"Exotic mind-like entities": Why we need new language for AI

“异类的类心智实体”：为什么我们需要为人工智能寻找新的语言

Editor’s Note: This transcript has been lightly edited with AI for clarity and readability, removing verbal repetitions, filler words, and false starts while preserving the original meaning and conversational tone of the speakers. Significant omissions or clarifications are indicated with [brackets].

编者按：本文字记录已通过AI 进行轻度编辑以提升清晰度和可读性，删除了口头重复、填充词和开头语，同时保留了发言者的原意和对话语气。重大省略或澄清以 [方括号] 标示（译文未添加）。

Introduction and Background

引言与背景

[00:00:00 - 01:56:03]

Murray Shanahan: I think there are just a huge number of enormously interesting philosophical questions that AI gives rise to. What is the nature of the human mind? What is the nature of mind?

Murray Shanahan：我认为人工智能引发了大量极其有趣的哲学问题。人类心智的本质是什么？心智的本质是什么？

Hannah Fry: What about consciousness?

Hannah Fry：那意识呢？

Murray Shanahan: I do think that is the wrong question, and I think it's wrong in many ways.

Murray Shanahan：我确实认为那是个错误的问题，而且我认为它在很多方面都是错的。

Hannah Fry: How good do you think that AI is at reasoning?

Hannah Fry：你认为人工智能的推理能力有多强？

Murray Shanahan: Well, that's a very interesting and kind of open question and somewhat controversial. It's really astonishing to think that every single child born today will grow up in a world where they've never known a world in which machines can't talk to them.

Murray Shanahan：嗯，这是一个非常有趣、尚无定论且颇具争议的问题。想想看，今天出生的每一个孩子，都将成长在一个机器不能与他们交谈的世界从未存在过的世界，这实在令人震惊。

Hannah Fry: Welcome back to Google DeepMind the podcast. My guest on this episode is Murray Shanahan, Professor of Cognitive Robotics at Imperial College London and Principal Research Scientist at Google DeepMind. Now, we have all heard the stories about people falling in love with their chatbots, about people pushing large language models to contemplate their own existence or questioning the limits of their conceptual understanding of reality. But these kinds of questions about self-identity and thinking and metacognition have been puzzling philosophers for millennia already. And so it makes sense that they should be turning to AI to interrogate the most profound questions about the nature of AI's intelligence, of its current capabilities, even its consciousness or otherwise.

Hannah Fry：欢迎回到 Google DeepMind 播客。本期嘉宾是 Murray Shanahan，伦敦帝国理工学院认知机器人学教授兼 Google DeepMind 首席研究科学家。我们都听说过人们爱上聊天机器人的故事，听说过人们推动大语言模型思考自身存在，或质疑它们对现实的概念理解的极限。但这类关于自我认同、思维和元认知的问题，哲学家们已经困惑了数千年。因此，人们转向人工智能来追问关于人工智能智能本质及其当前能力、乃至其意识与否的最深刻问题，这是很自然的。

Hannah Fry: Murray Shanahan has been working in the field of AI since the 1990s. And if you've been following this podcast for a while, you will remember him as the man that consulted on the 2014 science fiction film Ex Machina about a computer programmer who gets the chance to test the intelligence of a female robot, Ava, and ultimately questions whether she is conscious. Welcome back to the podcast, Murray.

Hannah Fry：Murray Shanahan 自 1990 年代起就一直在人工智能领域工作。如果你关注本播客已有一段时间，你会记得他就是那位为 2014 年科幻电影《机械姬》（“Ex Machina”）担任顾问的人，影片讲述的是一名程序员获得机会测试女性机器人 Ava 的智能，并最终质疑她是否有意识的故事。欢迎回到播客，Murray。

Murray Shanahan: Thanks Hannah.

Murray Shanahan：谢谢 Hannah。

Science Fiction and AI Representations

科幻电影与人工智能的呈现

[01:56:03 - 03:31:41]

TLDR: Murray discusses his work on Ex Machina and how science fiction films like Her have portrayed AI relationships. He notes that Her surprisingly predicted how people would form relationships with disembodied AI systems.

摘要：Murray 讨论了他参与《机械姬》的工作，以及像《她》（“Her”）这样的科幻电影如何描绘人工智能关系。他指出，《她》惊人地预言了人们会如何与无实体的人工智能系统建立关系。

Hannah Fry: Just thinking back because I know that you played a key role in Ex Machina, the Alex Garland film. What do you think you got right in that film? And in other science fiction films that were around at the time? I mean, thinking back to sort of 10, 15 years ago, were we on the right track?

Hannah Fry：回想一下，我知道你在 Alex Garland 的电影《机械姬》中扮演了关键角色。你认为那部电影有哪些地方拍对了？当时其他的科幻电影呢？我是说，回想 10 到 15 年前，我们当时的思路对吗？

Murray Shanahan: One respect in which Ex Machina really did a great service was that it raises a whole load of very interesting and provocative questions about consciousness and about AI and consciousness, and therefore about consciousness itself. So that's one huge success.

Murray Shanahan：《机械姬》有一个方面确实做出了巨大贡献，那就是它提出了一大批关于意识、关于人工智能与意识——进而关于意识本身——的非常有趣且引人深思的问题。所以这是一大成功。

Murray Shanahan: But it's interesting that just very shortly before Ex Machina came out, Her came out. So Spike Jones's movie Her came out. And at the time, I really wasn't all that keen on Her as a movie because I just thought it was so implausible that a person could fall in love with this kind of disembodied voice, even if it's Scarlett Johansson's. How wrong was that? As a bit of prediction, I think Her did amazingly well at predicting the world we've got now. Now, we don't know quite how things are going to unfold in the next few years because maybe robotics will progress rapidly as well in the way that language has in AI. But at the moment, it's all about disembodied language. And also Her showed how people can, in fact, form relationships, whatever, in the broadest sense with disembodied AI systems, which is an extraordinary thing really.

Murray Shanahan：但有趣的是，就在《机械姬》上映前不久，《她》上映了。Spike Jones 的电影《她》。当时我其实并不怎么喜欢《她》这部电影，因为我觉得一个人会爱上一个没有实体的声音——即便是 Scarlett Johansson 的声音——也太不可信了。这个判断错得多离谱啊！作为一种预测，我认为《她》在预言我们今天所处的世界方面做得非常出色。现在我们还不完全知道未来几年会如何展开，因为也许机器人技术也会像人工智能语言那样快速进步。但目前来看，一切都是关于无实体的语言。《她》还展示了人们如何能够事实上与无实体的人工智能系统建立最广义的关系，这确实是一件非同寻常的事。

The History and Evolution of AI

人工智能的历史与演变

[03:31:41 - 05:46:34]

Hannah Fry: Okay. We're talking 10, 15 years ago, but your involvement in AI goes back much further than this. You knew John McCarthy?

Hannah Fry：好的。我们说的是 10 到 15 年前，但你参与人工智能的历史远比这更久远。你认识 John McCarthy 吗？

Murray Shanahan: I did know John McCarthy. I knew him very well.

Murray Shanahan：我确实认识 John McCarthy。我和他很熟。

Murray Shanahan: John McCarthy was a professor of computer science and artificial intelligence, back in the day, he actually coined the phrase artificial intelligence. And was one of the authors of the proposal for the very famous Dartmouth conference that took place in 1956, which was the first AI conference in the world. And that conference really mapped out the whole field.

Murray Shanahan：John McCarthy 是计算机科学与人工智能教授，早年间正是他创造了“人工智能”（“artificial intelligence”）这个短语。他是 1956 年在达特茅斯举行的著名会议的提案作者之一，那是世界上第一次人工智能会议，那次会议真正勾勒了整个领域的蓝图。

Murray Shanahan: People just weren't thinking about this kind of thing seriously at all. It was just a handful. I think he was a real radical thinker and always was.

Murray Shanahan：当时人们根本没有认真思考这类事情，只是极少数人在做。我认为他是一个真正的激进思想家，而且一直都是。

The Term "Artificial Intelligence"

“人工智能”这个术语

[04:05:23 - 05:46:34]

TLDR: Murray discusses the terminology of "artificial intelligence" coined by McCarthy in 1955, addressing criticisms of the term while defending it as appropriate despite its limitations.

摘要：Murray 讨论了 McCarthy 在 1955 年创造的“人工智能”这一术语，回应了对此术语的批评，同时为其辩护，认为尽管有局限性，它仍是恰当的。

Hannah Fry: Okay, that choice of words, artificial intelligence back in 1955. Was it a good choice of words?

Hannah Fry：好，1955 年选择的“artificial intelligence”（人工智能）这个词。这个选词好吗？

Murray Shanahan: Yeah, I mean, I still think it was. I know that some people don't think that perhaps it wasn't a good choice of words, but I still—

Murray Shanahan：是的，我仍然认为它选得好。我知道有些人认为这个词可能选得不好，但我仍然——

Hannah Fry: Give us some of their arguments.

