TED2010
Stephen Wolfram: Computing a theory of all knowledge
斯蒂芬.沃尔夫勒姆：计算万物的理论
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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。
Stephen Wolfram - Scientist, inventor
Stephen Wolfram is the creator of Mathematica and Wolfram|Alpha, the author of A New Kind of Science, and the founder and CEO of Wolfram Research. Full bio
Stephen Wolfram is the creator of Mathematica and Wolfram|Alpha, the author of A New Kind of Science, and the founder and CEO of Wolfram Research. Full bio
Double-click the English transcript below to play the video.
00:16
So I want to talk today about an idea. It's a big idea.
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接下来，我今天想谈的是一个宏观理念。
00:19
Actually, I think it'll eventually
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其实，我认为这个构想最终
00:21
be seen as probably the single biggest idea
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会被视为上个世纪出现过的
00:23
that's emerged in the past century.
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最伟大的理念
00:25
It's the idea of computation.
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那就是计算的理念
00:27
Now, of course, that idea 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 technology we have today 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 a lot more to computation 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, very fundamental idea,
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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 effects we've only just begun 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 the past 30 years of my life
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过去30年里，我致力于
00:44
working on three large projects
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研究3个大型的项目
00:46
that really try to take the idea of computation seriously.
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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 off at a young age as a physicist
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刚开始时我只是个年轻的物理学家
00:53
using computers as tools.
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运用电脑作为工具
00:55
Then, I started drilling down,
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然后，我开始深入
00:57
thinking about the computations I might want to do,
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思考我可能想做的计算
00:59
trying to figure out what primitives they could be built up from
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尝试找出可以加以演变的主数据类型
01:02
and how they could be automated as much as possible.
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以及它们尽可能自动运行的方式
01:05
Eventually, I created a whole structure
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最终，我创立了整个架构
01:07
based on symbolic programming and so on
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基于符号编程等等
01:09
that let me build Mathematica.
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然后创造出了Mathematica
01:11
And for the past 23 years, at an increasing rate,
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过去23年间，以逐年增长的态势
01:13
we've been pouring more and more ideas
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我们已经为Mathematica注入了
01:15
and capabilities and so on into Mathematica,
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越来越多的概念和性能
01:17
and I'm happy to say that that's led to many 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,
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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, actually,
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当然，我必须承认，事实上
01:26
that I also had a very selfish reason for building Mathematica:
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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 got to use his telescope
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就像伽利略在400年前
01:33
400 years ago.
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想要用望远镜一样
01:35
But I wanted to look not at the astronomical universe,
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但我想了解的不是天文宇宙
01:38
but at the computational universe.
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而是可计算空间
01:41
So we normally think of programs as being
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通常我们觉得程序是
01:43
complicated things that we build
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复杂的东西
01:45
for very specific purposes.
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我们编程有很多特定的目的
01:47
But what about the space of all possible programs?
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然而所有程序的空间又有多少呢？
01:50
Here's a representation of a really simple program.
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这里有个非常简单的程序
01:53
So, if we run this program,
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所以呢，如果我们运行这个程序
01:55
this is what we get.
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这就是我们得到的结果
01:57
Very simple.
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很简单
01:59
So let's try changing the rule
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接下来，我们稍微修改一下
02:01
for this program a little bit.
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这个程序的规则
02:03
Now we get another result,
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我们便得到了另一个结果
02:05
still very simple.
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仍旧非常简单
02:07
Try changing it again.
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再试着改一下
02:10
You get something a little bit more complicated.
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你就看到稍微复杂一点的东西
02:12
But if we keep running 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 the pattern we get is very intricate,
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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 structure.
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但它具有有规律的结构
02:20
So the question is: Can anything else happen?
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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.
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好，我们可以做个小实验
02:25
Let's just do a little mathematical experiment, try and find out.
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来做个小的数学实验，试着找出规律
02:29
Let's just run all possible programs
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运行我们所关注的特定总类的
02:32
of the particular type that we're looking at.
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所有可能的程序
02:34
They're called cellular automata.
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他们被称为单元自动机
02:36
You can see a lot of diversity in the behavior here.
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你能看到这里有各种各样的图案模式
02:38
Most of them do very simple things,
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大多数都很简单
02:40
but if you look along all these different pictures,
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但是，如果你注意所有不同的图片
02:42
at rule number 30,
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在30号规则上
02:44
you start to see something interesting going on.
