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

Summary

本演讲探讨了计算作为宇宙终极机器代码的概念，提出了空间和物质由离散元素组成，并通过简单计算规则逐步构建宇宙的观点。演讲者分享了他在物理学、数学和计算机科学等领域的探索，以及如何通过计算语言（Wolfram Language）来表达和实现人类的知识成就。他还讨论了AI和自动化的未来，以及如何利用计算思维来定义我们的目标和探索广阔的“规则宇宙”（ruliad）。

Takeaways

• 🌐 计算是本世纪新的、强大的世界形式化方式。
• 🚀 演讲者近50年来致力于建立基于计算理念的科学和技术高塔。
• 🤔 计算可能是宇宙的根本，这一假设在2020年得到了支持。
• 📈 空间和物质由离散元素构成，宇宙由这些元素间的关系网络定义。
• 🌌 宇宙的起源和演化可以通过简单的计算规则逐步构建。
• 🕒 时间的多线程性导致量子力学的出现，描述了分支宇宙的感知。
• 📚 科学史上有四种主要的世界观模型，处理时间的方式各有不同。
• 🌟 所有可能的计算规则都在使用中，构建了称为“ruliad”的抽象但独特的对象。
• 🧠 人类作为观察者，受限于计算能力，但我们相信自己在时间上是持续存在的。
• 📈 观察者在ruliad中所感知的必然遵循某些法则，即20世纪物理学的三大理论。
• 🛠️ 计算语言（Wolfram Language）是表达和计算思考的语言，它封装了人类文明的知识成就。
• 🌟 计算语言为人类提供了一种在ruliad中定义目标和旅程的能力。

Q & A

演讲者提到了哪些将世界形式化的方式？

演讲者提到了人类语言、数学、逻辑以及计算作为将世界形式化的方式。

演讲者认为计算在宇宙中扮演了怎样的角色？

演讲者认为计算不仅仅是一种可能的世界形式化方式，而是宇宙的终极形式化方式，即宇宙的终极机器代码。

什么是空间的原子？

空间的原子是指构成空间的基本离散元素，它们通过一系列简单的计算规则组合起来形成了空间。

演讲者如何描述时间在新物理学范式中的作用？

在新的物理学范式中，时间不再是一个简单的坐标值，而是通过多线程的计算和观察者的角度来定义，这导致了量子力学的出现。

什么是ruliad？

ruliad是一个抽象但独特的对象，它是所有可能计算过程的纠缠极限，包含了所有计算上可能发生的事情。

为什么我们感知到的物理定律是特定的？

因为我们作为观察者具有计算上的局限性，并且我们在时间上是持续存在的，这些特性导致我们在ruliad中感知到遵循特定规律的物理现象。

演讲者如何解释人工智能在探索ruliad方面的作用？

演讲者认为人工智能可以进行所谓的ruliology，即探索ruliad中的可能规则，但它们通常只会做人类不关心或不理解的事情。

Wolfram语言在计算思维中扮演了什么角色？

Wolfram语言是一种全功能的计算语言，它封装了人类文明的知识成就，并使得人们可以用计算的方式表达和实现概念。

演讲者如何看待计算不可约性对社会的影响？

演讲者认为计算不可约性是科学自我吞噬的一种表现，它表明我们无法简单地通过公式预测一切，这使得时间的流逝变得有意义。

演讲者对未来人工智能发展的看法是什么？

演讲者认为人工智能的发展将会带来无法预测的社会困境，我们需要在让AI实现其全部计算潜力和使它们更可预测之间找到平衡。

演讲者为什么认为计算语言是定义我们想要的东西的关键？

因为计算语言允许我们以计算的方式明确地表达我们的目标和旅程，它是我们与AI和自动化交互的基础，帮助我们实现目标。

Outlines

00:00

🌟 计算：宇宙的终极语言

本段讲述了计算作为正式化世界的新而强大的方式，演讲者通过近50年的科学和技术建设，探索了计算的极限。他提出了一个问题：计算是否是宇宙万物的基础。经过十年的探索，他宣布发现了宇宙的终极机器代码，证明了计算不仅是可能的正式化方式，而是宇宙的终极正式化方式。他提出了空间由离散元素构成，并通过简单计算规则的应用展示了宇宙的诞生。

05:01

🤖 探索规则宇宙：从简单到复杂

这一部分深入探讨了计算规则如何构建宇宙，以及这些规则如何应用于不同的时间线和历史路径。演讲者介绍了量子力学作为观察者感知分支宇宙的故事，并讨论了科学史上的四种主要范式。他提出了一个名为“规则宇宙”（ruliad）的概念，这是一个包含所有可能计算过程的纠缠极限。

