How to Think Computationally About AI, the Universe and Everything | Stephen Wolfram | TED

翻译人员: Yip Yan Yeung 校对人员: suya f.

Human language, mathematics, logic.

人类语言、数学、逻辑。

These are all ways to formalize the world.

这些都是让世界形式化的方式。

And in our century,

在我们这个世纪，

there's a new and yet more powerful one: computation.

有一个新兴却更强大的方式：计算。

For nearly 50 years,

近 50 年来，

I've had the great privilege

我有幸建造了一座

of building up an ever-taller tower of science and technology

以计算为基础的科技高塔。

that's based on that idea of computation.

And so today, I want to tell you a little bit about what that's led to.

今天，我想简单介绍一下 它带来了什么结果。

There's a lot to talk about, so I'm going to go quickly.

我有很多话要说， 所以我就说快点。

And sometimes I'm going to summarize in a sentence

有时我会用一句话 总结我写了一整本书的内容。

what I've written a whole book about.

But you know,

但我上一次来到 TED 演讲 是在 13 年前，

I last gave a TED talk 13 years ago,

in February 2010,

2010 年 2 月，

soon after WolframAlpha launched,

也就是 WolframAlpha 发布后不久，

and I ended that talk with a question.

我以一个问题结束了那场演讲。

Question was,

问题是：

is computation ultimately what's underneath everything

计算真的是我们宇宙万物的本质吗？

in our universe?

I gave myself a decade to find out.

我给了自己十年的时间来找出答案。

And actually, it could have needed a century.

其实这可能需要一个世纪。

But in April 2020, just after the decade mark,

但是在 2020 年 4 月， 就在十年之后，

we were thrilled to be able to announce

我们很高兴宣布

what seems to be the ultimate machine code of the universe.

宇宙的终极机器代码。

And yes, it's computational.

没错，它是计算性的。

So computation isn't just a possible formalization,

计算不仅是一种可能的形式化，

it's the ultimate one for our universe.

更是我们宇宙的终极形式。

It all starts from the idea that space, like matter, is made of discrete elements,

这一切都源于这个理念：空间， 和物质一样，是由离散元素组成的，

and from that structure of space and everything in it,

空间的结构和空间中的所有事物，

it's defined just by a network of relations

仅仅是由这些元素之间的 关系网络来定义的，

between these elements that we might call atoms of space.

我们称之为空间原子。

So it's all very elegant, but deeply abstract.

一切都非常精妙，但非常抽象。

But here's kind of a humanized representation,

但还有一种较为人性化的表现形式，

a version of the very beginning of the universe.

宇宙起源的另一个版本。

And what we're seeing here is the emergence of space

我们将要看到的是 宇宙和万物的起源，

and everything in it

by the successive application of very simple computational rules.

源于不断应用 非常简单的计算规则。

And remember, these dots are not atoms in any existing space.

请记住，这些点 在任何现有空间中都不是原子。

They're atoms of space that get put together to make space.

它们是空间原子， 聚集在一起形成了空间。

And yes, if we kept going long enough,

是的，如果我们继续保持 足够长的时间，

we could build our whole universe this way.

我们就可以以这种方式 创造整个宇宙。

So eons later,

很久以后，

here's a chunk of space with two little black holes

出现了一大块空间， 上面有两个小黑洞，

that, if we wait a little while, will eventually merge,

如果我们稍等片刻， 它们最终会合并，

generating little ripples of gravitational radiation.

产生很小的引力辐射波。

And remember, all of this is built from pure computation.

请记住，一切都是 通过纯计算构建的。

But like fluid mechanics emerging from molecules,

但是，就像分子中涌现的 流体力学一样，

what emerges here is space-time and Einstein's equations for gravity,

这里出现的是时空 和爱因斯坦的重力方程，

though there are deviations that we just might be able to detect,

虽然有些我们可能能够检测到的偏差，

like that the dimensionality of space won't always be precisely three.

就像空间的维度 并不总是精确的三个。

And there's something else.

还有别的。

Our computational rules can inevitably be applied in many ways,

我们的计算规则 一定可以有多种用途，

each defining a different kind of thread of time,

每种方式都定义了不同的时间线，

a different path of history that can branch and merge.