Hannah Fry：给我们说说他们的论点。

Murray Shanahan: So, first of all, there is the word intelligence. So intelligence itself is, in some ways, a very contentious concept. Especially if people think about IQ tests and that kind of thing. And the idea that intelligence is something that can be quantified on a straightforward simple scale, and then some people are more intelligent than others. And I think in psychology, it's well recognized today that there are many different kinds of intelligence. And this is a really important point, right? There is that concern about that word there. So what would you have used differently? Well, maybe artificial cognition or something. I often use the word cognition to mean thinking and processing information and so on. But yeah, it doesn't have the same ring to it, does it? Let's be honest.

Murray Shanahan：首先，有“intelligence”（智能）这个词。智能本身在某些方面就是一个非常有争议的概念，尤其是当人们想到智商测试之类的东西时。认为智能可以用一个简单直接的尺度来量化，然后说有些人比其他人更聪明。我认为今天心理学界公认有很多种不同的智能，这是一个非常重要的观点，对吧？对那个词有那样的担忧。那你会用什么不同的词呢？好吧，也许可以用“人工认知”（“artificial cognition”）之类的。我经常用“认知”（“cognition”）这个词来表示思考和信息处理等。但，说实话，它没有同样的韵味，不是吗？

Hannah Fry: No. Especially not now. I think we're too far down this road, aren't we?

Hannah Fry：没有。尤其是现在。我觉得我们已经在这条路上走得太远了，不是吗？

Murray Shanahan: Yeah. The word artificial, I don't really have a problem with the word artificial. That seemed like the right kind of thing. It's alluding to the fact that it's something that we've built and that hasn't evolved in nature. And so that seems the right sort of word.

Murray Shanahan：是的。对于“artificial”（人工的）这个词，我倒没什么意见。这个词似乎挺合适的。它暗指这是我们建造的、不是在自然界中进化而来的东西。所以这个词用对了。

Hannah Fry: The objection to that word, I guess is that ultimately everything that artificial intelligence is built on is at some level constructed by humans.

Hannah Fry：对那个词的反对意见，我想在于，归根结底，人工智能所建构的一切，在某种程度上都是由人类构建的。

Murray Shanahan: Sure. Yes. But it is. So what's wrong with the word in that case? I mean, I think that's true.

Murray Shanahan：当然，是的。但这有什么问题呢？我觉得这确实是事实。

From Symbolic AI to Neural Networks

从符号人工智能到神经网络

[05:46:34 - 09:44:04]

TLDR: Murray explains the shift from symbolic AI (rule-based systems) to neural networks, describing how symbolic AI relied on explicit rules while modern approaches learn patterns from data.

摘要：Murray 解释了从符号人工智能（基于规则的系统）到神经网络的转变，描述了符号人工智能依赖显式规则，而现代方法从数据中学习模式。

Hannah Fry: And you are working on symbolic AI, right? Just talk to us about the difference between that and the other types and where we're at now with the contrast.

Hannah Fry：你当时在做符号人工智能，对吧？跟我们说说它与其他类型之间的区别，以及我们现在的对比状况。

Murray Shanahan: Absolutely, yeah. The so-called symbolic paradigm of artificial intelligence was very much preeminent, very much dominant for decades. So the idea there is that it's all about the manipulation of symbols and of language-like sentences and symbols, and using kind of reasoning processes with those symbols. So the classic example would be an expert system. So where back in the 1980s, people were building these expert systems. And the idea was that you would try to encode medical knowledge, say, in a set of rules. And the rules would be something like, "if the patient has a temperature of 104 and their skin is purple, then there's a 0.75% probability that they've got skinitis or something." You could tell that I'm not a medical doctor. And then you'd have thousands and thousands of these sorts of rules would be put into a kind of big knowledge base. And then you'd have what was called an inference engine which would carry out logical reasoning over all of these rules and therefore come to some conclusion about what the likely disease was in that case.

Murray Shanahan：当然。所谓的符号人工智能范式在几十年里一直非常突出、非常占主导地位。其核心思想是，一切都是关于符号的操控，关于类似语言的句子和符号，并使用这些符号进行推理过程。经典例子是专家系统。在 1980 年代，人们就在构建这些专家系统，想法是尝试将医学知识编码到一组规则中。规则可能类似于：“如果病人体温 104 度且皮肤呈紫色，那么有 0.75% 的概率得了皮肤炎之类的。”你能看出我不是医生。然后你会有成千上万条这样的规则，放进一个巨大的知识库里。然后你有一个所谓的推理引擎，它会对所有这些规则执行逻辑推理，从而得出该病例可能的疾病结论。

Hannah Fry: But it was a lot of if this, then that.

Hannah Fry：但基本上就是大量的“如果这样，那就那样”。

Murray Shanahan: It was a lot of if-then type rules largely. And one of the big problems with that is that where do the rules come from? Well, somebody has to write them all out, basically. And so there was a whole field of knowledge elicitation where you go around to experts, and you try and extract from them their understanding in their domain, which could be medical diagnosis, it could be fixing photocopiers, it could be the law, and you try and codify all of this in a computer comprehensible, very precise rule. That was a very cumbersome process and also what you ended up with at the end was very, very brittle. It would go wrong in all kinds of ways.

Murray Shanahan：大体上就是大量的 if-then 类型规则。其中一个很大的问题是：规则从哪来？好吧，基本上得有人把它们全部写出来。因此，有一整个叫做“知识抽取”的领域，你去走访专家，试图从他们那里提取出他们在自己领域中的理解——可能是医学诊断、修复印机、法律——然后你试着用计算机可理解的、非常精确的规则把所有这些都编码化。这是一个非常繁琐的过程，而且最终得到的东西非常非常脆弱，会在各种方面出错。

Murray Shanahan: And another big area of research was common sense because often it was realized that we implicitly have an enormous amount of common-sense knowledge about the everyday world to do with just everyday objects, the fact that they're solid, the fact that they move in certain ways, they fit into each other in certain ways, liquids and gases and gravity, all kinds of things like that. And we actually bring all of that knowledge to bear all the time in what we're doing, but it's sort of unconscious. So then there was a big project or various big projects to try and codify all of that common-sense knowledge. And trying to turn that into like axioms and logic and rules and everything was a nightmare.

Murray Shanahan：另一个重要的研究领域是常识，因为人们常常意识到，我们对日常世界隐性地拥有海量的常识知识，涉及到日常物品、它们是固体的、它们以特定方式运动、它们以特定方式彼此组合、液体和气体、重力，诸如此类。我们实际上无时无刻不在调用所有这些知识，但它是无意识的。于是，出现了一个大项目或多个大项目，试图将所有这些常识知识编码化。试图将它转化为公理、逻辑和规则简直是一场噩梦。

Murray Shanahan: So I eventually, I think by about the early 2000s, I'd really thought that this research paradigm was kind of doomed, to be honest. And I sort of started moving away from it.

Murray Shanahan：所以我最终——我想大约在 2000 年代初——真的认为这个研究范式基本上注定要失败，说实话。我开始逐渐远离它。

Hannah Fry: But then, of course, along came things like neural networks and so on. Yes. Which was much less about if-then rules and much more about sort of extracting information from a large amount of data.

Hannah Fry：但后来，当然，出现了神经网络之类的东西。是的。它不再是 if-then 规则，而更多是关于从大量数据中提取信息。

Murray Shanahan: Yeah.

Murray Shanahan：是的。

Hannah Fry: But I sort of wonder now about, now that language is effectively cracked, have we sort of reached a higher level of abstraction where we can go back to some more of those symbolic techniques, some of those more symbolic ideas.

Hannah Fry：但我现在在想，既然语言基本上已经被破解了，我们是否达到了一个更高的抽象层次，可以回过头来使用一些符号技术、一些符号化的思想。

Murray Shanahan: Yeah, well, we certainly have because nowadays, one of the hot topics at the moment with large language models is reasoning. So you have these so-called chain of thought models that actually carry out a whole—they rather than simply generating an answer to a question, they generate a whole chain of reasoning before they issue the answer. And that can be very, very effective. So it's interesting how that harks back in many ways to the kind of thing that people were looking at back in the days of symbolic AI. But the underlying substrate for doing all of that is very, very different indeed because it's not hard-coded rules. It's as you mentioned, it's neural, it's neural networks that have learned.

Murray Shanahan：是的，我们确实做到了。因为如今，大语言模型领域的一个热门话题就是推理。于是你有了所谓的“思维链”模型，它们实际上会执行一整套——它们不是简单地生成问题的答案，而是在给出答案之前先生成一整套推理链。这可以非常有效。有趣的是，这在很多方面让人回想起符号人工智能时代人们研究的那些东西。但支撑这一切的底层基础非常非常不同，因为它不是硬编码的规则。正如你所说，是神经的，是已经学会了的神经网络。

AI Reasoning and Intelligence

人工智能推理与智能

[09:44:04 - 13:20:23]

TLDR: Murray discusses the differences between human-like reasoning and more formal mathematical reasoning in AI systems, noting that LLMs can reason in everyday contexts but may struggle with formal theorem proving compared to purpose-built systems.

摘要：Murray 讨论了人工智能系统中类人推理与更形式化的数学推理之间的区别，指出大语言模型可以在日常语境中进行推理，但在形式化定理证明方面可能不如专用系统。

Hannah Fry: Let me pick up on that point about reasoning. As a philosopher, background in logic, how good do you think that AI is at reasoning?

