沃尔夫勒姆：人工智能能解决科学问题吗？
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沃尔夫勒姆：人工智能能解决科学问题吗？
沃尔夫勒姆：人工智能能解决科学问题吗？
译者：花猫舰长巡海纪
人工智能最终会不会无所不能？
Won’t AI Eventually Be Able to Do Everything?
Particularly given its recent surprise successes, there’s a somewhat widespread belief that eventually AI will be able to “do everything”, or at least everything we currently do. So what about science? Over the centuries we humans have made incremental progress, gradually building up what’s now essentially the single largest intellectual edifice of our civilization. But despite all our efforts, there are still all sorts of scientific questions that remain. So can AI now come in and just solve all of them?
特别是考虑到它最近取得的令人惊讶的成功，人们普遍相信人工智能最终将能够“做一切”，或者至少是我们目前所做的一切。那么科学又如何呢？几个世纪以来，我们人类取得了渐进的进步，逐渐建造了现在基本上是我们文明中最大的智力大厦。但尽管我们付出了一切努力，仍然存在各各样的科学问题。那么人工智能现在可以介入并解决所有这些问题吗？
To this ultimate question we’re going to see that the answer is inevitably and firmly no. But that certainly doesn’t mean AI can’t importantly help the progress of science. At a very practical level, for example, LLMs provide a new kind of linguistic interface to the computational capabilities that we’ve spent so long building in the Wolfram Language . And through their knowledge of “conventional scientific wisdom” LLMs can often provide what amounts to very high-level “autocomplete” for filling in “conventional answers” or “conventional next steps” in scientific work.
对于这个终极问题，我们将看到答案不可避免且坚决是否定的。但这当然并不意味着人工智能不能重要地帮助科学进步。例如，在非常实用的层面上，LLMs 为我们花了很长时间在 Wolfram 语言中构建的计算功能提供了一种新的语言接口。通过他们的“传统科学智慧”知识 LLMs 通常可以提供相当于非常高水平的“自动完成”，用于填写科学工作中的“传统答案”或“传统的后续步骤”。
But what I want to do here is to discuss what amount to deeper questions about AI in science. Three centuries ago science was transformed by the idea of representing the world using mathematics. And in our times we’re in the middle of a major transformation to a fundamentally computational representation of the world (and, yes, that’s what our Wolfram Language computational language is all about). So how does AI stack up? Should we think of it essentially as a practical tool for accessing existing methods, or does it provide something fundamentally new for science?
但我在这里想做的是讨论人工智能在科学中的更深层次问题。三个世纪前，科学被用数学代表世界的想法所改变。在我们这个时代，我们正处于向世界的基本计算表示的重大转变之中（是的，这就是我们的 Wolfram 语言计算语言的全部内容）。那么人工智能的表现如何呢？我们是否应该将其本质上视为访问现有方法的实用工具，或者它是否为科学提供了一些全新的东西？
My goal here is to explore and assess what AI can and can’t be expected to do in science. I’m going to consider a number of specific examples, simplified to bring out the essence of what is (or isn’t) going on. I’m going to talk about intuition and expectations based on what we’ve seen so far. And I’m going to discuss some of the theoretical—and in some ways philosophical—underpinnings of what’s possible and what’s not.
我的目标是探索和评估人工智能在科学领域可以做什么、不能做什么。我将考虑一些经过简化的具体示例，以揭示正在发生（或未发生）的本质。我将根据我们迄今为止所看到的情况来谈谈直觉和期望。我将讨论什么是可能的、什么是不可能的一些理论基础——在某种程度上是哲学基础。
So what do I actually even mean by “AI” here? In the past, anything seriously computational was often considered “AI”, in which case, for example, what we’ve done for so long with our Wolfram Language computational language would qualify—as would all my “ruliological” study of simple programs in the computational universe. But here for the most part I’m going to adopt a narrower definition—and say that AI is something based on machine learning (and usually implemented with neural networks), that’s been incrementally trained from examples it’s been given. Often I’ll add another piece as well: that those examples include either a large corpus of human-generated scientific text, etc., or a corpus of actual experience about things that happen in the world—or, in other words, that in addition to being a “raw learning machine” the AI is something that’s already learned from lots of human-aligned knowledge.