每一条都是可以分叉、 合并的不同历史走向。

But as observers embedded in this universe,

但作为身处这个宇宙中的观察者，

we're branching and merging, too.

我们也在分叉、合并。

And it turns out that quantum mechanics emerges as the story

量子力学就源自

of how branching minds perceive a branching universe.

分支思想理解分支宇宙的故事。

So the little pink lines you might be able to see here

你看这里的粉色细线，

show the structure of what we call branchial space,

代表的就是我们称之为 “鳃空间”的结构，

the space of quantum branches.

即量子分支的空间。

And one of the stunningly beautiful things,

美妙的一点就是，

at least for physicists like me,

至少对于像我这样的物理学家来说，

is that the same phenomenon that in physical space gives us gravity,

物理空间赋予我们引力，

in branchial space gives us quantum mechanics.

而分支空间赋予我们量子力学。

So in the history of science so far,

在迄今为止的科学史上， 我认为我们可以确定四个宽泛的范例，

I think we can identify sort of four broad paradigms

for making models of the world that can be distinguished

制作世界模型， 以它们处理时间的方式区分。

kind of by how they deal with time.

So in antiquity and in plenty of areas of science, even today,

在古代和许多科学领域， 即使在今天，

it's all about kind of, what are things made of.

都在说事物是由什么构成的。

And time doesn't really enter.

但从来没有考虑过时间。

But in the 1600s came the idea of modeling things

但是在 1600 年代，人们产生了

with mathematical formulas in which time enters,

使用数学公式对事物进行建模的想法， 这些公式考虑到了时间，

but basically just as a coordinate value.

但基本上只是作为坐标值。

Then in the 1980s, and this is something in which I was deeply involved,

20 世纪 80 年代， 我深入参与了一个想法——

came the idea of making models

从简单的计算规则开始， 然后让它自己运行，

by starting with simple computational rules

and just letting them run.

由此建模。

So can one predict what will happen?

人们能预测会发生什么吗？

No.

不能。

There's what I call computational irreducibility,

这就是我所说的计算不可还原性，

in which, in effect, the passage of time corresponds to an irreducible computation

时间的流逝对应着 一种不可还原的计算，

that we have to run in order to work out how it will turn out.

我们必须运行这种计算 才能得出结果。

But now there's kind of something,

但现在出现了另一种情况，

something even more -- in our physics project,

甚至更…… 在我们的物理项目中，

there’s things that have become multi-computational,

出现了“多重计算”的东西，

with many threads of time

多条时间线只能由观察者编织在一起。

that can only be knitted together by an observer.

So it's kind of a new paradigm that actually seems to unlock things

这是一种新的范式， 不仅可以解密基础物理学里的东西，

not only in fundamental physics,

but also in the foundations of mathematics and computer science,

还能解密数学和计算机科学的基础，

and possibly in areas like biology and economics as well.

可能还有生物学和 经济学等领域里的东西。

So I talked about building up the universe

我谈到了通过反复应用 计算规则来建立宇宙。

by repeatedly applying a computational rule.

But how is that rule picked?

但是这条规则是如何选择的呢？

Well, actually it isn't,

其实并没有选择，

because all possible rules are used,

因为所有有可能的规则都被用过了，

and we're building up what I call the ruliad,

我们正在构建我所谓的 鲁利亚德（Ruliad），

the kind of deeply abstract but unique object

非常抽象但独特的物体，

that is the entangled limit of all possible computational processes.

所有可能的计算过程的纠缠极限。

Here's a tiny fragment of it shown in terms of Turing machines.

以下是用图灵机显示的一小部分。

So this ruliad is everything.

鲁利亚德就是一切。

And we as observers are necessarily part of it.

作为观察者， 我们必然是其中的一部分。

In the ruliad as a whole,

鲁利亚德作为一切的世界里，

in a sense, everything computationally possible can happen.

任何从计算上有可能的事 都可以发生。

But observers like us just sample specific slices of the ruliad.

但我们这样的观察者 只是鲁利亚德的特定样本。

And there are two crucial facts about us.