10:01

🚀 计算语言：连接人类与AI的桥梁

演讲者分享了他对计算语言的贡献，这是一种能够表达和封装人类文明知识成就的全尺度计算语言。他强调了计算语言如何使人们能够以计算方式思考和操作概念，以及如何通过这种语言赋予人类和AI超能力。他还讨论了AI在计算空间中的潜力，以及如何通过与人类的紧密对齐来实现重大成就。

15:03

🌐 探索规则宇宙：人类的未来与AI

最后一部分探讨了AI和计算的未来发展，以及它们如何影响我们对世界的理解。演讲者讨论了计算不可约性的概念，以及它如何改变我们对系统预测和控制的看法。他还提出了关于AI在社会中的角色和目的的深刻问题，以及如何通过计算语言来明确我们的目标和愿望。他强调了计算语言在帮助我们导航规则宇宙中的重要性，并鼓励我们利用计算超能力来实现这些目标。

Keywords

💡计算

计算是指使用计算机或其他设备进行信息处理的过程。在视频中，计算被提出作为宇宙的基本构成方式，所有的物理现象和现实世界的结构都可以归结为计算过程。

💡空间原子

空间原子是指构成空间的基本离散元素，类似于物质中的原子。视频中提到，空间和其中的一切是由这些空间原子通过网络关系定义的，这些关系构成了空间的结构。

💡量子力学

量子力学是物理学的一个分支，描述了微观粒子如电子和光子的行为。视频中提到，量子力学作为观察者如何感知分支宇宙的故事而出现，它与物理空间中的引力现象相对应。

💡时间

时间是事件发生的顺序和持续性的度量。在视频中，时间被视为计算过程中的一个关键维度，它与宇宙的构建和物理现象的展现紧密相关。

💡物理范式

物理范式是指在物理学中用于解释和理解自然现象的基本概念和方法。视频中提到了四种主要的物理范式，包括物质组成、数学公式、简单计算规则和多计算范式。

💡普适计算语言

普适计算语言是一种能够表达和计算各种领域知识的形式化语言。视频中提到，这种语言能够将人类的知识以计算的方式正式化，从而使得任何人都可以通过计算机实现和探索这些知识。

💡计算不可约性

计算不可约性是指某些计算过程无法简化或预测，必须逐步骤执行才能得出结果。视频中提到，这是科学内在的局限性，也是时间重要性和意义的体现。

💡人工智能

人工智能是指由人造系统所表现出来的智能行为。视频中提到，人工智能可以通过学习和模仿人类的方式，探索计算空间，并在其中发现新的知识和事实。

💡分支空间

分支空间是一个抽象的概念，用于描述量子力学中多种可能历史的结构。视频中提到，分支空间中的量子分支现象与物理空间中的引力现象相对应。

💡自动化

自动化是指使用机器或系统在没有或很少人类干预的情况下执行任务的过程。视频中提到，自动化正在打开更多的方向，让人们在计算空间中探索新的可能性。

💡计算空间

计算空间是一个抽象的概念，指的是所有可能的计算过程所构成的无限广阔的领域。视频中提到，计算空间比我们宇宙的物理空间要大得多，它包含了所有可能的计算和知识。

Highlights

计算是本世纪新的、更强大的形式化世界的方式。

经过近50年的科学研究和技术发展，基于计算理念构建了一座更高的科学与技术之塔。

在2020年4月，宣布了宇宙的终极机器代码，它是计算的。

空间和其中的一切是由简单计算规则的连续应用产生的。

通过纯计算构建了整个宇宙。

量子力学作为分支心智感知分支宇宙的故事而出现。

科学史上可以识别出四种广泛的世界模型构建范式，它们处理时间的方式各不相同。

所有可能的计算规则都在使用中，构建了所谓的“规则宇宙”（ruliad）。

观察者在规则宇宙中感知到的必然遵循某些法则，这些法则正是20世纪物理学的三个关键理论。

使用生成性AI来探索规则宇宙，并从中获取直觉。

AI原则上可以去探索规则宇宙，但如果没有人类的引导，它们大多只会做人类不关心的事情。

通过训练大型语言模型（LLMs）来生成类似人类写作的文本，这告诉我们关于语言的语义语法和逻辑的深刻科学事实。

计算语言的目标是正式化我们对世界的知识，以计算方式表达城市、化学物质、电影、幽默和公式等。

Wolfram语言成功地创建了一个全面的计算语言，它正式化并封装了我们文明的知识成就。

计算语言为所有可想象的领域提供了一条通往计算X的道路，它比计算机科学更广阔。

计算语言允许人类和AI作为一个工具使用，以获取事实并计算新事实。

如果让AI实现其全部计算潜力，它们将有很多计算不可约性，我们将无法预测它们将做什么。

自动化正在打开规则宇宙中更多的方向，但需要人类来定义我们想要什么。

计算语言是我们在规则宇宙中定义目标和旅程的工具，它让所有人都能接触到那里的力量和深度。

Transcripts

Human language, mathematics, logic.