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你开始看见一些有趣的东西出现
02:46
So let's take a closer look
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所以我们仔细看一下
02:48
at rule number 30 here.
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在30号规则这里
02:50
So here it is.
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就在这里
02:52
We're just following this very simple rule at the bottom here,
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我们只是按照底部非常简单的规律
02:55
but we're getting all this amazing stuff.
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然而我们得到了惊人的结果
02:57
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
02:59
and I must 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
03:01
it came as a huge shock to my intuition.
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它让我直觉为之震惊
03:04
And, in fact, to understand it,
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实际上，为了理解它
03:06
I eventually had to create
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我们最终不得不建立
03:08
a whole new kind of science.
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一套全新的科学
03:11
(Laughter)
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（笑声）
03:13
This science is different, more general,
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这套科学是与众不同的，并且更加广义的
03:16
than the mathematics-based science that we've had
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比起已经存在的基于数学的其他科学来说
03:18
for the past 300 or so years.
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在过去300年甚至更久的时间内
03:21
You know, it's always seemed like a big mystery:
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你知道的，它总是看似神秘
03:23
how nature, seemingly so effortlessly,
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自然毫不费力地
03:26
manages to produce so much
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制造出如此多的东西
03:28
that seems to us so complex.
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让我们觉得如此复杂
03:31
Well, I think we've found its secret:
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于是，我觉得我们已经发现了其中的奥秘
03:34
It's just sampling what's out there in the computational universe
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这只是我们能探索的计算空间的一个样本
03:37
and quite often getting things like Rule 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
03:44
And knowing that starts to explain
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在知道这件事后，我们可以开始解释
03:46
a lot of long-standing mysteries in science.
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很多科学中长期以来的谜团
03:49
It also brings up new issues, though,
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不过，它也带来新的问题
03:51
like computational irreducibility.
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就像计算的不可化归性
03:54
I mean, we're used to having science let us predict 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
03:57
but something like this
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但是像这样的事情
03:59
is fundamentally irreducible.
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是根本不可简化的
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The only way to find its outcome
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发现它结果的唯一方法
04:03
is, effectively, just to watch it evolve.
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实际上就是看着它演化
04:06
It's connected to, what I call,
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与之相关的便是我所谓的
04:08
the principle of computational equivalence,
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计算等价性原则
04:10
which tells us that even incredibly simple systems
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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 as sophisticated 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 or biological evolution
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不需要多先进的技术或是生物进化过程
04:19
to be able to do arbitrary computation;
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就能使得它能够做任意的计算
04:21
just something that happens, naturally,
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这就是自然发生的事情
04:23
all over the place.
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随处可见
04:25
Things with rules as simple 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
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而且，这件事有深刻的意义
04:31
about the limits of science,
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涉及科学的极限
04:33
about predictability and controllability
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概率论和控制论等
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of things like biological processes or economies,
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在生物进程或者经济方面发挥作用
04:38
about intelligence in the universe,
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还有关于宇宙中的智能
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about questions like free will
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关于自由意志
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and about creating technology.
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以及创新技术的问题
04:45
You know, in working on this science for many years,
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从事这些科学工作很多年后
04:47
I kept wondering,
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我开始思考
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"What will be its first killer app?"
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第一个令人震惊的应用程序是什么？
04:51
Well, ever since I was a kid,
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恩，甚至我还是孩子时
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I'd been thinking about systematizing knowledge
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我就想过关于知识系统化的问题
04:55
and somehow making it computable.
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以及怎么让它变得可计算
04:57
People like Leibniz had wondered about that too
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莱布尼兹之辈也已经想过这个问题
04:59
300 years earlier.
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在300年前
05:01
But I'd always assumed that to make progress,
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但是我总是假设，为了进步，
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I'd essentially have to replicate a whole brain.
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我不得不克隆出整个大脑
05:06
Well, then I got to thinking:
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而现在，我想的是
05:08
This scientific paradigm of mine suggests something different --
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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 computation capabilities in Mathematica,
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使Mathematica具备了超强的计算能力
05:16
and I'm a CEO with some worldly resources
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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 crazy, projects --
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来做大型的，看似疯狂的项目。
05:22
So I decided 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 knowledge that's out there in the world
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在这世界上，有多少系统化的知识
05:27
we could make computable.