关于我们，有两个关键事实。

First, we're computationally bounded, our minds are limited,

首先，我们受计算限制，思维有限，

and second, we believe we're persistent in time,

其次，我们相信 我们在时间中是连贯的，

even though we're made of different atoms of space at every moment.

尽管我们每时每刻都由 不同的空间原子组成。

So then, here's the big result.

于是得出了这个最重要的结果。

What observers with those characteristics perceive in the ruliad

具备这些特征的观察者 在鲁利亚德中所感知的事物

necessarily follows certain laws.

必然遵循某些定律。

And those laws turn out to be precisely

而这些定律正是

the three key theories of 20th century physics:

20 世纪物理学的三个关键理论：

general relativity, quantum mechanics,

广义相对论、量子力学

and statistical mechanics in the second law.

和量子力学中的统计力学。

So it's because we're observers like us

正是因为我们是这样的观察者，

that we perceive the laws of physics we do.

我们才能如此理解物理定律。

We can think of sort of different minds

我们可以将不同的思想

as being at different places in rulial space.

视为鲁利亚德空间中的各个位置。

Human minds who think alike are nearby,

思维相近的人类思维就在附近，

animals further away,

动物在远处，

and further out, we get to kind of alien minds

更远的就是外星人思维， 无法转译。

where it's hard to make a translation.

So how can we get intuition for all of this?

我们该如何理解这一切呢？

Well, one thing we can do is use generative AI

我们可以做的一件事 就是使用生成式 AI

to take what amounts to an incredibly tiny slice of the ruliad

将极小的一个鲁利亚德切片

aligned with images we humans have produced.

与我们人类生成的图像对齐。

We can think of this as sort of a place in the ruliad

我们可以把它看作是 鲁利亚德里的一个空间

described by using the concept of a cat in a party hat.

由“戴着派对帽的猫咪” 这个说法描述。

So zooming out, we saw there

向外延伸， 我们可以看到

what we might call Cat Island.

我们称之为“猫岛”的地方。

Pretty soon we’re in a kind of an inter-concept space.

很快我们就进入了 一个跨概念的空间。

Occasionally things will look familiar,

有些东西可能看起来很眼熟，

but mostly, what we'll see is things we humans don't have words for.

但多数的情况是 我们会看到无法用语言描述的东西。

In physical space, we explore the universe

在物理空间中，我们 通过发射宇宙飞船探索宇宙。

by sending out spacecraft.

In rulial space, we explore more

在鲁利亚德空间中，

by expanding our concepts and our paradigms.

我们通过扩展概念 和范式来进一步探索。

We can kind of get a sense of what's out there

我们可以通过 对可能的规则进行抽样，

by sampling possible rules,

doing what I call ruliology.

我称之为“鲁利亚德学”来理解。

So even with incredibly simple rules,

即使是非常简单的规则，

there's incredible richness.

也有令人难以置信的丰富性。

But the issue is that most of it doesn't yet connect

但问题在于，其中大部分尚未

with things we humans understand or care about.

与我们人类理解 或关心的事物发生关联。

It's like when we look at the natural world

就像当我们看着自然世界时，

and only gradually realize that we can use features of it for technology.

才逐渐意识到我们可以 将自然世界的特征用于技术。

So even after everything our civilization has achieved,

即使我们的文明取得了所有成就，

we're just at the very, very beginning of exploring rulial space.

我们也只是在 鲁利亚德空间的冰山一角。

What about AIs?

AI 呢？

Well, just like we can do ruliology,

就像我们可以进行“鲁利亚德”学，

AIs can in principle go out and explore rulial space.

AI 理论上也可以去 探索鲁利亚德空间。

Left to their own devices, though,

虽然只凭它们自己的设备，

they'll mostly just be doing things

它们大多只会做

we humans don't connect with or care about.

我们人类不在意或关心的事情。

So the big achievements of AI in recent times

近来 AI 的重大成就

have been about making systems that are closely aligned with us humans.

在于创造出了 与我们人类水平相当的系统。

We train LLMs on billions of web pages so they can produce texts

我们用上亿网页训练了 大语言模型，让它们生成

that's typical of what we humans write.

