Are We Thinking Correctly About AI Intelligence?

I’m Steve Strogatz.

我是史蒂夫·斯特罗加茨。

And I’m Janna Levin.

我是詹娜·莱文。

And this is The Joy of Why .

这就是“为什么的喜悦”。

A podcast from Quanta Magazine where we explore some of the biggest unanswered questions in math and science today.

来自《Quanta》杂志的播客，我们探讨了当今数学和科学中一些尚未解答的最大问题。

Well, hello, hello.

嗯，你好，你好。

This is unsurprisingly yet another show about AI.

毫不奇怪，这是又一个关于人工智能的节目。

I’m telling you, it’s a topic people can’t seem to get enough about, and I’m becoming reluctant to pontificate anymore.

我告诉你，这是一个人们似乎永远无法理解的话题，而我也变得不愿意再武断了。

It’s changing too quickly.

变化太快了。

It’s true.

这是真的。

It is moving very fast.

它的移动速度非常快。

Anything we say could be obsolete by next week.

我们所说的任何内容都可能在下周就过时了。

As we speak, it’s July 23rd, 2026.

As we speak, it’s July 23rd, 2026.

And it feels different to me than it did in July 23rd, 2025, that’s for sure.

对我来说，这与 2025 年 7 月 23 日的感觉不同，这是肯定的。

Mmm.

嗯。

That’s actually relevant, this talking about timelines, because our guest today, Melanie Mitchell, who is a cognitive scientist and computer scientist at Santa Fe Institute, is someone that we had on the show previously.

这实际上是相关的，这是关于时间线的，因为我们今天的嘉宾梅兰妮·米切尔（Melanie Mitchell）是圣达菲研究所的认知科学家和计算机科学家，她是我们之前在节目中出现过的人。

She and I spoke about five years ago, and that is before ChatGPT.

我和她谈过大约五年前，那是在 ChatGPT 之前。

Right.

正确的。

And was she interested in AI then?

那么她对人工智能感兴趣吗？

Okay, so it wasn’t just cognitive science.

Okay, so it wasn’t just cognitive science.

Absolutely.

绝对地。

I, I mean, yes, I should say Melanie has been thinking about AI for a long time, and she’ll tell us about that.

我，我的意思是，是的，我应该说梅兰妮长期以来一直在思考人工智能，她会告诉我们这一点。

But the thing that’s gonna be so interesting, I feel, for us to discuss today is, um, Melanie’s point of view, which is to think about the problem of AI from the standpoint of fields like developmental psychology.

但我觉得我们今天要讨论的非常有趣的是，嗯，梅兰妮的观点，即从发展心理学等领域的角度来思考人工智能的问题。

Like, how does a baby or a young child get to be as intelligent as they soon become?

例如，婴儿或幼儿如何才能变得如此聪明？

Oh, I think that’s so interesting ’cause we’re so excited about the artificial mind when we have very little comprehension of the human mind.

哦，我认为这很有趣，因为当我们对人类思维知之甚少时，我们对人工智能非常兴奋。

Right, so we’re trying to skip a step.

Right, so we’re trying to skip a step.

Well, that’s right.

嗯，没错。

And not just human mind, but also animal minds, right?

不仅是人类的思想，还有动物的思想，对吗？

So there’s the field of comparative psychology where we look at intelligence in birds or dogs or dolphins, whatever.

因此，在比较心理学领域，我们研究鸟类、狗或海豚等的智力。

Um, we have a lot to learn about thinking about intelligences other than our own adult human intelligence.

嗯，关于思考除了我们自己的成年人智力之外的智力，我们还有很多东西需要学习。

Yeah, and this idea that we’re going to somehow simply understand a mechanism to generate an artificial intelligence when we, again, don’t understand the mechanism that brings a baby to have its level of intelligence when it’s born or when it’s developing.

是的，当我们再次不了解婴儿出生或发育时达到其智力水平的机制时，我们将以某种方式简单地理解一种产生人工智能的机制。

I mean, I think that’s really interesting to combine those two.

我的意思是，我认为将两者结合起来真的很有趣。

So I’m looking forward to this one.

所以我很期待这个。

Well, great.

嗯，太好了。

So then let’s dive in with Melanie Mitchell.

那么让我们和梅兰妮·米切尔一起深入了解一下吧。

Here she is.

她在这里。

Very excited to see you again.

很高兴再次见到你。

This is gonna be fun.

这会很有趣。

We talked a few years ago back when this show was called The Joy of X , and I think you may be our first return champion.