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是我们能够计算的
05:29
So, it's been a big, very complex project,
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所以，这是个大型、复杂的项目，
05:31
which 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 to say it's actually 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
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就在去年
05:39
to release the first website version
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我们发布了第一个网络版本的
05:41
of Wolfram Alpha.
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Wolfram Alpha
05:43
Its purpose is to be a serious knowledge engine
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目的是提供一个专业的知识搜索引擎
05:46
that computes answers to questions.
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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 off with something really easy.
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让我们先试试简单的东西
05:53
Hope for the best.
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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)
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（笑声）
06:02
Let's try something a little bit harder.
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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 mathy thing,
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我们做点数学
06:10
and with luck it'll work out the answer
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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 things
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并且试着告诉我们一些
06:15
things about related math.
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关于数学的有趣的事
06:17
We could ask it something about the real world.
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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 GDP of Spain?
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西班牙的国民生产总值是多少？
06:25
And it should be able to tell us that.
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它应该能告诉我们
06:27
Now we could compute something related 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 GDP of Spain
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比如西班牙的国民生产总值
06:31
divided by, I don't know,
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除以， 让我想想
06:33
the -- hmmm ...
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嗯
06:35
let's say the revenue of Microsoft.
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比如微软公司的收入
06:37
(Laughter)
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（笑声）
06:39
The idea is that we can just type 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 we think of it.
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不论是什么奇怪的问题
06:44
So let's try asking a question,
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所以，我们提个问题
06:46
like a health related question.
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比如有关健康的问题
06:48
So let's say we have a lab finding that ...
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比如，跟据实验室数据
06:51
you know, we have an LDL level 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
06:53
for a male aged 50.
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这是针对50多岁的男性
06:56
So let's type that in, and now Wolfram Alpha
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我们输入这个，然后Wolfram Alpha
06:58
will go and use available public health data
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就会使用存在的公共健康数据库
07:00
and try and figure out
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来试着分析出
07:02
what part of the population that corresponds to and so on.
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这组数据对应哪部分人群等等
07:05
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
07:08
the International Space Station.
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国际空间站的问题
07:10
And what's happening here is that
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结果就是
07:12
Wolfram Alpha is not just looking up something;
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Wolfram Alpha不仅在查找信息
07:14
it's computing, in real time,
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它是在实时计算
07:17
where the International Space Station is right now at this moment,
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国际空间站现在此刻的位置
07:20
how fast it's going, and so on.
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它运行的速度等等
07:24
So Wolfram Alpha knows 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 good coverage of everything you might find
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它几乎可以很好的涵盖了你能在
07:31
in a standard reference library.
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一个标准图书馆中找到的知识
07:34
But the goal is to go much further
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不过，我们的目标远不止这些
07:36
and, very broadly, to democratize
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概括地说
07:39
all of this knowledge,
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是要使所有的知识民主化
07:42
and to try and be an authoritative
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并且试着提供
07:44
source in all areas.
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所有领域中的权威资料
07:46
To be able to compute answers to specific questions that people have,
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使它能够计算人们特定问题的答案
07:49
not by searching what other people
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不是靠搜索其他人
07:51
may have written down before,
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之前可能写下的资料
07:53
but by using built in knowledge
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而是使用内建知识
07:55
to compute fresh new answers to specific questions.
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来对特定问题计算新的答案
07:58
Now, of course, Wolfram Alpha
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现在，当然，Wolfram Alpha
08:00
is a monumentally huge, long-term project
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是一个非常大型、长远的项目
08:02
with lots and lots of challenges.
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面临着众多挑战
08:04
For a start, one has to curate a zillion
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开始的时候，我们要收集数以万计的
08:07
different sources of facts and data,
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不同的事实来源和数据
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and we built quite a pipeline of Mathematica automation
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而且，我们建立了Mathematica自动化流水线
08:13
and human domain experts for doing this.
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还有知识领域专家来做这件事
08:16
But that's just the beginning.
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不过，这只是开始
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Given raw facts or data
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对于运用一些没有处理的事实和数据
08:20
to actually answer questions,
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来解答实际问题
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one has to compute:
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一方面要计算
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one has to implement all those methods and models
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另一方面要执行所有的方法、模型
08:26
and algorithms and so on
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以及算法等等
08:28
that science and other areas have built up over the centuries.