我们人类通常会写的文本。

And yes, the fact that this works

没错，它确实有效的事实

is undoubtedly telling us some deep scientific things

无疑告诉了我们一些 关于语言语义语法的深刻科学知识，

about the semantic grammar of language

and generalizations of things like logic

还有一些逻辑之类事物的泛化，

that perhaps we should have known centuries ago.

这也许是几百年前我们就该知晓的东西。

You know, for much of human history,

在人类的大部分历史中，

we were kind of like the LLMs,

我们就和大语言模型差不多，

figuring things out by kind of matching patterns in our minds.

通过匹配脑海中的模式想明白事情。

But then came more systematic formalization and eventually computation.

但随后出现了更系统性的形式化， 最终出现了计算。

And with that, we got a whole other level of power to truly create new things

有了计算，我们就拥有了 更高维度的力量去创造新的事物，

and to, in effect, go wherever we want in the ruliad.

去向鲁利亚德里 我们想去的任何地方。

But the challenge is to do that in a way that connects with what we humans,

但面临的挑战是 如何以一种我们人类

and our AIs, understand.

和我们的 AI 可以理解的方式达成。

In fact, I've devoted a large part of my life

我其实把人生中的一大段时间

to kind of trying to build that bridge.

花在了打造那个联结上。

It's all been about creating a language for expressing ourselves computationally,

这一切都是为了创造一种 用计算方式表达我们自己的语言，

a language for computational thinking.

一种用于计算思维的语言。

The goal is to formalize what we know about the world in computational terms,

目标是用计算术语 形式化我们对世界的了解，

to have computational ways to represent cities and chemicals and movies

以计算的方式表示 城市、化学物质、电影、

and humor and formulas and our knowledge about them.

幽默和公式 以及我们对它们的认知。

It’s been a vast undertaking that spanned more than four decades of my life,

这是一项艰巨的任务， 跨越了我生命中的四十多年，

but it's something very unique and different.

但它非常独特、与众不同。

But I'm happy to report that in what has been Mathematica

但我很高兴与大家分享 ， 在曾经的 Mathematica，

and is now the Wolfram Language,

如今的 Wolfram 语言中，

I think we firmly succeeded in creating

我认为我们成功地创建了

a truly full-scale computational language.

一种真正的全方位计算语言。

In effect,

其实这里的每一个函数 都可以看作是用计算术语

every one of these functions here can be thought of as formalizing

and encapsulating, in computational terms,

形式化并概括我们文明的 智慧成就的某些方面。

some facet of the intellectual achievements of our civilization.

It's sort of the most concentrated form of intellectual expression that I know,

这是我所知道的 智力表达形式的最浓缩形态，

sort of finding the essence of everything and coherently expressing it

某种程度上就是找到了万物的本质 并在我们的计算语言的设计中

in the design of our computational language.

连贯地表达出来。

For me personally,

就我个人而言，

it's been an amazing journey, kind of, year after year,

这是一段奇妙的旅程，年复一年地

building the sort of tower of ideas and technology that's needed.

建造所需的思想和技术之塔。

And nowadays sharing that process with the world

如今 ，通过直播等方式 与世界分享这一过程。

in things like open live streams and so on.

A few centuries ago,

几个世纪前，

the development of mathematical notation,

数学符号的发展，

and what amounts to the language of mathematics,

相当于数学语言的发展，

gave a systematic way to express math and made possible algebra and calculus,

为数学的表达提供了一种系统的方法， 使代数和微积分成为可能，

and eventually all of modern mathematical science.

最终使所有现代数学科学成为可能。

And computational language now provides a similar path,

计算语言现在提供了类似的路径，

letting us ultimately create a computational X

让我们为所有可以想到的 X 领域 创造出可进行计算的 X。

for all imaginable fields X.

I mean, we've seen the growth of computer science, CS,

我们已经看到了 计算机科学（CS）的发展，

but computational language opens up something ultimately much bigger

但计算语言最终会开辟出更大、

and broader, CX.

更广阔的东西，那就是 CX。

I mean, for 70 years we've had programming languages

70 年来，我们的编程语言

which are about telling computers in their terms what to do.