These are all ways to formalize the world.

And in our century,

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

For nearly 50 years,

I've had the great privilege

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

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.

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

And sometimes I'm going to summarize in a sentence

what I've written a whole book about.

But you know,

I last gave a TED talk 13 years ago,

in February 2010,

soon after WolframAlpha launched,

and I ended that talk with a question.

Question was,

is computation ultimately what's underneath everything

in our universe?

I gave myself a decade to find out.

And actually, it could have needed a century.

But in April 2020, just after the decade mark,

we were thrilled to be able to announce

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

And yes, it's computational.

So computation isn't just a possible formalization,

it's the ultimate one for our universe.

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

and from that structure of space and everything in it,

it's defined just by a network of relations

between these elements that we might call atoms of space.

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

But here's kind of a humanized representation,

a version of the very beginning of the universe.

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

and everything in it

by the successive application of very simple computational rules.

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

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

And yes, if we kept going long enough,

we could build our whole universe this way.

So eons later,

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

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

generating little ripples of gravitational radiation.

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

But like fluid mechanics emerging from molecules,

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

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

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

And there's something else.

Our computational rules can inevitably be applied in many ways,

each defining a different kind of thread of time,

a different path of history that can branch and merge.

But as observers embedded in this universe,

we're branching and merging, too.

And it turns out that quantum mechanics emerges as the story

of how branching minds perceive a branching universe.

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

show the structure of what we call branchial space,

the space of quantum branches.

And one of the stunningly beautiful things,

at least for physicists like me,

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

in branchial space gives us quantum mechanics.

So in the history of science so far,

I think we can identify sort of four broad paradigms

for making models of the world that can be distinguished

kind of by how they deal with time.

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

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

And time doesn't really enter.

But in the 1600s came the idea of modeling things

with mathematical formulas in which time enters,

but basically just as a coordinate value.

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

came the idea of making models

by starting with simple computational rules

and just letting them run.

So can one predict what will happen?

No.

There's what I call computational irreducibility,

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

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

But now there's kind of something,

something even more -- in our physics project,

there’s things that have become multi-computational,

with many threads of time

that can only be knitted together by an observer.

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

not only in fundamental physics,

but also in the foundations of mathematics and computer science,

and possibly in areas like biology and economics as well.

So I talked about building up the universe

by repeatedly applying a computational rule.

But how is that rule picked?

Well, actually it isn't,

because all possible rules are used,

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

the kind of deeply abstract but unique object

that is the entangled limit of all possible computational processes.

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

So this ruliad is everything.

And we as observers are necessarily part of it.

In the ruliad as a whole,

in a sense, everything computationally possible can happen.

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

And there are two crucial facts about us.

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

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

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

So then, here's the big result.

What observers with those characteristics perceive in the ruliad

necessarily follows certain laws.

And those laws turn out to be precisely

the three key theories of 20th century physics:

general relativity, quantum mechanics,

and statistical mechanics in the second law.

So it's because we're observers like us

that we perceive the laws of physics we do.

We can think of sort of different minds

as being at different places in rulial space.

Human minds who think alike are nearby,

animals further away,

and further out, we get to kind of alien minds

where it's hard to make a translation.

So how can we get intuition for all of this?

Well, one thing we can do is use generative AI

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

aligned with images we humans have produced.

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

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

So zooming out, we saw there

what we might call Cat Island.

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

Occasionally things will look familiar,

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

In physical space, we explore the universe

by sending out spacecraft.

In rulial space, we explore more

by expanding our concepts and our paradigms.

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

by sampling possible rules,

doing what I call ruliology.

So even with incredibly simple rules,

there's incredible richness.

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

with things we humans understand or care about.

It's like when we look at the natural world

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

So even after everything our civilization has achieved,

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

What about AIs?

Well, just like we can do ruliology,

AIs can in principle go out and explore rulial space.