Hannah Fry：让我来谈谈推理这个点。作为有逻辑学背景的哲学家，你认为人工智能的推理能力有多强？

Murray Shanahan: Well, that's a very interesting and kind of open question and somewhat controversial. So computer scientists and AI people, they have a particular notion of reasoning, a particular concept of reasoning, which very much harks back to formal logic and theorem proving. And so in the days of symbolic AI, for example, then you had systems that were really very good at doing theorem proving with formal logic. And so people think, well, that's proper reasoning. That's really your hardcore kind of reasoning. And today's large language models, they can't match the performance of a hand coded theorem prover, or logic engine of the sort that's been around for decades.

Murray Shanahan：嗯，这是一个非常有趣、尚无定论且颇具争议的问题。计算机科学家和人工智能从业者对推理有一个特定的概念，这个概念很大程度上回溯到形式逻辑和定理证明。所以在符号人工智能时代，比如，你有那些真的非常擅长用形式逻辑进行定理证明的系统。因此人们认为，那才是真正的推理，那才是硬核的推理。而今天的大语言模型，在性能上无法匹配那种手工编码的定理证明器，或是已经存在了几十年的那种逻辑引擎。

Hannah Fry: Give me an example of the type of theorem that might be able to be proved by a hard-coded system.

Hannah Fry：给我举一个硬编码系统能够证明的定理类型的例子。

Murray Shanahan: So, it will be where you've got maybe 20 or 30 axioms of logic and So it might be something like the number that follows one is two. It could be something like that. It could be in the domain of number theory or something very mathematical, but it could be something much more everyday. So, for example, suppose that you've got some very difficult logistical planning problem where maybe you have hundreds of lorries and depots and goods and all kinds of things like that. And you need to plan the routes and the deployment of the lorries and where they're going to go. So that's a very kind of difficult problem computationally, and it can be expressed very precisely in formal rules. And that's the kind of situation where you might want to use a good old-fashioned straightforward algorithm, planning algorithm of the sort that's been around for a long time. Now, contemporary large language models are getting better and better at this kind of thing, but they're still, you don't have those kinds of mathematical guarantees that they're always going to come up with the exact right answer. And it's very easy to kind of make examples where you have more and more axioms and so on, where they're going to slip up.

Murray Shanahan：比如，你可能有 20 或 30 条逻辑公理。可能是像“1 后面的数是 2”这样的东西。可能是数论领域或非常数学化的东西，但也可能是更日常的东西。例如，假设你有一个非常困难的物流规划问题，也许你有几百辆卡车、仓库、货物等等。你需要规划卡车的路线和部署以及它们要去的地方。这是一种计算上非常困难的问题，可以用形式规则非常精确地表达。这种情况你可能就想用那种好的老式直接算法，已经存在很长时间的规划算法。现在，当代大语言模型在这方面的表现越来越好了，但它们仍然——你无法得到那种总能得出精确正确答案的数学保证。而且你很容易构造出公理越来越多的例子，它们就会出错。

Murray Shanahan: There's a whole separate research direction, which is to try and build more hand-coded things that combine today's AI techniques with more old-fashioned symbolic techniques to specifically for mathematical theorem proving, and Deep Mind has done some amazing work along those lines. But that's different from large language models. So with large language models, we're thinking of these chatbots that can talk about anything under the sun. And one of the things they happen to be able to do is a kind of reasoning. So that kind of that's not going to be at the moment quite as good as you could do by hand building something for that.

Murray Shanahan：有一个完全独立的研究方向，就是尝试构建更多手工编码的东西，将今天的人工智能技术与更老式的符号技术相结合，专门用于数学定理证明，DeepMind 在这方面做了一些了不起的工作。但那不同于大语言模型。对于大语言模型，我们想到的是这些可以谈论天下万事的聊天机器人。它们碰巧能做的一件事是一种推理。但这种推理目前还不会像你手工为此专门构建的系统做得那么好。

Hannah Fry: It's kind of interesting because hand building something is—you end up with something that's very rigid.

Hannah Fry：这挺有意思的，因为手工构建会得到非常僵硬的东西。

Murray Shanahan: That's the problem, yes.

Murray Shanahan：这就是问题所在，是的。

Hannah Fry: And brittle. Yes, absolutely. But then at the same time, the sort of flexibility that you get from the generative AI approach, it's too floppy, as it were. You know, you want the rigidity in there.

Hannah Fry：而且脆弱。是的，绝对的。但与此同时，生成式人工智能方法带来的灵活性又太松散了，可以这么说。你想要的是那种刚性在里面。

Murray Shanahan: Well, you know, maybe or maybe not. I mean, I think many examples of human affairs are just not as black and white as that. And you do maybe want things to be a bit more blurry. Even in sort of simple everyday things, like, what would be good flowers to put over in this corner of the garden? Well, we've already got some roses in that corner there, and those roses are yellow. So maybe we can't have too much yellow, so maybe we need to move them to the other corner of the garden.

Murray Shanahan：嗯，你知道，也许是，也许不是。我的意思是，我认为人类事务中的很多例子并不都那么黑白分明。你也许确实希望事情模糊一些。即使在一些简单的日常事务中，比如，“这个花园角落适合种什么花？嗯，那个角落已经有些黄玫瑰了，所以也许我们不能有太多黄色，也许我们需要把它们移到花园的另一个角落。”

Defining "Real Reasoning"

定义“真正的推理”

[13:20:23 - 14:33:22]

TLDR: Murray challenges the notion of "real reasoning," suggesting that reasoning exists on a spectrum and can manifest differently in different contexts. He argues that everyday reasoning is different from formal mathematical reasoning.

摘要：Murray 挑战了“真正推理”的概念，暗示推理存在于一个光谱上，在不同语境中表现为不同形式。他认为日常推理不同于形式化的数学推理。

Hannah Fry: But then at the same time though, is this real reasoning? Or is this just the AI kind of mimicking well-structured arguments that have existed in the training data, but just in a sort of novel environment?

Hannah Fry：但与此同时，这是真正的推理吗？还是只是 AI 在模仿训练数据中存在的结构良好的论证，只是放到了一个新颖的环境中？

Murray Shanahan: Yeah. Well, of course, that begs the question, what is real reasoning? I don't think it's not written in the sky, what real reasoning is. It's up to us to define the concept of real reasoning or of reasoning. And so we have that, we were talking earlier on about kind of mathematical reasoning of the sort that logicians do and that was, is done by kind of theorem provers in the past and so on and today. But that's, when people were first using the terms like reasoning, they weren't thinking of that kind of thing. And when we use the word reasoning in everyday life, we're not thinking about that sort of thing. So if you're chatting away to a large language model about your garden and you sort of say, well, I'm thinking about what plants, and it says, well, maybe you should consider this kind of plant in that kind of location because that's best for the soil and given you said that the winds, it's windy there and, we would just say that that is supplying reasons. I mean, it is supplying reasons. Now, where they come from as another matter. So people might say, well, it's just mimicking what's in the training set, but, it's probably never seen exactly that example, that kind of scenario exactly before. So it's moving beyond the training set to a certain extent. And I think it's just using the everyday concept of reasoning in an everyday way to call that reasoning.

Murray Shanahan：是的。当然，这本身就在回避问题：什么是真正的推理？我不认为“真正的推理是什么”是刻在天上的。推理或“真正推理”的概念是由我们来定义的。我们之前谈到过逻辑学家做的那种数学推理，也是过去的定理证明器所做的那种。但当人们最初使用“推理”这样的词时，他们想的不是那种东西。当我们在日常生活中使用“推理”这个词时，我们想的也不是那种东西。所以，如果你在和大语言模型聊你的花园，你大致说，我在考虑种什么植物，它说，嗯，考虑到土壤条件和你说那里风大，也许你应该考虑在这个位置种那种植物。我们会直接说，它是在给出理由。我的意思是，它确实在给出理由。至于这些理由从哪里来，那是另一回事。所以人们可能会说，它只是在模仿训练集里的东西。但，它可能从未见过完全相同的那个场景、那个例子。所以在一定程度上它超越了训练集。我认为把这种用日常方式在日常生活意义上的推理称为推理，是恰当的。

Testing AI Capabilities

测试人工智能的能力

[14:33:22 - 22:06:41]

The Turing Test and Its Limitations

图灵测试及其局限

[14:33:22 - 16:48:12]

TLDR: Murray criticizes the Turing Test as too narrow because it focuses only on language without testing embodied cognition, though he acknowledges modern LLMs could likely pass the test.

摘要：Murray 批评图灵测试过于狭隘，因为它只关注语言而不测试具身认知，尽管他承认现代大语言模型很可能已经能够通过该测试。

Hannah Fry: I'm just thinking back to some of the different characteristics that the earlier philosophers wanted artificial intelligence to have. And reasoning being one of them. But then, also the Turing test, which of course, gets brought up all the time about a way to test for the capability of an artificial intelligence. I mean it's kind of controversial, right? I suppose in terms of how good it ever would have been as a test for the capability of AI. What's your take on it? Do you think it was ever a good test?

