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