就是用计算机的术语 告诉计算机该怎么做。

But computational language

但是计算语言涉及的东西

is about something intellectually much bigger.

在智力上要广阔得多。

It's about taking everything we can think about

它是将所有我们能想到的东西，

and operationalizing it in computational terms.

用计算语言运行起来。

You know, I built the Wolfram Language

我创建 Wolfram 语言

first and foremost because I wanted to use it myself.

最首要的原因是我自己想使用它。

And now when I use it,

现在，当我使用它时，

I feel like it's kind of giving me some kind of superpower.

我感觉它给了我某种超能力。

I just have to imagine something in computational terms.

我只需要用计算术语想象一些东西。

And then the language sort of almost magically lets me bring it into reality,

然后，这种语言 就能神奇地让我实现它、

see its consequences, and build on them.

看到它的结果 并在此基础上添砖加瓦。

And yes, that's the sort of superpower

是的，正是这种超能力

that's let me do things like our physics project.

让我能够做到一些事情， 比如我们的物理项目。

And over the past 35 years,

在过去的 35 年中，

it's been my great privilege to share this superpower with many other people,

我很荣幸能与许多人分享这项超能力，

and by doing so,

借此在多个领域取得了大量的进步。

to have enabled an incredible number of advances across many fields.

It's sort of a wonderful thing to see people, researchers, CEOs, kids,

看到人们、研究人员、 CEO、孩子们，

using our language to fluently think in computational terms,

用我们的语言 流畅地用计算术语思考，

kind of crispening up their own thinking,

提高自己的思维能力，

and then in effect, automatically calling in computational superpowers.

然后自动用上了计算超能力， 真是一件很棒的事情。

And now it's not just people who can do that.

能够做到这一点的不仅仅是人。

AIs can use our computational language as a tool, too.

AI 也可以使用我们的 计算语言作为工具。

Yes, to get their facts straight,

是的，是为了校正它们的事实，

but even more importantly, to compute new facts.

但更重要的是，要计算出新的事实。

There are already some integrations of our technology into LLMs.

我们的技术已经 集成到了大语言模型中。

There's a lot more you'll be seeing soon.

你很快就会看到更多。

And, you know, when it comes to building new things

在非常强大的新兴工作 流中构建新事物时，

in a very powerful emerging workflow,

it's basically to start by telling the LLM roughly what you want,

第一步基本上就是大致 告诉大语言模型你想要什么，

then to have it try to express that in precise Wolfram Language,

然后让它尝试用精准的 Wolfram 语言来表达，

then, and this is a critical feature of our computational language,

这就是我们计算语言的关键特色，

compared to, for example, programming language,

举个例子，与编程语言相比，

you as a human can read the code,

你作为人类可以阅读代码，

and if it does what you want,

如果它能达成你想要的目的，

you can use it as kind of a dependable component to build on.

你可以将它用作一个 可以继续构建的依赖组件。

OK, but let's say we use more and more AI,

假设我们用了越来越多的 AI，

more and more computation.

越来越多的计算。

What's the world going to be like?

世界会是什么样子？

From the industrial revolution on,

从工业革命开始，

we’ve been used to doing engineering where we can in effect,

我们就习惯于 在可能的地方进行工程设计，

see how the gears mesh to understand how things work.

看看齿轮如何相互作用， 从而了解事物是如何运作的。

But computational irreducibility

但是计算的不可还原性

now shows us that that won't always be possible.

向我们表明，这并不总是可能的。

We won't always be able to make a kind of simple human or, say,

我们不可能总是能够让一个普通人

mathematical narrative

或数学叙事

to explain or predict what a system will do.

解释或预测系统会做什么。

And yes, this is science, in effect, eating itself from the inside.

是的，这就是科学从内部吞噬自己。

From all the successes of mathematical science,

从数学科学取得的所有成功中，

we've come to believe that somehow, if we only could find them,

我们开始相信，只要找得到，

there'd be formulas to kind of predict everything.

就一定可以用公式预测一切。

But now computational irreducibility shows us that that isn't true.