那么我在这里所说的“人工智能”到底是什么意思呢？在过去，任何认真计算的东西通常都被认为是“人工智能”，在这种情况下，例如，我们长期以来使用 Wolfram 语言计算语言所做的事情就符合资格——就像我对简单程序的所有“规则学”研究一样。计算宇宙。但在这里，我将在很大程度上采用更狭义的定义，并说人工智能是基于机器学习（通常通过神经网络实现）的东西，它是根据给出的示例进行增量训练的。我通常还会添加另一件事：这些例子要么包括人类生成的科学文本的大型语料库等，要么包括关于世界上发生的事情的实际经验的语料库，或者换句话说，是在除了作为“原始学习机器”之外，人工智能还可以从大量与人类相关的知识中学到东西。
OK, so we’ve said what we mean by AI. So now what do we mean by science, and by “doing science”? Ultimately it’s all about taking things that are “out there in the world” (and usually the natural world) and having ways to connect or translate them to things we can think or reason about. But there are several, rather different, common “workflows” for actually doing science. Some center around prediction: given observed behavior, predict what will happen; find a model that we can explicitly state that says how a system will behave; given an existing theory, determine its specific implications. Other workflows are more about explanation: given a behavior, produce a human-understandable narrative for it; find analogies between different systems or models. And still other workflows are more about creating things: discover something that has particular properties; discover something “interesting”.
好的，我们已经说了人工智能的含义。那么现在我们所说的科学和“做科学”是什么意思呢？最终，这一切都是为了获取“世界上”（通常是自然世界）的事物，并找到方法将它们连接或转化为我们可以思考或推理的事物。但实际上进行科学研究有几种截然不同的常见“工作流程”。有些以预测为中心：给定观察到的行为，预测将会发生什么；找到一个我们可以明确说明系统将如何运行的模型；给定现有理论，确定其具体含义。其他工作流程更多的是解释：给定一个行为，为其生成一个人类可以理解的叙述；寻找不同系统或模型之间的类比。还有一些工作流程更多的是关于创建事物：发现具有特定属性的事物；发现一些“有趣”的东西。
In what follows we’ll explore these workflows in more detail, seeing how they can (or cannot) be transformed—or informed—by AI. But before we get into this, we need to discuss something that looms over any attempt to “solve science”: the phenomenon of computational irreducibility .
接下来，我们将更详细地探讨这些工作流程，看看它们如何能够（或不能）被人工智能改造或通知。但在讨论这个问题之前，我们需要讨论任何“解决科学问题”的尝试中都存在的一些问题：计算不可约性现象。
计算不可约性的硬极限
The Hard Limit of Computational Irreducibility
Often in doing science there’s a big challenge in finding the underlying rules by which some system operates. But let’s say we’ve found those rules, and we’ve got some formal way to represent them, say as a program. Then there’s still a question of what those rules imply for the actual behavior of the system. Yes, we can explicitly apply the rules step by step and trace what happens. But can we—in one fell swoop—just “solve everything” and know how the system will behave?
在科学研究中，寻找某些系统运行的基本规则常常面临着巨大的挑战。但假设我们已经找到了这些规则，并且我们有一些正式的方式来表示它们，比如作为一个程序。那么仍然存在一个问题：这些规则对系统的实际行为意味着什么。是的，我们可以明确地逐步应用规则并跟踪发生的情况。但我们能否一举解决“解决所有问题”并知道系统将如何运行？
To do that, we in a sense have to be “infinitely smarter” than the system. The system has to go through all those steps—but somehow we can “jump ahead” and immediately figure out the outcome. A key idea—ultimately supported at a foundational level by our Physics Project —is that we can think of everything that happens as a computational process. The system is doing a computation to determine its behavior. We humans—or, for that matter, any AIs we create—also have to do computations to try to predict or “solve” that behavior. But the Principle of Computational Equivalence says that these computations are all at most equivalent in their sophistication. And this means that we can’t expect to systematically “jump ahead” and predict or “solve” the system; it inevitably takes a certain irreducible amount of computational work to figure out what exactly the system will do. And so, try as we might, with AI or otherwise, we’ll ultimately be limited in our “scientific power” by the computational irreducibility of the behavior.