Left to their own devices, though,

they'll mostly just be doing things

we humans don't connect with or care about.

So the big achievements of AI in recent times

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

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

that's typical of what we humans write.

And yes, the fact that this works

is undoubtedly telling us some deep scientific things

about the semantic grammar of language

and generalizations of things like logic

that perhaps we should have known centuries ago.

You know, for much of human history,

we were kind of like the LLMs,

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

But then came more systematic formalization and eventually computation.

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

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

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

and our AIs, understand.

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

to kind of trying to build that bridge.

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

a language for computational thinking.

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

to have computational ways to represent cities and chemicals and movies

and humor and formulas and our knowledge about them.

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

but it's something very unique and different.

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

and is now the Wolfram Language,

I think we firmly succeeded in creating

a truly full-scale computational language.

In effect,

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

and encapsulating, in computational terms,

some facet of the intellectual achievements of our civilization.

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

sort of finding the essence of everything and coherently expressing it

in the design of our computational language.

For me personally,

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

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

And nowadays sharing that process with the world

in things like open live streams and so on.

A few centuries ago,

the development of mathematical notation,

and what amounts to the language of mathematics,

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

and eventually all of modern mathematical science.

And computational language now provides a similar path,

letting us ultimately create a computational X

for all imaginable fields X.

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

but computational language opens up something ultimately much bigger

and broader, CX.

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

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

But computational language

is about something intellectually much bigger.

It's about taking everything we can think about

and operationalizing it in computational terms.

You know, I built the Wolfram Language

first and foremost because I wanted to use it myself.

And now when I use it,

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

I just have to imagine something in computational terms.

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

see its consequences, and build on them.

And yes, that's the sort of superpower

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

And over the past 35 years,

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

and by doing so,

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

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

using our language to fluently think in computational terms,

kind of crispening up their own thinking,

and then in effect, automatically calling in computational superpowers.

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

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

Yes, to get their facts straight,

but even more importantly, to compute new facts.

There are already some integrations of our technology into LLMs.

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

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

in a very powerful emerging workflow,

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

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

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

compared to, for example, programming language,

you as a human can read the code,

and if it does what you want,

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

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

more and more computation.

What's the world going to be like?

From the industrial revolution on,

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

see how the gears mesh to understand how things work.

But computational irreducibility

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

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

mathematical narrative

to explain or predict what a system will do.

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

From all the successes of mathematical science,

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

there'd be formulas to kind of predict everything.

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

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

we have to go through the same irreducible computational steps

as the system itself.

Yes, it's a weakness of science,

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

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

We have to live the steps.

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

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

they'll have lots of computational irreducibility

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

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

we'll limit what they can do for us.

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

Well, it's really nothing new

because that's the story with much of nature.

And what's happened there

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

even though nature can sometimes still surprise us.

And so it will be with the AIs.

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

we can't predict.

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

Maybe we need you know, a promptocracy

where people write prompts instead of just voting.

But basically, every control the outcome scheme

seems full of both political philosophy

and computational irreducibility gotchas.

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

the one thing that's systematically changed

is that more and more gets automated.

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

So what does that mean?

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

Well, if we look at history,

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

it opens up lots of new things to do.

And as economies develop,

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

And now we're back to the ruliad.

Because at a foundational level,

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

to go in the ruliad.

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

It's a question of what we humans want,

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

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

would effectively go off and explore the whole ruliad.

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

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

like it's achieving a purpose.

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

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

But AI and the whole phenomenon of computation

tell us that really what we need

is more just to define what we want.

Then computation, AI, automation can make it happen.

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

is computational language.

And, you know, even after 35 years,

for many people,

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

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

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

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

you now have to conceptualize the next thing.

Of course, it's great for CEOs and CTOs

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

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

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

from concentrating on mechanics to concentrating on conceptualization,

and the key to that conceptualization is broad computational thinking.

So how can one learn to do that?

It's not really a story of CS,

it's really a story of CX.

And as a kind of education,

it's more like liberal arts than STEM.

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

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

but what to do.

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

than any kind of narrow specialization.

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

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

the particulars of us humans would become ever less relevant.

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

even our physics,

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

Before our physics project,

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

but now it's pretty clear that it is.

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

with all its kind of vastness

so hugely greater than the physical space in our universe.

So where will we go in the ruliad?

Computational language is what lets us chart our path.

It lets us humans define our goals and our journeys.

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

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

One just has to learn to harness those computational superpowers,

which kind of starts here,

you know, our portal to the ruliad.

Thank you.

(Applause)