Hannah Fry：我在回想早期的哲学家们希望人工智能具备的一些特征。推理是其中之一。但还有图灵测试，当然它经常被提起作为一种检验人工智能能力的方法。我想它在关于能否作为人工智能能力的良好测试方面是有争议的，对吧？你的看法呢？你认为它曾经是一个好的测试吗？

Murray Shanahan: No. I've always thought it was a terrible test, but a really great spur to philosophical discussion about things. And again, with a bit of hindsight, maybe I might backtrack on a little bit on a few of my views because I was certainly very, very much of the opinion that embodiment was a critical facet of intelligence was critical for achieving intelligence.

Murray Shanahan：不。我一直认为这是一个糟糕的测试，但却是推动哲学讨论的绝佳素材。而且，事后回看，我可能会在某些观点上稍微退一步，因为我曾经非常非常强烈地认为，具身性是智能的一个关键方面，是实现智能的关键。

Hannah Fry: Which doesn't come anywhere near the Turing test at all, right?

Hannah Fry：而图灵测试完全与此无关，对吧？

Murray Shanahan: No, the Turing test is absolutely explicitly nothing to do with embodiment because the judge, so just to remind people what it is. So you have, in the Turing test, you have kind of two subjects, as it were, one is a human and the other is the computer. And then you have a judge. The human judge can't see which is the computer and which is the human. And they're only talking to these subjects through a kind of chat-like interface. They can't see whether they're embodied or not. So we can, easily suppose that the computer might be one of today's large language models. In which case, I have to say that, today they would pretty much would pass the Turing test. I mean, we've got to that point, which is amazing, really. But, so I used to think that it was a bad test because it didn't test any of these embodied skills. So you'd need a robot really to test whether something was capable of the kind of everyday cognition that we all put to use when we're, for example, making a cup of tea or something.

Murray Shanahan：没错，图灵测试绝对明确地与具身性无关。因为——让我提醒大家一下——在图灵测试中，有两个“受试者”，一个是人类，另一个是计算机。然后有一个裁判。人类裁判看不到哪个是计算机、哪个人是人类。他们只能通过一种类似聊天的界面与这些受试者交谈。他们看不到它们是否有身体。所以我们很容易假设计算机就是今天的大语言模型之一。如果是这样，我不得不说，今天它们基本上能通过图灵测试了。我们已经到了那个地步，这真的很惊人。但，我以前认为这是个糟糕的测试，因为它不测试任何具身性技能。你需要一个机器人才能真正测试某样东西是否有能力进行我们都在使用的日常认知，比如泡杯茶的时候。

Hannah Fry: Because otherwise it's a very, very narrow form of intelligence.

Hannah Fry：因为否则它就只是一种非常非常狭隘的智能形式。

Murray Shanahan: Yes, it's all to do with language and reasoning and not to do with the kinds of things that evolution, developed in us and in other animals before language, right? Which is the ability to manipulate and move around with and navigate and explore, in the best sense of the word, the everyday physical world.

Murray Shanahan：是的，它完全关乎语言和推理，而不关乎进化在我们和其他动物身上——在语言出现之前——就已经发展出的那类东西，对吧？也就是在最真实的意义上操纵、移动、导航和探索日常物理世界的能力。

Embodiment and Intelligence

具身性与智能

[16:48:12 - 18:12:24]

TLDR: Murray emphasizes that human intelligence is grounded in our physical experience, noting that even our language relies heavily on spatial metaphors derived from our embodied existence.

摘要：Murray 强调人类智能根植于我们的身体经验，指出甚至我们的语言也在很大程度上依赖源自我们具身存在的空间隐喻。

Hannah Fry: So actually, that's really interesting. That's so interesting because I often think about how fine, maybe the large language models we have at the moment can pass the Turing test, but they don't flinch if you throw a ball at your computer. No, indeed. And in a sense, there are these sort of, as you say, these much deeper forms, maybe we wouldn't class them as intelligence in the way that we talk about it. But ultimately they sort of is a form of intelligence too.

Hannah Fry：这真的很有趣。太有趣了，因为我经常想，好吧，也许我们目前的大语言模型可以通过图灵测试，但如果你向你的电脑扔一个球，它们不会退缩。是的，确实不会。在某种意义上，正如你所说，存在这些更深层次的形式，也许我们不会按我们谈论智能的方式来归类它们，但归根结底它们也是一种智能形式。

Murray Shanahan: Well, I think very much is a form of intelligence. And moreover, I think that in the biological case, so now I have to caveat all these things by saying in the biological case, our ability to think and to reason and to talk is very much grounded in our interaction with the everyday world. If you think about almost all of your everyday speech is using spatial metaphors. I mean, they completely permeate our everyday speech. Even the word permeate. Grounded, I use the word grounded there, So, we just use those kind of things all the time.

Murray Shanahan：我认为很大程度上就是一种智能形式。而且，我认为在生物案例中——现在我必须对这些说法加上限定，说“在生物案例中”——我们思考、推理和说话的能力很大程度上根植于我们与日常世界的互动。你想想，你几乎所有的日常语言都使用空间隐喻。它们完全渗透在我们的日常语言中。就连“渗透”这个词也是。我用过“根植于”（grounded）这个词。我们无时无刻不在使用这些东西。

Hannah Fry: Because we're fundamentally physical beings.

Hannah Fry：因为我们从根本上就是有物理实体的存在。

Murray Shanahan: Because we're fundamentally physical beings and because our brains have evolved to help us to navigate and survive and reproduce, in this physical world. And while interacting with all these other beings that are doing the same thing, right?

Murray Shanahan：因为我们从根本上就是有物理实体的存在，因为我们的大脑进化为帮助我们在物理世界中导航、生存和繁殖，同时与所有其他在做同样事情的生物互动，对吧？

Alternative Testing Methods

替代测试方法

[18:12:24 - 19:59:38]

TLDR: Murray discusses the "Garland test" from Ex Machina, which focuses on whether a machine can be recognized as conscious even when we know it's artificial - a different measure than the Turing test's focus on intelligence.

摘要：Murray 讨论了《机械姬》中的“Garland 测试”，该测试关注的是，即使我们知道机器是人造的，我们是否仍会认为它是有意识的——这与图灵测试关注智能是不同的衡量标准。

Hannah Fry: Because there are some alternatives. When you are trying to test for the capability of an artificial intelligence, just talk me through some of the potential alternatives that we have.

Hannah Fry：因为还有其他替代方案。当你试图测试人工智能能力时，跟我们说说一些潜在的替代方案。

Murray Shanahan: Well, I think perhaps you've got in mind the Garland test. What I call the Garland test, which is so that goes back to the film Ex Machina, which was directed by Alex Garland, of course. And there's a bit in the script where Nathan, the billionaire guy, is talking to Caleb, who's the guy who's been brought in to interact with Ava the robot. And Caleb says, oh, I'm here to kind of conduct a Turing test on Ava. And Nathan says, oh, no, we're way past that. Ava could pass the Turing test easily. The point is to show you she's a robot and see if you still think she's conscious. Wow. And that's what I call the Garland test and it's different from the Turing test in two respects. So first of all, the sort of judge, as it were, which in that case is, Caleb, can see that she's a robot. So in the Turing test, the judge can't see which is which. But here, the idea is that Caleb sees, knows that she's a robot. And yet still attributes these characteristics. And yet still, yeah, and the characteristic in question also is different because it's not intelligence, it's not, can she think, but is she conscious? Or is it conscious? is she, and, which is an entirely different test? And I think, intelligence and consciousness are different things and we can disentangle those two things, dissociate them. So when I first read the script of the film, and those particular lines were in there for Caleb and Nathan, I wrote next to it in my version, Spot on! with an exclamation mark because I just thought Alex had totally nailed a really important idea there.

Murray Shanahan：我想你心里想的可能是 Garland 测试。我称之为 Garland 测试，它回溯到 Alex Garland 导演的电影《机械姬》。剧本中有一段，亿万富翁 Nathan 在对被带来与机器人 Ava 互动的 Caleb 说话。Caleb 说，哦，我是来这里对 Ava 做图灵测试的。Nathan 说，哦不，我们早过了那个阶段了。Ava 可以轻松通过图灵测试。重点是让你看看她是个机器人，然后看看你是否仍然觉得她有意识。哇。这就是我所说的 Garland 测试，它在两个方面与图灵测试不同。首先，裁判——在那个例子中是 Caleb——可以看到她是个机器人。在图灵测试中，裁判看不到哪个是哪个。但这里，Caleb 看见、知道她是个机器人，却仍然赋予她这些特征。其次，所讨论的特征也不同，因为它不是智能，不是“她能思考吗”，而是“她有意识吗？”或者说“它是有意识的吗？”这是一个完全不同的测试。我认为智能和意识是不同的东西，我们可以把这两者分离开来。当我第一次读到电影剧本，看到 Caleb 和 Nathan 之间那些台词，我在我的版本旁边写道：“完全正确！”还加了个感叹号，因为我觉得 Alex 真的精准地抓住了那个非常重要的思想。

Murray Shanahan: And so in my writing, I call this the Garland test, and quite a few people have picked up on that and call it the Garland test as well.