要做到这一点，从某种意义上说，我们必须比系统“无限聪明”。系统必须经历所有这些步骤，但我们可以以某种方式“跳过”并立即找出结果。一个关键思想——最终在我们的物理项目的基础层面上得到了支持——是我们可以将发生的一切视为一个计算过程。系统正在进行计算以确定其行为。我们人类——或者我们创造的任何人工智能——也必须进行计算来尝试预测或“解决”这种行为。但计算等效原理表明，这些计算在复杂程度上至多是等效的。这意味着我们不能期望系统地“跳跃”并预测或“解决”系统；不可避免地需要一定数量的不可减少的计算工作来弄清楚系统到底会做什么。因此，无论我们如何尝试，无论是人工智能还是其他方式，我们的“科学力量”最终都会受到行为的计算不可约性的限制。
But given computational irreducibility, why is science actually possible at all? The key fact is that whenever there’s overall computational irreducibility, there are also an infinite number of pockets of computational reducibility. In other words, there are always certain aspects of a system about which things can be said using limited computational effort. And these are what we typically concentrate on in “doing science”.
但考虑到计算的不可约性，为什么科学实际上是可能的呢？关键事实是，只要存在整体计算不可约性，就会存在无数个计算可约性。换句话说，系统的某些方面总是可以用有限的计算量来描述。这些就是我们“做科学”时通常关注的内容。
But inevitably there are limits to this—and issues that run into computational irreducibility. Sometimes these manifest as questions we just can’t answer, and sometimes as “surprises” we couldn’t see coming. But the point is that if we want to “solve everything” we’ll inevitably be confronted with computational irreducibility, and there just won’t be any way—with AI or otherwise—to shortcut just simulating the system step by step.
但不可避免地存在局限性，并且会遇到计算不可约性的问题。有时这些表现为我们无法回答的问题，有时表现为我们看不到的“惊喜”。但关键是，如果我们想“解决所有问题”，我们将不可避免地面临计算不可约性，而且无论是人工智能还是其他方式，都没有任何方法可以走捷径，一步步模拟系统。
There is, however, a subtlety here. What if all we ever want to know about are things that align with computational reducibility? A lot of science—and technology—has been constructed specifically around computationally reducible phenomena. And that’s for example why things like mathematical formulas have been able to be as successful in science as they have.
然而，这里有一个微妙之处。如果我们想知道的只是与计算可简化性一致的事情怎么办？许多科学和技术都是专门围绕计算可简化现象构建的。这就是为什么像数学公式这样的东西能够在科学上取得如此成功的原因。
But we certainly know we haven’t yet solved everything we want in science. And in many cases it seems like we don’t really have a choice about what we need to study; nature, for example, forces it upon us. And the result is that we inevitably end up face-to-face with computational irreducibility.
但我们当然知道我们还没有解决科学上我们想要的一切。在很多情况下，我们似乎并没有真正选择我们需要学习什么；我们只能选择学习什么。例如，大自然将其强加于我们。结果是我们不可避免地要面对计算不可约性。
As we’ll discuss, AI has the potential to give us streamlined ways to find certain kinds of pockets of computational reducibility. But there’ll always be computational irreducibility around, leading to unexpected “surprises” and things we just can’t quickly or “narratively” get to. Will this ever end? No. There’ll always be “more to discover”. Things that need more computation to reach. Pockets of computational reducibility that we didn’t know were there. And ultimately—AI or not—computational irreducibility is what will prevent us from ever being able to completely “solve science”.
正如我们将讨论的，人工智能有潜力为我们提供简化的方法来找到某些类型的计算可简化性。但总会存在计算的不可约性，导致意想不到的“惊喜”以及我们无法快速或“叙述性”到达的事情。这会结束吗？不，总会有“更多有待发现”。需要更多计算才能达到的东西。我们不知道存在一些计算可简化性。最终，无论是否有人工智能，计算的不可约性将阻止我们完全“解决科学问题”。