几年前我们谈过，当时这个节目被称为“X 的欢乐”，我认为你可能是我们的第一个回归冠军。

Oh boy, I’m honored.

哦，孩子，我很荣幸。

Well, you should be.

嗯，你应该是的。

And, I have you back because so much feels like it’s changed in artificial intelligence.

而且，我让你回来了，因为人工智能已经改变了很多。

We talked, I think it was maybe 2021, and ChatGPT tidal wave hit the world at something like November of 2022.

我们聊过，我想可能是 2021 年，ChatGPT 浪潮大约在 2022 年 11 月席卷全球。

Is that right?

是这样吗？

So everybody knows that AI is everywhere.

So everybody knows that AI is everywhere.

We seem to be talking about it.

我们似乎正在谈论它。

People are worrying about it.

人们正在为此担心。

Some people are excited about it.

有些人对此感到兴奋。

It’s certainly very widely used.

它的使用确实非常广泛。

I suppose I’d like to start by asking, what has surprised you the most about the past few years?

我想我想首先问，过去几年最让你惊讶的是什么？

Oh, wow.

哦，哇。

So much has surprised me.

这么多让我感到惊讶。

Just the thought that we could get to where we are now just by training these models on huge amounts of human-generated language and images and so on.

只是想到，我们只需通过大量人类生成的语言和图像等来训练这些模型，就可以达到现在的水平。

I never would’ve dreamed it.

我从来没有梦想过。

So I’ve just been really surprised by what’s happened in AI.

所以我对人工智能领域发生的事情感到非常惊讶。

Also just the kind of polarized reaction that appeared in the AI community and society at large, I think, has been a little surprising to me, too.

我认为，人工智能社区和整个社会中出现的那种两极分化的反应也让我有点惊讶。

Polarized in terms of, like, sometimes people will distinguish AI doomers and AI optimists.

两极分化，比如，有时人们会区分人工智能厄运者和人工智能乐观主义者。

Is that the kind of thing you’re talking about?

你说的是这种事吗？

There’s that dimension, then there’s the dimension of people who believe that AI is smarter than humans and people who think that it’s far, far from being anywhere near human-like intelligence.

有这个维度，还有一些人相信人工智能比人类更聪明，还有一些人认为人工智能距离类人智能还很远很远。

I guess related to that is sort of the love-it and hate-it.

我想与此相关的是一种又爱又恨的感觉。

And these are separate dimensions, but maybe they’re correlated.

这些是不同的维度，但也许它们是相关的。

Well, and right, and the love-it and hate-it can be also tied to things like the impact on the environment versus, you know, the economic prosperity for certain companies, but then again, what about job loss?

好吧，对了，爱与恨也可能与对环境的影响以及某些公司的经济繁荣等因素联系在一起，但话又说回来，失业又如何呢？

There’s so many dimensions to this.

这有很多维度。

Oh, there’s so many, yeah.

Oh, there’s so many, yeah.

But the thing that I really wanna focus on with you today is complex systems, cognitive science, artificial intelligence.

但我今天真正想和大家讨论的是复杂系统、认知科学和人工智能。

You have a lot of different hats but I’m really very curious about the work that you’ve been doing to look at AI through the lens of either developmental psychology, like the way that we try to think about the alien intelligence of human babies, or comparative psychology with the alien intelligence of our pet dogs or smart birds or dolphins or that kind of thing.

你有很多不同的帽子，但我真的很好奇你一直在通过发展心理学的视角来看待人工智能，就像我们试图思考人类婴儿的外星智力的方式，或者与我们的宠物狗、聪明的鸟类或海豚或类似事物的外星智力的比较心理学。

I mean, it’s a really interesting take on this alien intelligence of AI.

我的意思是，这是对人工智能的外星智能的一个非常有趣的看法。

Yeah.

是的。

Many people have described AI as an alien kind of intelligence ’cause it’s very different from humans, even though it’s been trained on human language and books and everything on the internet and so on.

许多人将人工智能描述为一种外星智能，因为它与人类非常不同，尽管它接受过人类语言、书籍和互联网上的一切训练等等。

But the way that these systems work, the way that they learn, the way that they reason, the way they do what they do is just really different from the way humans do it.

但这些系统的工作方式、学习方式、推理方式以及做事的方式与人类的方式确实不同。

Yeah, I really like this point of view, and I think it may not be so familiar to our listeners.

是的，我真的很喜欢这个观点，而且我想我们的听众可能不太熟悉。

I have to admit it wasn’t that familiar to me.