Murray Shanahan：所以在我的写作中，我把它称为 Garland 测试，不少人也采纳了这一说法，也称之为 Garland 测试。

Abstract Reasoning Tests

抽象推理测试

[19:59:38 - 21:56:41]

TLDR: Murray describes Francois Chollet's ARC (Abstract Reasoning Corpus) tests, which evaluate pattern recognition abilities through visual puzzles. While initially impressive, he notes that brute-force approaches have begun to solve these challenges.

摘要：Murray 描述了 François Chollet 的 ARC（抽象推理语料库）测试，通过视觉谜题评估模式识别能力。虽然最初令人印象深刻，但他指出暴力穷举方法已经开始攻克这些挑战。

Hannah Fry: Is there a test that would really impress you if an AI were able of passing it?

Hannah Fry：有没有什么测试，如果 AI 能通过，会让你真正印象深刻？

Murray Shanahan: So I always was impressed by Francois Chollet's ARC tests. And that's ARC, which stands for Abstract Reasoning Corpus. So these are little sequences of images of the sort that you get in IQ tests and things. And the images are arranged in pairs. So you have the first image, it's kind of pixelated image, it's got little cells with little kind of things that you can interpret as objects or lines and so on in the images. And you're interested in the challenge is to work out a rule that takes you from one image to the second one. Then you've got to apply that rule to a third image. First of all, the held out are made completely secret. All of the test ones. So you couldn't game it by kind of knowing what the actual test versions were. Or using it in a training set. Or using it in the training set. That's what that's sort of what I mean, by gaming it. And also he very carefully designed them so that it was very different rules each time. Each rule, was completely different to the other rules. And you usually have to find some kind of intuitive application of, often our everyday common-sense knowledge, seeing this as like a liquid that's moving in this direction or imagining this thing moving, growing or something.

Murray Shanahan：我一直对 François Chollet 的 ARC 测试印象深刻。ARC 代表抽象推理语料库（Abstract Reasoning Corpus）。这些是类似于智商测试中的那种小图像序列。图像成对排列。你有第一张图像，是一种像素化图像，有带小格子的小单元，你可以在图像中解读为物体或线条等。挑战在于找出一个规则，把第一张图像变成第二张。然后你需要把这个规则应用到第三张图像上。首先，测试用的图像完全保密。所有测试集都是保密的，所以你无法通过事先知道测试版本来作弊。也无法用在训练集中。其次，他非常精心地设计它们，每次规则都完全不同。每个规则与其他规则都完全不同。你通常需要找到某种直觉性的应用，往往是我们的日常常识知识——把这个看作是一种液体朝这个方向移动，或者想象这个东西在移动、生长之类的。

Hannah Fry: So it required grounding in a way.

Hannah Fry：所以在某种程度上它需要“根植于”。

Murray Shanahan: Well, it seemed to, but, recently, people have been able to make significant progress on these in a more brute force kind of way. So, I don't feel that the solutions are not really, getting at the spirit of the original test quite so much.

Murray Shanahan：似乎是如此。但最近，人们已经能够以更暴力穷举的方式在这些测试上取得显著进展。所以我觉得这些解法并没有真正触及原始测试的精神本质。

Hannah Fry: Well, that's it, I guess, in a way is that as soon as you set a metric, as soon as you set a bar of once we've crossed this threshold, then we will have capability, intelligence, consciousness, whatever it might be. It sort of changes the whole nature of the test in itself.

Hannah Fry：是啊，我想这就是问题所在，一旦你设定了一个度量标准，一旦你设定了一条“一旦跨过这个门槛，我们就有了能力、智能、意识，不管是什么”的线，这本身就会改变测试的整个性质。

Murray Shanahan: Yeah, or people are going to start, gaming the test, right? It's Goodhart's law, right? So, absolutely.

Murray Shanahan：是的，否则人们就会开始作弊，对吧？这就是古德哈特定律，对吧？绝对的。

Anthropomorphization and AI Understanding

拟人化与人工智能的理解

[21:56:41 - 26:36:42]

TLDR: Murray discusses the nuances of anthropomorphizing AI systems, suggesting that some forms may be appropriate while others can lead to misunderstandings about AI capabilities.

摘要：Murray 讨论了将人工智能系统拟人化的细微差别，暗示某些形式的拟人化可能是合适的，而其他形式则可能导致对人工智能能力的误解。

Hannah Fry: A lot of people who come on this podcast have sort of expressed real need for caution about anthropomorphizing these things. Are you one of those people who thinks that we shouldn't?

Hannah Fry：很多来本节目的人都表达了需要对拟人化这些事物保持警惕。你是那些认为我们不应该这样做的人之一吗？

Murray Shanahan: Well, I think there are different ways of looking at this and I think there are sorts of good and bad forms of anthropomorphization. So, on the one hand, people can start to form relationships as they see it with AI systems, friendships, and companionships and mentorships. And that can potentially be a bad thing if they are misled into thinking that things have capabilities that they don't really have. So I think that's where it becomes problematic. So you say, the Encyclopedia Britannica, right? The physical volume of the Encyclopedia Britannica doesn't know that Argentina won the World Cup in this, because it's too old. So if you made that remark, it would make perfect sense, you might say that and it's fine. But if somebody said to you, why don't you have a conversation with it about England's football prowess, or lack thereof, that would be ridiculous, right? Now, the interesting thing is that now we've got these large language models, you can have a conversation with them about, you can tell it things, and you can so that it kind of pushes the boundary of where we might start to say, well, it doesn't really X Y Z, it pushes that a little bit further out.

Murray Shanahan：嗯，我认为有不同的角度来看待这个问题，我认为有好和坏的拟人化形式。一方面，人们可能开始与人工智能系统建立——在他们看来——关系、友谊、陪伴和师徒关系。如果他们被误导，以为这些东西具有它们实际上并不具有的能力，那就可能是一件坏事。所以我认为这就是它会有问题的地方。比如，你说《大英百科全书》——那本实体的《大英百科全书》不知道阿根廷赢了某年的世界杯，因为它太旧了。如果你这么说，这话完全说得通，你可以这么说，这没问题。但如果有人对你说，你为什么不跟它聊聊英格兰的足球实力（或者说缺乏实力）呢？那就很荒谬了，对吧？有趣的是，现在我们有了大语言模型，你可以跟它们聊天，你可以告诉它们东西，这样就把我们可以说“它不是真的如何如何”的边界又向外推了一点点。

Hannah Fry: I wonder if there's something even deeper here about this human need or maybe it's just a desire to really want AI to have these characteristics to be anthropomorphized.

Hannah Fry：我想知道这里是否有某种更深层的东西，关于人类需要——或者也许只是渴望——真正希望人工智能拥有这些特征，希望它被拟人化。

Murray Shanahan: Yeah, yeah. Well, that's a really interesting question, isn't it? So, I don't think it kind of comes back to that. It comes back to language, in this case, we're inclined to anthropomorphize things because they're really good at using language. And for us, the only things that are good at using language are other humans. And so it's very strange in a way to be suddenly in a world where we have language using things that, it's not just humans that can talk. That's astonishing. Yeah. I mean, it is astonishing. It is astonishing. I mean, it's really is astonishing to think that every single child born today, they're going to grow up in a world where they've never known a world in which machines can't talk to them. Isn't that an extraordinary thing? Yeah. I mean, it really is. And so what the implications are of that for us all is really hard to say.

Murray Shanahan：是的，是的。嗯，这是一个很有趣的问题，不是吗？所以，我觉得这又回到了语言上。在这种情况下，我们之所以倾向于拟人化事物，是因为它们非常擅长使用语言。而对我们来说，唯一擅长使用语言的东西就是其他人类。所以，突然生活在一个拥有会使用语言的非人类事物的世界里，这在某种程度上非常奇怪。不仅仅是人类会说话。这令人震惊。确实令人震惊。想想看，今天出生的每一个孩子，他们成长的世界将是一个机器不能与他们交谈的世界从未存在过的世界。这不是一件非同寻常的事吗？是的，确实如此。这会对我们所有人产生什么影响，真的很难说。

Embodiment, Consciousness, and Future AI

具身性、意识与未来的人工智能

[26:36:42 - 38:12:34]

The Importance of Embodiment

具身性的重要性

[26:36:42 - 28:09:05]

TLDR: Murray discusses how embodied AI might lead to deeper forms of intelligence, suggesting that physical interaction with the world may be necessary for certain types of understanding that current language models lack.

摘要：Murray 讨论了具身人工智能如何可能导致更深层次的智能形式，暗示与物理世界的互动可能是当前的纯语言模型所缺乏的某些类型的理解所必需的。

Hannah Fry: Just thinking back to what you were saying about how grounded humans are in the physical world.

Hannah Fry：回想一下你之前说的，人类是多么根植于物理世界。

Murray Shanahan: Yes.

Murray Shanahan：是的。

Hannah Fry: It does feel like the kind of embodied aspect of AI has lagged behind this language aspect quite a bit.

Hannah Fry：确实感觉人工智能的具身方面远远落后于语言方面。

Murray Shanahan: Yeah.

Murray Shanahan：是的。

Hannah Fry: Do you think that we're going to see a big up step in intelligence, however you want to define it, or broader capabilities once we get good and effective embodied AI?