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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 from Mathematica,
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甚至从Mathematica开始
08:34
this is still a huge 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 lines
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至今为止，有8百万行
08:38
of Mathematica code in Wolfram Alpha
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Mathematica的代码写在Wolfram Alpha里
08:40
built by experts from many, many different fields.
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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 idea of Wolfram Alpha
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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
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是你可以问它问题
08:48
using ordinary human language,
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使用普通人类语言
08:51
which means that we've got to be able to take
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这意味着我们必须能够接受
08:53
all those strange utterances that people type into the input field
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人们输入所有的奇怪的文字
08:56
and understand them.
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并理解它们
08:58
And I must say that I thought that step
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我必须说我曾觉得做到那一步
09:00
might just be plain impossible.
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相当不可能
09:04
Two big things happened:
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后来有了两大重要进步
09:06
First, a bunch of new ideas about linguistics
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首先是语言学上的很多新想法
09:09
that came from studying the computational universe;
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来自于对计算空间的研究
09:12
and second, the realization that having actual computable knowledge
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其次，可计算知识的实现
09:15
completely changes how one can
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完全地改变了如何一个人能够
09:17
set about understanding language.
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开始理解语言
09:20
And, of course, now
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当然，现在
09:22
with Wolfram Alpha actually out in the wild,
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在浩瀚的网络中有了Wolfram Alpha
09:24
we can learn from its actual usage.
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我们就能学习它的使用方法
09:26
And, in fact, there's been
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实际上，一直都有
09:28
an interesting coevolution that's been going on
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一个有趣的共同进化
09:30
between Wolfram Alpha
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发生在Wolfram Alpha
09:32
and its human users,
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和用户之间
09:34
and it's really encouraging.
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并且，这相当鼓舞人心
09:36
Right now, if we look at web queries,
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现在，对于任意网络搜索
09:38
more than 80 percent of them get handled successfully 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 app,
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如果你看看类似iPhone应用程序的东西
09:43
the fraction is considerably larger.
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那被成功搜索部分就相当大了
09:45
So, I'm pretty pleased with it all.
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所以我对此很满意
09:47
But, in many ways,
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但是，从很多角度看
09:49
we're still at the very beginning with Wolfram Alpha.
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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 up very nicely
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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 more confident.
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我们也变得更有信心
09:56
You can expect to see Wolfram Alpha technology
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你能期待看到Wolfram Alpha技术
09:58
showing up in more and more places,
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在越来越多的地方使用
10:00
working both with this kind of public data, like on the website,
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既能使用公共数据，比如网站
10:03
and with private knowledge
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又能使用私人数据
10:05
for people and companies 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 that Wolfram Alpha actually gives one
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我觉得Wolfram Alpha其实是一个
10:11
a whole new kind of computing
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全新的计算方法
10:13
that one can call knowledge-based computing,
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我们可以称之基于知识的计算
10:15
in which one's starting not just from raw computation,
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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 amount of built-in knowledge.
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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
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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 of delivering computational things,
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传递可计算事物的理论
10:26
whether it's on the web or elsewhere.
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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 interesting situation 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,
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一方面，我们拥有Mathematica这个软件
10:33
with its sort of precise, formal language
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它有精确性，正规性
10:36
and a huge network
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以及大规模
10:38
of carefully designed capabilities
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设计仔细的功能网络
10:40
able to get a lot done in just a few lines.
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用几行代码就能做很多事情
10:43
Let me show you a couple of examples here.
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我来展示几个例子
10:47
So here's a trivial piece of Mathematica programming.
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这是Mathematica编程中很小的一段代码
10:51
Here's something where we're sort of
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这里是我们整合
10:53
integrating a bunch of different capabilities here.
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大量不同的功能
10:56
Here we'll just create, in this line,
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这行，我们就能建立
10:59
a little user interface that allows us to
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一个简单的用户界面
11:02
do something fun 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 program
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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 of algorithmic things
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这些程序在运行算法之类的程序
11:10
and creating user interface 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 stuff.
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不过，这是非常精准的东西
11:15
It's a precise specification with a precise formal language
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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 Mathematica 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 Alpha,
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另一方面，我们拥有Wolfram Alpha
11:24
with all the messiness of the world
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包含了世界上所有杂乱无章的东西
11:26
and human language and so on built 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 when you put these things together?