但计算的不可还原性 向我们表明，事实并非如此。

And that in effect, to find out what a system will do,

要搞清楚系统会做什么，

we have to go through the same irreducible computational steps

我们必须经历与系统相同的 不可还原的计算步骤。

as the system itself.

Yes, it's a weakness of science,

是的，这是科学的弱点，

but it's also why the passage of time is significant and meaningful

但这也是为什么时间的流逝 是重要又有意义的，

and why we can't just sort of jump ahead to get the answer.

也是为什么我们不能直接 穿越时空获取答案的原因。

We have to live the steps.

我们必须经历这些步骤。

It's actually going to be, I think, a great societal dilemma of the future.

我认为，这将是未来 一个巨大的社会困境。

If we let our AIs achieve their kind of full computational potential,

如果我们想让我们的 AI 充分发挥其计算潜力，

they'll have lots of computational irreducibility

它们就会有很高的不可还原性，

and we won't be able to predict what they'll do.

我们也无法预测它们会做什么。

But if we put constraints on them to make them more predictable,

但是，如果我们对它们加以限制。 使其更具可预测性，

we'll limit what they can do for us.

我们也将限制 它们能为我们做的事情。

So what will it feel like if our world is full of computational irreducibility?

如果我们的世界充满计算的不可还原性，

那会是什么感觉呢？

Well, it's really nothing new

这并不是什么新鲜事，

because that's the story with much of nature.

因为那是一个 类似于大自然的故事。

And what's happened there

在那里发生的就是

is that we've found ways to operate within nature,

我们找到了在大自然中运作的方式，

even though nature can sometimes still surprise us.

尽管大自然有时 仍然会让我们猝不及防。

And so it will be with the AIs.

AI 也是如此。

We might give them a constitution, but there will always be consequences

我们可以给他们一部宪法，但总会有

we can't predict.

我们无法预测的后果。

Of course, even figuring out societally what we want from the AIs is hard.

当然，即使从社会层面弄清楚我们 想要从 AI 那里得到什么也很难。

Maybe we need you know, a promptocracy

也许你得知道，这是一种“提示制度”，

where people write prompts instead of just voting.

人们得写提示词，而不仅仅是投票。

But basically, every control the outcome scheme

但每一次操纵最终得出的计划

seems full of both political philosophy

似乎都充满了政治哲学

and computational irreducibility gotchas.

和计算不可还原性的陷阱。

You know, if we look at the whole arc of human history,

如果我们纵观人类历史的整个轨迹，

the one thing that's systematically changed

发生系统性改变的一件事是

is that more and more gets automated.

越来越多的东西被自动化。

And LLMs just gave us a dramatic and unexpected example of that.

大语言模型给了我们一个 激进又出乎意料的例子。

So what does that mean?

那是什么意思呢？

Does that mean that in the end, us humans will have nothing to do?

是否意味着我们人类终将无事可做？

Well, if we look at history,

如果我们回顾历史，

what seems to happen is that when one thing gets automated away,

当一件事被自动化处理时，

it opens up lots of new things to do.

它会开辟出很多新的事情。

And as economies develop,

随着经济的发展，

the pie chart of occupations seems to get more and more fragmented.

职业饼状图似乎变得越来越分散。

And now we're back to the ruliad.

我们再说回鲁利亚德。

Because at a foundational level,

因为在基础层面上，

what's happening is that automation is opening up more directions

自动化开辟了更多 鲁利亚德中的发展方向。

to go in the ruliad.

But there's no abstract way to choose between these.

但是选择哪条路 并没有简单的方法。

It's a question of what we humans want,

这是我们人类想要什么的问题，

and it requires kind of humans doing work to define that.

需要人类努力定义它。

So a society of AI as sort of untethered by human input,

不受人类输入束缚的 AI 社会

would effectively go off and explore the whole ruliad.

可以开始探索整个鲁利亚德。

But most of what they do would seem to us random and pointless,

但是在我们眼里，它们的大部分 所作所为是随机又无意义的，

much like most of nature doesn't seem to us right now,

就像我们现在觉得大自然并非如此，

like it's achieving a purpose.

它像是在追寻着某个目标。

I mean, one used to imagine that to build things that are useful to us,

曾经有人认为， 要打造对我们有用的东西，

we'd have to do it kind of step by step.