Hannah Fry：你认为一旦我们有了好的、有效的具身人工智能，我们会在智能——无论你怎么定义它——或更广泛的能力上看到一个大的飞跃吗？

Murray Shanahan: Well, I think it might make a big difference because the large language models that we have at the moment, it's really difficult to discern actually, to be honest, right now, where the limits are for how good they're going to get. Whether we really are on the road to producing, general intelligence that's comparable to human general intelligence. And often, when you get to the boundaries of the capabilities of these kinds of things, you sort of get, sometimes you get the impression that the AI system doesn't really quite grok, something. It doesn't really deeply understand something. You reach some kind of limit and you realize that it's been faking it a little bit. But it may be that that sort of general ability to really kind of get things on a deep level or on a deep, kind of common-sense level maybe, that does still require a bit of embodiment. It does still basically require training data that involves, interacting with a real world of physical objects with their spatial organization. And there's something fundamental about that.

Murray Shanahan：嗯，我认为这可能会产生很大的不同。因为我们目前的大语言模型，说实话，现在真的很难辨别它们会变得有多好。我们是否真的走在通往产生可媲美人类通用智能的通用人工智能的道路上。通常，当你接近这些东西能力的边界时，你会——有时你会得到一种印象，即人工智能系统并没有真正“领会”某些东西。它并没有真正深刻地理解某些东西。你达到了某种极限，你意识到它一直在某种程度上假装。但也许那种真正在深层次或深刻的常识层面上理解事物的通用能力，仍然需要一定的具身性。它仍然基本上需要包含与真实物体及其空间组织的物理世界互动的训练数据，而这里面有某种根本性的东西。

The Question of AI Consciousness

人工智能意识的问题

[28:09:05 - 31:13:17]

TLDR: Murray breaks down consciousness into multiple facets (awareness of the world, self-awareness, metacognition, and sentience), arguing that these aspects can be separated and that modern AI systems might exhibit some but not all components.

摘要：Murray 将意识分解为多个维度（对世界的觉察、自我意识、元认知和感受能力），认为这些方面可以分离，现代人工智能系统可能表现出其中一些而非全部组成部分。

Hannah Fry: Okay. If understanding then, however we define it, is something that can emerge as a consequence of more and more data. What about consciousness? I mean, I'm sure you've been asked a thousand times about AI consciousness and whether it's something that we can expect to happen or has already happened.

Hannah Fry：好的。那么理解——无论我们怎么定义它——是否可以随着越来越多的数据而涌现？意识呢？我敢肯定你被问过一千次关于人工智能意识的问题，以及它是不是我们可以期待会发生、或者已经发生的事。

Murray Shanahan: Yeah, yeah. Well, the very first thing to point out is that I do think we can dissociate, intelligence or cognition and cognitive capabilities. We can dissociate that from consciousness. So I think we can imagine things that are very capable and have, that we want to say are very intelligent because of the way they can achieve their goals and so on, but that we don't want to ascribe consciousness to. But actually, what does that even mean – to ascribe consciousness to something at all? And I think the concept of consciousness itself, can be broken down into many parts. It's a multi-faceted concept. So, for example, we might talk about awareness of the world. And many in the scientific study of consciousness, there are all of these experimental protocols and paradigms. And many of them are to do with perception, and you're looking at whether a person is aware of something is consciously perceiving something in the world. Large language models are not aware of the world at all in that respect. But there are other facets of consciousness. We also have self-awareness. And our self-awareness, part of that is awareness of our own body and where it is in space. But another aspect of self-awareness is a kind of awareness of our own inner machinations or of our stream of consciousness, as William James called it. So we have that kind of self-awareness as well. And we have what some people call metacognition as well. We have the ability to think about what we know. And then, additionally, there's the emotional side or the feeling side of consciousness or sentience. So the capacity to feel, the capacity to suffer. And that's another aspect of consciousness. Now, I think we can dissociate all of these things. Now, in humans, they all come as a big package, a big bundle. But you only actually have to think about non-human animals to realize that we can kind of start to separate these things a little bit because I think that much as I love cats, I think there's a limited self-awareness going on in cats. How dare you? Well, I'm a big cat person. I have to say, so I do say that with some hesitation and, There's little metacognition, should we say? Well, yeah, certainly they don't have an awareness of their own ongoing stream of verbal consciousness because they don't have it. So they're not thinking about what they did yesterday in verbal terms or what they want to do with their lives. So if we think about like robots, you may have a very sophisticated robot, even your robot vacuum cleaner, and you may say that it's, well, it does actually have a kind of awareness of the world. And that's not an inappropriate use of that phrase awareness of the world. Do I want to call it consciousness? Well, then I seem to be bringing on board all of this other stuff as well. But you don't have to. You can break down the concept of consciousness into these different aspects.

Murray Shanahan：是的，是的。首先要指出的是，我确实认为我们可以将智能或认知与认知能力，与意识解耦。所以我认为我们可以想象出非常有能力的、我们想称之为非常智能的东西——因为它们实现目标的方式等等——但我们不想赋予它们意识。但实际上，给某物赋予意识到底意味着什么？而且我认为意识这个概念本身可以分解成很多部分。它是一个多维度的概念。比如，我们可以谈论对世界的觉察。在意识的科学研究中，有各种各样的实验范式和协议，很多都与感知有关，你在观察一个人是否觉察到某物，是否在有意识地感知世界中的某物。大语言模型在这方面完全不具有对世界的觉察。但意识还有其他的维度。我们也有自我意识。我们的自我意识中，一部分是对自己身体及其在空间中位置的觉察。但自我意识的另一个方面是对我们自己内在运作或意识流的觉察，正如威廉·詹姆斯所称的那样。所以我们也有那种自我意识。我们还有某些人所说的元认知。我们有能力思考我们所知道的事情。此外，还有意识的情感面或感受面，即感受能力——感受的能力、承受痛苦的能力。这是意识的另一个方面。现在，我认为我们可以把所有这些都解耦。在人类身上，它们是一个大包裹、一个大捆绑包。但只要你想到非人类动物，你就会意识到我们可以在某种程度上开始把这些东西分离开，因为我认为，尽管我很爱猫，我认为猫身上的自我意识是有限的。你怎么敢这样说？我其实是个爱猫之人，我带着一些犹豫这么说。说它元认知很少，可以这么说吧？嗯，肯定的，它们没有对自己正在进行的语言意识流的觉察，因为它们根本没有。所以它们不是在用语言思考昨天做了什么，或者它们的人生想做什么。所以如果我们思考机器人，你可能有一个非常精密的机器人，甚至是你的扫地机器人，你可能会说它确实对世界有一种觉察。并且这种“对世界的觉察”的说法并非不当。那我是否想称它为意识呢？好吧，那样我似乎就把所有其他东西也带进来了。但你不必如此。你可以把意识这个概念分解成这些不同的方面。

Shared Worlds and Consciousness

共享世界与意识

[31:13:17 - 35:31:27]

TLDR: Murray argues that consciousness is most meaningfully discussed in the context of shared physical experiences. He uses the example of octopuses to show how our concept of consciousness evolves as we interact with different entities.

摘要：Murray 认为，在共享物理体验的语境下讨论意识才最有意义。他用章鱼的例子说明，当我们与不同实体互动时，我们对意识的概念会如何演变。

Hannah Fry: Because your robot vacuum can know exactly where it is in a space and how and respond in a, in an intelligent and sensitive way to where it is and the objects around it and achieve its ends and so on. So there's a kind of awareness of the world there. I don't [think] there's no self-awareness. There's certainly no capacity for suffering. And so in a large language model, there might not be awareness of the world in that perceptual sense. But maybe there's some kind of sort of self-awareness or reflexive capabilities, reflexive cognitive capabilities, they can talk about the things that they've talked about earlier in the conversation, for example, and can do so in a reflective manner, which kind of feels a little bit like some aspects of self-awareness that we have a little bit. I don't think that it's appropriate to think of them in terms of having feelings. They can't experience pain because they don't have a body. I think we can take the concept apart, basically.

Hannah Fry：因为你的扫地机器人可以精确地知道自己在空间中的位置，并对其位置和周围物体做出智能且灵敏的反应，并实现其目标等等。所以那里有一种对世界的觉察。我不认为有任何自我意识。当然也没有承受痛苦的能力。而在大语言模型中，可能没有那种感知意义上的对世界的觉察。但也许有某种自我意识或自反能力，自反认知能力——它们可以谈论对话中早先谈论过的东西，并且可以以反思的方式进行，这在某种程度上感觉有点像我们拥有的自我意识的某些方面。我不认为把它们视为拥有感受是恰当的。它们无法体验痛苦，因为它们没有身体。我认为我们基本上可以把这个概念拆解开来。

Hannah Fry: So then is the question, can AI be conscious or not, as though it's a binary thing? It's the wrong question from the [start].

Hannah Fry：那么，“人工智能是否能有意识”这个问题，好像它是个二元的——这从一开始就是一个错误的问题。

Murray Shanahan: I do think that is the wrong question. And I think it's wrong in many ways. So, just then we were talking about the fact that it's actually a sort of multi-faceted concept. But also, I think that we tend to have these very deep metaphysical commitments to the idea of consciousness as some, sort of magical thing that is, a metaphysical thing. So the question of whether something is conscious or not is not a matter of consensus or a matter of just our language, but it's something that is out there in the metaphysical reality or in the mind of God or in the Platonic heaven or something like that. But ultimately, I do think that that's the wrong way of thinking about consciousness.

