整个 universe宇宙?
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所以,这可能是我们的物理世界的本质吗?
14:36
If the rules规则 for the universe宇宙 are simple简单,
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如果这个宇宙的规则很简单
14:38
it's kind of inevitable必然 that they have to be
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不可避免的,他们一定是
14:40
very abstract抽象 and very low level水平;
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十分抽象以及初级
14:42
operating操作, for example, far below下面
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远远运行于
14:44
the level水平 of space空间 or time,
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时间、空间之下
14:46
which哪一个 makes品牌 it hard to represent代表 things.
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这种运行方法很难表现某种东西
14:48
But in at least最小 a large class of cases,
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但是至少,从其中一类大量的事例中
14:50
one can think of the universe宇宙 as being存在
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我们能把这个宇宙想成
14:52
like some kind of network网络,
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某种网络
14:54
which哪一个, when it gets得到 big enough足够,
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当它变得足够大时
14:56
behaves的行为 like continuous连续 space空间
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它表现得像一个连续空间
14:58
in much the same相同 way as having lots of molecules分子
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某种程度上就像很多分子
15:00
can behave表现 like a continuous连续 fluid流体.
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表现得像流体一样。
15:02
Well, then the universe宇宙 has to evolve发展 by applying应用
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之后,宇宙进化就要依靠
15:05
little rules规则 that progressively逐步 update更新 this network网络.
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应用这个网络中不断更新的简单规则。
15:08
And each possible可能 rule规则, in a sense,
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并且,每一个可能的规则,在某种程度上说,
15:10
corresponds对应 to a candidate候选人 universe宇宙.
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对应一个候选空间
15:12
Actually其实, I haven't没有 shown显示 these before,
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事实上,我之前从来没有展示过
15:16
but here are a few少数 of the candidate候选人 universes宇宙
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不过,这里有几个候选空间
15:19
that I've looked看着 at.
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我正在研究的
15:21
Some of these are hopeless绝望 universes宇宙,
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一些是没希望的空间
15:23
completely全然 sterile无菌,
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完全不能演化,
15:25
with other kinds of pathologies病理 like no notion概念 of space空间,
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包括很多缺点,例如没有空间的观念
15:27
no notion概念 of time, no matter,
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没有时间的概念,没有物质
15:30
other problems问题 like that.
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或者类似的其他问题
15:32
But the exciting扣人心弦 thing that I've found发现 in the last few少数 years年份
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但是,我近几年发现的最令人激动的事
15:35
is that you actually其实 don't have to go very far
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是你其实不必深入
15:37
in the computational计算 universe宇宙
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在计算空间中
15:39
before you start开始 finding发现 candidate候选人 universes宇宙
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你就能发现与我们的物理空间
15:41
that aren't obviously明显 not our universe宇宙.
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明显不同的候选空间
15:44
Here's这里的 the problem问题:
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问题在这里:
15:46
Any serious严重 candidate候选人 for our universe宇宙
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任何有可能的候选空间
15:49
is inevitably必将 full充分 of computational计算 irreducibility不可约.
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不可避免地充满了计算不可化归性,
15:52
Which哪一个 means手段 that it is irreducibly不可还原 difficult
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这意味着简化它的具体表现
15:55
to find out how it will really behave表现,
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是极其困难的
15:57
and whether是否 it matches火柴 our physical物理 universe宇宙.
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并且不易判断它是否符合我们的物理世界。
16:01
A few少数 years年份 ago, I was pretty漂亮 excited兴奋 to discover发现
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几年前,我非常兴奋地发现
16:04
that there are candidate候选人 universes宇宙 with incredibly令人难以置信 simple简单 rules规则
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有些候选空间具有极其简单的规则
16:07
that successfully顺利 reproduce复制 special特别 relativity相对论,
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却能成功再现狭义相对论
16:09
and even general一般 relativity相对论 and gravitation引力,
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和广义相对论以及重力
16:12
and at least最小 give hints提示 of quantum量子 mechanics机械学.
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而且至少还给出了量子力学的暗示。
16:15
So, will we find the whole整个 of physics物理?
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所以,我们将会发现整个物理学吗?