必须循序渐进。

But AI and the whole phenomenon of computation

但是 AI 和整个计算的现状

tell us that really what we need

告诉我们，我们真正需要的

is more just to define what we want.

只是定义我们想要的东西。

Then computation, AI, automation can make it happen.

然后，计算、AI、 自动化可以实现这一目标。

And yes, I think the key to defining in a clear way what we want

是的，我认为清晰地 定义我们想要什么的关键

is computational language.

是计算语言。

And, you know, even after 35 years,

即使在 35 年之后，

for many people,

对于许多人来说，

Wolfram Language is still sort of an artifact from the future.

Wolfram 语言仍然 是未来的产物。

If your job is to program, it seems like a cheat.

如果你的工作是编程， 它就像是一种外挂。

How come you can do in an hour what would usually take you a week?

为什么你能在一小时内 完成通常需要一周的时间？

But it can also be kind of daunting because having dashed off that one thing,

但这也可能有点令人望而生畏， 因为在突破了这一件事之后，

you now have to conceptualize the next thing.

你就得着手实现下一件事了。

Of course, it's great for CEOs and CTOs

当然，这对于已经准备好 在下一件事上比拼的 CEO、

and intellectual leaders who are ready to race on to the next thing.

CTO 以及有识领袖来说是个好消息。

And indeed, it's an impressively popular thing in that set.

在这种环境中确实屡见不鲜。

In a sense, what's happening is that Wolfram Language shifts

Wolfram 语言将对机械学的关注 转变成了对概念化的关注，

from concentrating on mechanics to concentrating on conceptualization,

and the key to that conceptualization is broad computational thinking.

而这种概念化的关键 就是广泛的计算思维。

So how can one learn to do that?

如何才能学会这样做呢？

It's not really a story of CS,

这不是个 CS 故事， 而是 CX 的故事。

it's really a story of CX.

And as a kind of education,

作为一种教育，

it's more like liberal arts than STEM.

它更像文科而不是 STEM。

It's part of a trend that when you automate technical execution,

趋势就是当你自动化技术操作时，

what becomes important is not figuring out how to do things,

重要的不是弄清楚如何做事，

but what to do.

而是要做什么。

And that's more a story of broad knowledge and general thinking

这是广泛知识和通用思维的故事,

than any kind of narrow specialization.

而不是细分的专业领域。

You know, there's sort of an unexpected human centeredness to all of this.

一切都意外是以人类为中心的。

We might have thought that with the advance of science and technology,

我们可能以为， 随着科学技术的进步，

the particulars of us humans would become ever less relevant.

我们作为人类的特质 将变得越来越不重要。

But we've discovered that that's not true, and that, in fact, everything,

但是我们发现事实 并非如此，其实一切，

even our physics,

甚至是我们的物理学，

depends on how we humans happen to have sampled the ruliad.

都取决于我们人类 是如何裁剪鲁利亚德的片段的。

Before our physics project,

在我们的物理项目之前，

we didn't know if our universe really was computational,

我们都不知道我们的宇宙 是不是真的是计算性的，

but now it's pretty clear that it is.

但现在很明显确实如此。

And from that, we're sort of inexorably led to the ruliad,

由此，我们被直接引向了鲁利亚德，

with all its kind of vastness

它的广阔程度

so hugely greater than the physical space in our universe.

比我们宇宙中的物理空间要大得多。

So where will we go in the ruliad?

我们要去向鲁利亚德里的哪里呢？

Computational language is what lets us chart our path.

计算语言使我们能够规划自己的道路。

It lets us humans define our goals and our journeys.

它使我们人类能够定义 我们的目标和旅程。

And what's amazing is that all the power and depth

妙就妙在每个人都可以使用 鲁利亚德中的所有力量和深度。

of what's out there in the ruliad is accessible to everyone.

One just has to learn to harness those computational superpowers,

人们只需要学会利用 这些计算超能力，

which kind of starts here,

由此开始，这里就是 我们通往鲁里亚德的大门。

you know, our portal to the ruliad.

Thank you.

谢谢。

(Applause)

（掌声）