Murray Shanahan：我确实认为那是一个错误的问题，而且我认为它在很多方面是错的。刚才我们谈到了它其实是一个多维度的概念。但我也认为，我们往往对意识这个观念有一种非常深刻的形而上学承诺，把它当作某种魔法般的东西，一种形而上学的东西。所以，某物是否有意识，不是一个共识问题，也不仅仅是我们的语言问题，而是一种存在于形而上学现实中的东西，或存在于上帝的心智中，或存在于柏拉图的天国之中。但最终，我确实认为那是思考意识的错误方式。

Hannah Fry: Let's take one aspect of consciousness then that you described about the sort of emotional side. The ability to suffer, but not necessarily physical pain, emotional pain too. And sort of a sense of self in the emotional way. Do you think this is something that will just emerge as a natural consequence of intelligence? That if you build something that is intelligent enough, at some point this is going to happen? Or is there something unique about biological creatures and I guess the process of evolution that we've been through that has resulted in that that can't be replicated in a machine?

Hannah Fry：那么，就拿你描述的意识的一个方面来说，关于情感的那一面。承受痛苦的能力，但不一定是身体疼痛，情感痛苦也算。以及一种情感意义上的自我感。你认为这是否会作为智能的自然结果而涌现？如果你构建了足够智能的东西，某个时刻这就会发生？还是说生物体以及我们所经历过的进化过程中有某种独特的东西，这种独特的东西产生了那种能力，而这无法在机器中复制？

Murray Shanahan: I don't think there is a right or wrong answer to your question there. I think we just have to wait and see what things we bring into the world and how we end up treating them and talking about them and thinking about them. And I don't think we really know until they're among us as it were, these things that we're building. And then we will just be led to think about them and talk about them and treat them in a particular way. So an example I like to think of in this regard is the octopus. So, octopuses have recently been brought into, UK legislation, brought into the category of things that we have to care about the welfare of. That's as a result of lots of things, I think, happening. So the public has been exposed to being with octopuses a lot more. Now, you don't have to literally be under the water and poking around with octopuses to know what it's like to be with them, because there's all kinds of wonderful documentaries and wonderful books by like Peter Godfrey Smith has these great books about interacting with octopuses and so on. And so those sort of narratives and documentaries, they give us a feel for what it's like to be with an octopus, what it's like to have an encounter with an octopus. And then, you can't sort of can't help yourself but to see it as a fellow conscious creature. But complementing that is the scientific progress as well. So at the same time, scientists study the nervous systems of octopuses and, realize the extent to which their nervous systems are similar to ours and the way that when we experience pain, you can find analogous, aspects of their nervous systems to ours. So taking all these things together, I think that tends to affect the way we think about them and the way we talk about them and the way we treat them. So I think the same kind of thing will, is going to happen with AI systems. Do I think there's a right or wrong answer to could we be misled there? I think that's a really, really deep and difficult metaphysical, philosophical question.

Murray Shanahan：我不认为你的问题有一个正确或错误的答案。我认为我们只能等着看我们会把什么样的东西带到这个世界上，以及我们最终会如何对待它们、谈论它们、思考它们。我认为在这些我们正在建造的东西真正“来到我们中间”之前，我们并不会真正知道。然后我们才会以特定的方式被引导去思考它们、谈论它们、对待它们。在这方面我喜欢举的一个例子是章鱼。章鱼最近被纳入了英国立法，被纳入我们必须关心其福利的范畴。这是很多事情共同发生的结果，我认为。公众接触章鱼的机会大大增加了。你不必真的在水下和章鱼打交道就能知道和它们相处的感受，因为有各种精彩的纪录片，还有像 Peter Godfrey-Smith 写的关于与章鱼互动的精彩书籍。所以这些叙事和纪录片，给了我们一种与章鱼相处、与章鱼相遇的感受。然后你忍不住把它看作是一个有意识的同伴生物。但与此相辅相成的是科学的进步。同时，科学家研究章鱼的神经系统，意识到它们的神经系统与我们的相似程度，以及当我们经历疼痛时，可以在它们的神经系统中找到与我们类似的方面。所以把这些综合起来看，我认为这倾向于影响我们对它们的看法、谈论它们的方式和对待它们的方式。所以我认为同样的事情也会发生在人工智能系统上。我是否认为“我们是否可能在这方面被误导”有一个正确或错误的答案？我认为那是一个非常非常深刻且困难的形而上学、哲学问题。

Ethical Considerations of AI Suffering

人工智能痛苦的伦理学考量

[35:31:27 - 38:12:34]

TLDR: Murray emphasizes the ethical importance of considering potential AI suffering, noting that we should be cautious about creating entities capable of suffering. He suggests that current systems likely don't have this capacity.

摘要：Murray 强调了考虑潜在的人工智能痛苦在伦理上的重要性，指出我们应当谨慎对待创造具有受苦能力的实体。他认为目前的系统很可能不具备这种能力。

Hannah Fry: I do wonder, though, that that point about suffering to me seems different to the others because metacognition, the sort of sense of the world, etc. There's not these ethical implications necessarily about those. But I think with suffering, like, you wouldn't want your shoes to be conscious. You know? You wouldn't want a forklift truck to be sort of conscious.

Hannah Fry：不过我确实觉得，关于痛苦那一点，在我看来与其他方面不同。因为元认知、对世界的感知等等，这些不一定有那些伦理含义。但对于痛苦，你大概不会希望你的鞋子有意识，你知道吗？你不会希望一辆叉车有意识。

Murray Shanahan: Unless they happen to really like being a forklift truck. Sure. Sure.

Murray Shanahan：除非它们碰巧真的很喜欢当一辆叉车。当然，当然。

Hannah Fry: But then do we have to be a tiny bit more careful about that particular aspect?

Hannah Fry：但那个特定方面，我们是不是需要稍微更加小心一些？

Murray Shanahan: Absolutely. Yes, we do. If there were the prospect of bringing into being something that is genuinely capable of suffering, then we should think very hard about whether we should do it or not. I tend to think that that's not the case with anything that we've got at the moment. But, some people will push back against that.

Murray Shanahan：绝对的。是的，我们需要。如果有可能创造出真正能够承受痛苦的东西，那么我们应该非常认真地思考是否应该这样做。我倾向于认为，目前我们拥有的任何东西都不属于这种情况。但，有些人会对此提出反驳。

Murray Shanahan: If we take the example of large language models, well, okay, so there's one level in which what they do is next token prediction, next word prediction. But in order to be able to do that, really, really, really well in the way that they can at the moment, then all they've had to learn, and acquire all kinds of emergent mechanisms. So who knows whether or not there's some kind of emergent mechanism has been learned in the weights of this huge, staggering number, hundreds of billions of weights in a language model. Whether some mechanism isn't hasn't been learned there that, has, for example, genuine understanding in it, whatever that means, or even consciousness.

Murray Shanahan：以大语言模型为例。好吧，在某个层面上，它们所做的是下一个 token 预测、下一个词预测。但为了能够非常好地做到这一点，像现在这样好，它们不得不学习并获得各种涌现机制。所以谁知道呢——在这庞大的、惊人数量——数千亿个权重的语言模型中，是否有什么涌现机制已经被学习到了这些权重里。那里是否已经学到了某种机制，具备——例如——真正的理解，不管那意味着什么，甚至是意识。

Murray Shanahan: Coming back to embodiment again, I've always been of the view that it's only really legitimate to talk about consciousness in the context of something we can share a world with and have that kind of encounter with that we have with an octopus or a dog or a horse or whatever, and being together in the world with that animal and responding to things together. Then I'm in no doubt that they are conscious. That's a kind of primal case for me. Now with a large language model, you can't be in the same world as them in that kind of way, and you can't hang out with them and interact with physical objects with today's large language models, right? So, to my mind, using the language of consciousness in that context is, well, [Wittgenstein] would say it's taking language on holiday. It's using it out so far outside of its normal use, maybe it's inappropriate. But that can change, and the more I interact with large language models, the more I have these sophisticated and interesting conversations with them, the more I'm inclined to think, well, maybe I want to extend the language of consciousness, bend it, change it, distort it, make up some new words, break it apart in ways that are going to fit these new things that I'm interacting with all the time.

Murray Shanahan：再回到具身性。我一直以来的观点是，只有在我们可以共享一个世界、并且拥有像我们与章鱼或狗或马或其他动物相处时的那种相遇的语境下，谈论意识才是真正合理的——在同一个世界里与那个动物共处，一起对事物做出反应。在这种情况下，我毫不怀疑它们是有意识的。对我来说，那是一种原初案例。现在，对于大语言模型，你不能以那种方式与它们在同一个世界里，你不能和它们一起闲逛并一起互动物理对象——用今天的大语言模型来说，对吧？所以在我看来，在那个语境下使用意识的语言，嗯，[维特根斯坦]会说这是把语言带出去度假了。把它用得太远超出其正常用途了，也许是不合适的。但这可以改变。我越与大语言模型互动，越与它们进行这些复杂而有趣的对话，我就越倾向于想，也许我想扩展意识的语言，弯折它，改变它，扭曲它，造一些新词，以适合这些我一直在与之互动的新事物。

Interacting with AI and Future Conceptualization

与人工智能互动及未来的概念化

[38:12:34 - 41:43:06]

Tips for AI Interaction

人工智能交互技巧

[38:12:34 - 39:37:15]

TLDR: Murray advises treating AI systems politely and conversationally - as if they were human - to get better results, noting that being polite to an AI may improve its responses.