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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 rather wonderful.
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我觉得真是非常棒
11:33
With Wolfram Alpha inside Mathematica,
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Mathematica里有Wolfram Alpha，
11:35
you can, for example, make precise programs
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你就能编写精准的程序
11:37
that call on real world data.
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来接触真实世界的数据
11:39
Here's a real simple example.
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这里有个很简单的例子
11:44
You can also just sort of give vague input
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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 Alpha
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试着让Wolfram Alpha
11:49
figure 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 I think the most exciting 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
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它给了我们一个机会
11:58
to democratize programming.
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来全民编程
12:01
I mean, anyone will be able to say what they want in plain language.
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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 is that Wolfram Alpha will be able to figure out
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关键在于，Wolfram Alpha能分析出
12:07
what precise 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 them examples 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 up bigger and bigger, precise programs.
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由此建立越来越多的精准程序
12:17
So, sometimes, Wolfram Alpha
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所以，有时候，Wolfram Alpha
12:19
will be able to do the whole thing immediately
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能够立即处理整个问题
12:21
and just give back a whole big program that you can then compute 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 a big website
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这里有个大网站
12:26
where we've been collecting lots of educational
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这里，我们收集了很多关于教育等
12:29
and other demonstrations about lots of kinds of things.
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各种事物的样例
12:32
I'll show 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 documents.
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这只是可计算文档的其中一个样例
12:39
This is probably a fairly small
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它是相当小的
12:41
piece of Mathematica code
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一段Mathematica代码
12:43
that's able to be run here.
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能在这里运行
12:47
Okay. Let's zoom out again.
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我们再缩小一下
12:50
So, given our new kind of science,
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所以，有了这个新版科学
12:52
is there a general way to use it to make technology?
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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 materials,
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使用物理材料
12:57
we're used to going around the world
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我们过去常常遍步世界
12:59
and discovering that particular materials
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并发现特定材料
13:01
are useful for particular
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用于特定的
13:03
technological purposes.
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技术目的等等。
13:05
Well, it turns out we can do very much the same 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 universe.
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在这个可计算的世界中。
13:09
There's an inexhaustible supply of programs out there.
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有无穷无尽的程序资源在那儿。
13:12
The challenge is to see how to
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面临的挑战是如何
13:14
harness them for human purposes.
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让它们供人类使用
13:16
Something like Rule 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 generator.
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结果可以是很好的随机生成器。
13:20
Other simple programs are good models
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其他简单的程序是很好的模型
13:22
for processes in the natural or social world.
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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 Alpha and Mathematica
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再比如，Wolfram Alpha和Mathematica
13:27
are actually now full of algorithms
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确实包含很多算法
13:29
that we discovered by searching the computational universe.
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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 surprisingly popular
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这个已经变成相当的流行
13:39
among composers
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在作曲家间
13:41
finding musical forms by searching the computational universe.
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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 universe
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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 creativity.
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来获得大量的个性化创造。
13:50
I'm hoping we can, for example,
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我希望我们能够
13:52
use that even to get Wolfram Alpha
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使用Wolfram Alpha
13:54
to routinely do invention and discovery 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 of wonderful stuff
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并且来找出所有令人惊讶的事情
13:59
that no engineer
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这些事情没有一个工程师
14:01
and no process of incremental evolution 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 to kind of an ultimate question:
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这些最终导向一个终极问题
14:08
Could it be that someplace out there in the computational universe
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有没有可能使这个计算空间
14:11
we might find our physical universe?
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与我们的物理世界相融合？
14:14
Perhaps there's even some quite simple rule,
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也许存在简单的规则
14:16
some simple program for our universe.
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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 of physics would have us believe
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物理的历史让我们相信
14:21
that the rule for the universe must be pretty complicated.
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宇宙的内部规则一定是很复杂的
14:24
But in the computational universe,
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但是在计算空间中
14:26
we've now seen how rules that are incredibly simple
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我们已经看到那些规则惊人的简单
14:29
can produce incredibly rich and complex behavior.
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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 universe?