16:17
I don't know for sure,
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我不确定。
16:19
but I think at this point it's sort分类 of
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但是我觉得现在
16:21
almost几乎 embarrassing尴尬 not to at least最小 try.
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不去尝试的话真的是令人羞愧的。
16:23
Not an easy简单 project项目.
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虽然这不是件简单的事。
16:25
One's那些 got to build建立 a lot of technology技术.
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一方面要发展技术
16:27
One's那些 got to build建立 a structure结构体 that's probably大概
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一方面要建立架构
16:29
at least最小 as deep as existing现有 physics物理.
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这架构至少要达到现有物理学的深度。
16:31
And I'm not sure what the best最好 way to organize组织 the whole整个 thing is.
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而且,我不确定去整合整件事情最好的方法是什么。
16:34
Build建立 a team球队, open打开 it up, offer提供 prizes奖品 and so on.
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建立一个团队,运营它,还是提供奖励等等。
16:37
But I'll tell you, here today今天,
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但是,我今天要告诉你
16:39
that I'm committed提交 to seeing眼看 this project项目 doneDONE,
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我要把这个项目做完,
16:41
to see if, within this decade,
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要看看在这10年内
16:44
we can finally最后 hold保持 in our hands
404
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我们是否最终可以掌握
16:46
the rule规则 for our universe宇宙
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我们宇宙的规则
16:48
and know where our universe宇宙 lies
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并且知道我们宇宙在
16:50
in the space空间 of all possible可能 universes宇宙 ...
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所有可能的宇宙空间的位置
16:52
and be able能够 to type类型 into Wolfram AlphaΑ, "the theory理论 of the universe宇宙,"
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并且,能够在Wolfram Alpha中输入“宇宙理论”
16:55
and have it tell us.
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让它告诉我们结果。
16:57
(Laughter笑声)
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(笑声)
17:00
So I've been working加工 on the idea理念 of computation计算
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我已经在计算的这个想法上做了
17:02
now for more than 30 years年份,
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超过30年了研究
17:04
building建造 tools工具 and methods方法 and turning车削 intellectual知识分子 ideas思路
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打造工具,创立方法,将专业知识
17:07
into millions百万 of lines线 of code
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编写成数百万行的代码
17:09
and grist谷物 for server服务器 farms农场 and so on.
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在服务器中收获结果等等。
17:11
With every一切 passing通过 year,
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每过去一年
17:13
I realize实现 how much more powerful强大
417
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我都意识到
17:15
the idea理念 of computation计算 really is.
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计算的想法是多么的强大。
17:17
It's taken采取 us a long way already已经,
419
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它已引领我们走过很长一段路
17:19
but there's so much more to come.
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但是还有更多可以做的事情。
17:21
From the foundations基金会 of science科学
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从科学的根基
17:23
to the limits范围 of technology技术
422
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到技术的极限
17:25
to the very definition定义 of the human人的 condition条件,
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再到人类条件的定义,
17:27
I think computation计算 is destined注定 to be
424
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我觉得,计算注定
17:29
the defining确定 idea理念 of our future未来.
425
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是定义我们的未来的想法
17:31
Thank you.
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谢谢。
17:33
(Applause掌声)
427
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(鼓掌)
17:47
Chris克里斯 Anderson安德森: That was astonishing惊人.
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Chris Anderson(克里斯 安德森):太令人惊讶了。
17:49
Stay here. I've got a question.
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别走,我有问题。
17:51
(Applause掌声)
430
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(鼓掌)
17:57
So, that was, fair公平 to say, an astonishing惊人 talk.
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说实在的,那真的是很惊人的演讲。
18:01
Are you able能够 to say in a sentence句子 or two
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您能用一两句话概括
18:04
how this type类型 of thinking思维
433
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这种思考方式如何
18:07
could integrate整合 at some point
434
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能在某些点上整合
18:09
to things like string theory理论 or the kind of things that people think of
435
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一些如弦论或者
18:11
as the fundamental基本的 explanations说明 of the universe宇宙?
436
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人们在思考的一些关于根本宇宙解释的问题?