摘要：Murray 建议礼貌地、以对话方式对待人工智能系统——当作人类——以获得更好的结果，并指出对人工智能保持礼貌可能会提升其回应质量。

Hannah Fry: I know you've spent a lot of time interacting with these large language models. I've actually seen you described as a renowned prompt whisperer. What's your secrets?

Hannah Fry：我知道你花了很多时间与这些大语言模型互动。我其实看到过你被称为“著名的提示词耳语者”。你的秘诀是什么？

Murray Shanahan: Well, one secret is to talk to the large language model as if it were human. So if you think that what they're doing is role playing a human character, such as, say, a very smart and helpful intern, then you should treat them like a smart and helpful intern, and talk to them as if they were a smart and helpful intern. For example, just being polite and saying, is that clear? And please and thank you. And in my experience, you get better responses out of things if you do things that way.

Murray Shanahan：嗯，一个秘诀是像对待人类一样与大语言模型交谈。如果你认为它们在做的事情是角色扮演一个人类角色，比如说一个非常聪明、乐于助人的实习生，那么你就应该像对待一个聪明、乐于助人的实习生一样对待它们，像对聪明、乐于助人的实习生那样跟它们说话。比如，保持礼貌，说“这样清楚吗？”，说“请”和“谢谢”。根据我的经验，这样做你会得到更好的回应。

Hannah Fry: Do you say please and thank you?

Hannah Fry：你会说“请”和“谢谢”吗？

Murray Shanahan: You can say please and thank you. Yeah. Now there's a good scientific reason why that might get, it just depends, and models are changing all the time. Why that might get better performance out of it because if it's role-playing, say it's role-playing a very smart intern, right? Then they might then it's going to just role-play maybe you being a bit more stroppy if they don't if they're not being treated politely. It's, it's just mimicking what humans would do, in that scenario. So the mimicry might extend to kind of being a bit more, not being as responsive if their boss is a bit of a stroppy bossy boss.

Murray Shanahan：你可以说“请”和“谢谢”。是的。有一个很好的科学理由说明为什么这可能带来更好的表现：因为这取决于——而且模型在不断变化——如果它在角色扮演，比如在角色扮演一个非常聪明的实习生，对吧？那么，如果它没有被礼貌对待，它可能就会角色扮演一个你有点暴躁的样子。它只是在模仿人类在那个场景下会做的事情。所以，如果老板是个有点暴躁、专横的老板，这种模仿可能延伸到不那么积极配合。

Reconceptualizing AI

重新概念化人工智能

[39:37:15 - 41:43:06]

TLDR: Murray proposes thinking of modern AI systems as "exotic mind-like entities" - something with mind-like qualities that differs significantly from human minds in how they exist and operate.

摘要：Murray 提议将现代人工智能系统视为“异类的类心智实体”——一种具有类心智特质但在存在和运作方式上与人类心智显著不同的存在。

Hannah Fry: I absolutely love that. I think I want to return to where we started, which is about how we think about AI and the language we use to describe it, and sort of how we frame it in our minds. Do you think that we need a new way of talking about AI? Both acknowledges its potential without overestimating it, but then similarly isn't dismissive of the things that it can do?

Hannah Fry：我非常喜欢这个说法。我想回到我们开始的地方，就是关于我们如何看待人工智能，我们用来描述它的语言，以及我们如何在心智中框定它。你认为我们需要一种谈论人工智能的新方式吗？既承认它的潜力而不高估它，同时又不贬低它所能做的事情？

Murray Shanahan: I think that's exactly what we need. In one of my papers, I used the phrase exotic mind-like entities to describe large language models. So I think that they are to a degree, exotic mind-like entities. Say it again. Exotic mind-like entities. Lovely. So they are, they are kind of mind-like, and they're increasingly mind-like. Now, there's a very important reason for using the little hyphen like there, which is because I want to hedge my bets as to whether they really qualify as minds. And so I can wiggle out of that problem by just using mind like. They're exotic because they're not like us. Language use, but in other respects, they're disembodied for a start. There's really weird conceptions of selfhood that are applicable to them, maybe. So they are quite exotic entities as well. So I think of them as, exotic mind-like entities. And we just don't have the right kind of conceptual framework and vocabulary for talking about these exotic mind-like entities yet. We're working on it. And the more they are around us, the more we'll develop new kinds of ways of talking and thinking about them.

Murray Shanahan：我认为这正是我们所需要的。在我的一篇论文中，我用了一个短语“exotic mind-like entities”——异类的类心智实体——来描述大语言模型。所以我认为它们在某种程度上是异类的类心智实体。你再说一遍。异类的类心智实体。真好。因为它们是一种类似心智的东西，而且越来越像心智。现在，使用那个小小的连字符“类”有一个非常重要的原因，那就是我想在这个问题上保留余地，不确定它们是否真的够格被称为心智。这样我可以通过用“类心智”来规避这个问题。它们是异类的，因为它们不像我们。在使用语言方面像，但在其他方面，首先它们没有实体。可能适用于它们的自我概念也非常奇怪。所以它们也是非常异类的实体。所以我将它们视为异类的类心智实体。而我们还没有正确的概念框架和词汇来谈论这些异类的类心智实体。我们正在努力。它们在我们身边越多，我们就越会发展出新的谈论和思考它们的方式。

Hannah Fry: It is interesting though that you are still going for the sort of the Turing-like approach of like a creature almost, rather than the tool idea.

Hannah Fry：不过有趣的是，你仍然倾向于那种图灵式的方法，更像是把它当作一个生物，而不是工具的概念。

Murray Shanahan: Well, entity is a pretty neutral term, isn't it? I suppose you could just say thing. Exotic mind like thing, if you prefer. Yeah, let's go with that. I think let's push for that for the new naming of it. Okay. Okay. But I mean, I can't Hannah because I've used the word entity in that context like in many publications now, so. Exotic mind like entities. I like it. I like it a lot. Murray, thank you so much for joining us.

Murray Shanahan：嗯，“实体”（entity）是一个相当中性的词，不是吗？我想你可以直接说“东西”。异类的类心智的东西（exotic mind-like thing），如果你更喜欢的话。好，就用这个。我觉得我们应该推动这个成为对它的新的命名。好的。好的。但是，我的意思是，我没办法，Hannah，因为我在那个语境下已经在很多出版物中用了“实体”这个词。异类的类心智实体。我喜欢，我非常喜欢。Murray，非常感谢你来参加我们的节目。

Murray Shanahan: It's been a pleasure, Hannah. Thank you.

Murray Shanahan：这是我的荣幸，Hannah。谢谢。

Conclusion

结论

[41:43:06 - 42:29:07]

TLDR: Hannah reflects on how AI experts' views have evolved over time, noting that physical embodiment and consciousness are being reconsidered as AI advances in unexpected ways.

摘要：Hannah 反思了人工智能专家们的观点如何随着时间推移而演变，指出随着人工智能以出人意料的方式进步，人们对物理具身性和意识的看法正在被重新审视。

Hannah Fry: One of the nice things about having done this podcast for a number of years is that you really get to see how the people at the frontier of AI, how their opinions change and shift over time. And the last few years have been a real game changer in all sorts of ways. About the extent to which intelligence requires a physical body. About how much we need to expand our definition of consciousness to account for the subtly different ways that these mind-like entities can operate. And the next few years, well, who knows? But if past predictions are any indication, the only thing we know about tomorrow's science and technology is that it will be radically different to what we imagine today.

Hannah Fry：做了这么多年播客，其中一件很美好的事情是，你真的能看到人工智能前沿的人们，他们的观点如何随着时间的推移而变化和转变。过去几年在各种方面都真正改变了游戏规则。关于智能在多大程度上需要物理身体。关于我们需要在多大程度上扩展我们对意识的定义，以涵盖这些类心智实体运作的微妙不同方式。而接下来几年呢，谁知道呢？但如果过去的预测有任何指示意义的话，关于明天的科学和技术，我们唯一知道的就是，它将与我们今天想象的截然不同。

Hannah Fry: You have been listening to Google Deep Mind the podcast with me, Professor Hannah Fry. If you enjoyed this episode, then do subscribe to our YouTube channel. You can also find us on your favourite podcast platform. And of course, we have plenty more episodes on a whole range of topics to come. So do check those out. See you next time.

Hannah Fry：你正在收听的是 Google DeepMind 播客，我是 Hannah Fry 教授。如果你喜欢本期节目，请订阅我们的 YouTube 频道。你也可以在你最喜欢的播客平台上找到我们。当然，我们还有很多关于各方面话题的节目即将推出，请一定去看看。下次再见。