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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 for the universe are simple,
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如果这个宇宙的规则很简单
14:38
it's kind of inevitable that they have to be
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不可避免的，他们一定是
14:40
very abstract and very low level;
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十分抽象以及初级
14:42
operating, for example, far below
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远远运行于
14:44
the level of space or time,
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时间、空间之下
14:46
which makes it hard to represent 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 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 as being
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我们能把这个宇宙想成
14:52
like some kind of network,
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某种网络
14:54
which, when it gets big enough,
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当它变得足够大时
14:56
behaves like continuous space
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它表现得像一个连续空间
14:58
in much the same way as having lots of molecules
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某种程度上就像很多分子
15:00
can behave like a continuous fluid.
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表现得像流体一样。
15:02
Well, then the universe has to evolve by applying
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之后，宇宙进化就要依靠
15:05
little rules that progressively update this network.
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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 rule, 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 to a candidate universe.
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对应一个候选空间
15:12
Actually, I haven't shown 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 of the candidate universes
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不过，这里有几个候选空间
15:19
that I've looked at.
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我正在研究的
15:21
Some of these are hopeless universes,
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一些是没希望的空间
15:23
completely sterile,
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完全不能演化，
15:25
with other kinds of pathologies like no notion of space,
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包括很多缺点，例如没有空间的观念
15:27
no notion of time, no matter,
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没有时间的概念，没有物质
15:30
other problems like that.
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或者类似的其他问题
15:32
But the exciting thing that I've found in the last few years
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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 don't have to go very far
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是你其实不必深入
15:37
in the computational universe
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在计算空间中
15:39
before you start finding candidate universes
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你就能发现与我们的物理空间
15:41
that aren't obviously not our universe.
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明显不同的候选空间
15:44
Here's the problem:
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问题在这里：
15:46
Any serious candidate for our universe
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任何有可能的候选空间
15:49
is inevitably full of computational irreducibility.
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不可避免地充满了计算不可化归性，
15:52
Which means that it is irreducibly 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,
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是极其困难的
15:57
and whether it matches our physical universe.
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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 years ago, I was pretty excited to discover
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几年前，我非常兴奋地发现
16:04
that there are candidate universes with incredibly simple rules
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有些候选空间具有极其简单的规则
16:07
that successfully reproduce special relativity,
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却能成功再现狭义相对论
16:09
and even general relativity and gravitation,
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和广义相对论以及重力
16:12
and at least give hints of quantum mechanics.
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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 of physics?
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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 of
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但是我觉得现在
16:21
almost embarrassing not to at least try.
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不去尝试的话真的是令人羞愧的。
16:23
Not an easy project.
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虽然这不是件简单的事。
16:25
One's got to build a lot of technology.
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一方面要发展技术
16:27
One's got to build a structure that's probably
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一方面要建立架构
16:29
at least as deep as existing physics.
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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 way to organize the whole 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 a team, open it up, offer prizes 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,
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但是，我今天要告诉你
16:39
that I'm committed to seeing this project done,
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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 hold in our hands
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我们是否最终可以掌握
16:46
the rule for our universe
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我们宇宙的规则
16:48
and know where our universe lies
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并且知道我们宇宙在
16:50
in the space of all possible universes ...
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所有可能的宇宙空间的位置
16:52
and be able to type into Wolfram Alpha, "the theory of the universe,"
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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)
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（笑声）
17:00
So I've been working on the idea of computation
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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,
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超过30年了研究
17:04
building tools and methods and turning intellectual ideas
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打造工具，创立方法，将专业知识
17:07
into millions of lines of code
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编写成数百万行的代码
17:09
and grist for server farms and so on.
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在服务器中收获结果等等。
17:11
With every passing year,
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每过去一年
17:13
I realize how much more powerful
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我都意识到
17:15
the idea of computation really is.
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计算的想法是多么的强大。
17:17
It's taken us a long way already,
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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 of science
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从科学的根基
17:23
to the limits of technology
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到技术的极限
17:25
to the very definition of the human condition,
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再到人类条件的定义，
17:27
I think computation is destined to be
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我觉得，计算注定
17:29
the defining idea of our future.
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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)
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（鼓掌）
17:47
Chris Anderson: That was astonishing.
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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)
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（鼓掌）
17:57
So, that was, fair to say, an astonishing talk.
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说实在的，那真的是很惊人的演讲。
18:01
Are you able to say in a sentence or two
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您能用一两句话概括
18:04
how this type of thinking
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这种思考方式如何
18:07
could integrate at some point
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能在某些点上整合
18:09
to things like string theory or the kind of things that people think of
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一些如弦论或者
18:11
as the fundamental explanations of the universe?