18:14
Stephen斯蒂芬 Wolfram: Well, the parts部分 of physics物理
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Stephen Wolfram(斯蒂芬.沃尔夫勒姆):好的。
18:16
that we kind of know to be true真正,
438
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那部分我们视作真理的物理学
18:18
things like the standard标准 model模型 of physics物理:
439
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就像标准物理模型
18:20
what I'm trying to do better reproduce复制 the standard标准 model模型 of physics物理
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我尝试做得更好的是再现标准物理模型
18:23
or it's simply只是 wrong错误.
441
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或者说明它是错的。
18:25
The things that people have tried试着 to do in the last 25 years年份 or so
442
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人们在近25年里已尝试的事情
18:27
with string theory理论 and so on
443
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有关弦论等等
18:29
have been an interesting有趣 exploration勘探
444
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都是非常有趣的探索
18:31
that has tried试着 to get back to the standard标准 model模型,
445
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这些探索已经尝试回到标准模型,
18:34
but hasn't有没有 quite相当 gotten得到 there.
446
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却还不能到那一步。
18:36
My guess猜测 is that some great simplifications简化 of what I'm doing
447
1101000
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我猜我的研究中的一些极端简化
18:39
may可能 actually其实 have considerable大量 resonance谐振
448
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可能和弦论中的某些研究
18:42
with what's been doneDONE in string theory理论,
449
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有相当的相似度
18:44
but that's a complicated复杂 math数学 thing
450
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不过,那是复杂的数学
18:47
that I don't yet然而 know how it's going to work out.
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我还不知道有些是怎么回事情。
18:50
CACA: Benoit伯努瓦 Mandelbrot曼德尔布罗 is in the audience听众.
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克里斯 安德森: Benoit Mandlebrot也在观众席中。
18:52
He also has shown显示 how complexity复杂
453
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他也展示了如何复杂
18:54
can arise出现 out of a simple简单 start开始.
454
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可以从简单的初始状态演化过来。
18:56
Does your work relate涉及 to his?
455
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这和你的研究相关吗?
18:58
SWSW: I think so.
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史蒂芬:我觉得有。
19:00
I view视图 Benoit伯努瓦 Mandelbrot's曼德尔布罗的 work
457
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我看过Benoit Mandlebrot的研究,
19:02
as one of the founding创建 contributions捐款
458
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觉得像这个领域的
19:05
to this kind of area.
459
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基础贡献
19:08
Benoit伯努瓦 has been particularly尤其 interested有兴趣
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Benoit致力于
19:10
in nested嵌套 patterns模式, in fractals分形 and so on,
461
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复杂图样,分型等等的研究,
19:12
where the structure结构体 is something
462
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在那些方面,结构就像
19:14
that's kind of tree-like树状,
463
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树型之类的东西,
19:16
and where there's sort分类 of a big branch that makes品牌 little branches分支机构
464
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有大分支,能产生小分支
19:18
and even smaller branches分支机构 and so on.
465
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和更小分支
19:21
That's one of the ways方法
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那也是一种方法
19:23
that you get towards true真正 complexity复杂.
467
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来到达真正的复杂。
19:26
I think things like the Rule规则 30 cellular细胞的 automaton自动机
468
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我觉得像30号规则的单元自动机
19:29
get us to a different不同 level水平.
469
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将我们带到了不同的水平上。
19:31
In fact事实, in a very precise精确 way, they get us to a different不同 level水平
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事实上,更精确地说,它能将我们带到不同的水平
19:34
because they seem似乎 to be things that are
471
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因为他们看似能够
19:37
capable of complexity复杂
472
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达到复杂状态
19:40
that's sort分类 of as great as complexity复杂 can ever get ...
473
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这种复杂是前所未有的...
19:44
I could go on about this at great length长度, but I won't惯于. (Laughter笑声) (Applause掌声)
474
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我可以持续不断地讲下去,但是我不打算去做。
19:47
CACA: Stephen斯蒂芬 Wolfram, thank you.
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克里斯:史蒂芬,谢谢你。
19:49
(Applause掌声)
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(鼓掌)
Translated by Hao Li
Reviewed by Vivian Lee

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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