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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 Wolfram: Well, the parts of physics
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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,
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那部分我们视作真理的物理学
18:18
things like the standard model of physics:
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就像标准物理模型
18:20
what I'm trying to do better reproduce the standard model of physics
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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 wrong.
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或者说明它是错的。
18:25
The things that people have tried to do in the last 25 years or so
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人们在近25年里已尝试的事情
18:27
with string theory and so on
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有关弦论等等
18:29
have been an interesting exploration
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都是非常有趣的探索
18:31
that has tried to get back to the standard model,
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这些探索已经尝试回到标准模型，
18:34
but hasn't quite gotten there.
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却还不能到那一步。
18:36
My guess is that some great simplifications of what I'm doing
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我猜我的研究中的一些极端简化
18:39
may actually have considerable resonance
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可能和弦论中的某些研究
18:42
with what's been done in string theory,
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有相当的相似度
18:44
but that's a complicated math thing
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不过，那是复杂的数学
18:47
that I don't yet 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
CA: Benoit Mandelbrot is in the audience.
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克里斯 安德森： Benoit Mandlebrot也在观众席中。
18:52
He also has shown how complexity
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他也展示了如何复杂
18:54
can arise out of a simple start.
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可以从简单的初始状态演化过来。
18:56
Does your work relate to his?
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这和你的研究相关吗？
18:58
SW: I think so.
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史蒂芬：我觉得有。
19:00
I view Benoit Mandelbrot's work
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我看过Benoit Mandlebrot的研究，
19:02
as one of the founding contributions
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觉得像这个领域的
19:05
to this kind of area.
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基础贡献
19:08
Benoit has been particularly interested
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Benoit致力于
19:10
in nested patterns, in fractals and so on,
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复杂图样，分型等等的研究，
19:12
where the structure is something
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在那些方面，结构就像
19:14
that's kind of tree-like,
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树型之类的东西，
19:16
and where there's sort of a big branch that makes little branches
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有大分支，能产生小分支
19:18
and even smaller branches and so on.
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和更小分支
19:21
That's one of the ways
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那也是一种方法
19:23
that you get towards true complexity.
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来到达真正的复杂。
19:26
I think things like the Rule 30 cellular automaton
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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 level.
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将我们带到了不同的水平上。
19:31
In fact, in a very precise way, they get us to a different level
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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 to be things that are
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因为他们看似能够
19:37
capable of complexity
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达到复杂状态
19:40
that's sort of as great as complexity can ever get ...
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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, but I won't. (Laughter) (Applause)
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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
CA: Stephen Wolfram, thank you.
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克里斯：史蒂芬，谢谢你。
19:49
(Applause)
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（鼓掌）
ABOUT THE SPEAKER
Stephen Wolfram - Scientist, inventorStephen Wolfram is the creator of Mathematica and Wolfram|Alpha, the author of A New Kind of Science, and the founder and CEO of Wolfram Research.
Why you should listen
Stephen Wolfram published his first scientific paper at the age of 15, and received his PhD in theoretical physics from Caltech by the age of 20. Having started to use computers in 1973, Wolfram rapidly became a leader in the emerging field of scientific computing.
In 1981 Wolfram became the youngest recipient of a MacArthur Prize Fellowship. He then set out on an ambitious new direction in science aimed at understanding the origins of complexity in nature. Wolfram's first key idea was to use computer experiments to study the behavior of simple computer programs known as cellular automata. This allowed him to make a series of startling discoveries about the origins of complexity.
Wolfram founded the first research center and the first journal in the field, Complex Systems, and began the development of Mathematica. Wolfram Research soon became a world leader in the software industry -- widely recognized for excellence in both technology and business.
Following the release of Mathematica Version 2 in 1991, Wolfram began to divide his time between Mathematica development and scientific research. Building on his work from the mid-1980s, and now with Mathematica as a tool, Wolfram made a rapid succession of major new discoveries, which he described in his book, A New Kind of Science.
Building on Mathematica, A New Kind of Science, and the success of Wolfram Research, Wolfram recently launched Wolfram|Alpha -- an ambitious, long-term project to make as much of the world's knowledge as possible computable, and accessible to everyone.
Stephen Wolfram | Speaker | TED.com