我必须承认它对我来说并不那么熟悉。

You know, I never studied cognitive science, or never took a course in developmental psychology, and people in those fields have been thinking about these issues for…

你知道，我从未研究过认知科学，也从未上过发展心理学课程，而这些领域的人们一直在思考这些问题……

Well, I don’t know.

好吧，我不知道。

You tell me.

你告诉我。

Yeah, at least 100 years.

Yeah, at least 100 years.

Yeah, 100 years now.

是的，现在已经100年了。

Wow.

哇。

And I was thinking on the way over we constantly talk about AI as a black box.

我在想，我们不断地将人工智能视为一个黑匣子。

That we can’t read the weights on the neurons very easily, or even if we can, we don’t know what they tell us.

我们无法很容易地读取神经元上的权重，或者即使我们可以，我们也不知道它们告诉我们什么。

But for that matter, couldn’t you say that our own intelligence is in a lot of ways a black box?

但就此而言，你难道不能说我们自己的智力在很多方面都是一个黑匣子吗？

Absolutely.

绝对地。

I mean, we have different ways to penetrate the black box.

我的意思是，我们有不同的方法来穿透黑匣子。

One is neuroscience, where we actually stick probes into neurons, or we use fMRI or other imaging techniques.

一是神经科学，我们实际上将探针插入神经元，或者使用功能磁共振成像或其他成像技术。

There’s also psychology, where you actually look at just the behavior of a person or an animal, and try and infer from that underlying mechanisms.

还有心理学，你实际上只观察人或动物的行为，并尝试从潜在的机制中进行推断。

You mean it didn’t catch on sociologically, or what do you mean?

你的意思是它在社会学上没有流行，或者你的意思是什么？

You know, originally it was thought we’re going to program them the way that humans work.

你知道，最初我们认为我们要按照人类工作的方式对它们进行编程。

And there was a very close connection between human psychology and people trying to build human psychology into AI.

人类心理学与试图将人类心理学融入人工智能的人们之间存在着非常密切的联系。

And then that actually didn’t yield success in AI the way that we’ve seen neural networks and learning from data rather than trying to program it in.

事实上，这并没有像我们所看到的神经网络和从数据中学习而不是试图对其进行编程那样在人工智能领域取得成功。

And neural networks itself was originally inspired by neuroscience, but the way that neural networks work today has diverged considerably from that original inspiration.

神经网络本身最初受到神经科学的启发，但今天神经网络的工作方式与最初的灵感有很大不同。

So I think the field of machine learning has gone much more in the direction of statistics, which is quite separate from how cognitive science works.

所以我认为机器学习领域更多地朝着统计学的方向发展，这与认知科学的工作原理完全不同。

So at this point, I guess I’d like to talk a bit about benchmarks, because they do seem to be a big part of the discussion broadly in society these days.

因此，在这一点上，我想我想谈谈基准，因为它们似乎确实是当今社会广泛讨论的重要组成部分。

There was something that got a lot of people chattering in the world of math.

在数学世界里，有一件事情让很多人议论纷纷。

One of the latest frontier models did something that looked like a kind of creativity, solved an old, longstanding math problem one of the problems that Paul Erdős, the great, Hungarian mathematician, he left lots of problems for people to think about, and one of them that they call the unit distance problem was recently solved in a very clever way by AI, and it involved putting two parts of math together in a way that hadn’t really been tried before.

一个最新的前沿模型做了一些看起来像是一种创造力的事情，解决了一个古老的、长期存在的数学问题，伟大的匈牙利数学家保罗·埃尔多斯（Paul Erdős）留下了很多问题供人们思考，其中一个被他们称为单位距离问题的问题最近被人工智能以一种非常聪明的方式解决了，它涉及到以一种以前从未真正尝试过的方式将数学的两个部分放在一起。

And so I bring that up because the last time we spoke, we were talking about an old AI that was learning to play some Atari game, or something.

所以我提出这个问题是因为上次我们谈话时，我们谈论的是一个旧的人工智能，它正在学习玩一些 Atari 游戏或其他东西。

And you talked about how it was so good at playing, but then if you move the paddle a couple pixels up or something, it had to relearn all over again.

你谈到了它如何如此擅长演奏，但是如果你将桨向上移动几个像素或其他什么，它就必须重新重新学习。

It didn’t know how to play the slightest variation on the original game.

它不知道如何玩原始游戏的最细微的变化。

Yeah, I mean, that particular model was not a large language model.

是的，我的意思是，那个特定模型不是一个大型语言模型。

It was a specific model to play the Atari game.

这是玩雅达利游戏的特定模型。

Whereas now we have large language models that are trained on everything.

而现在我们拥有可以对所有内容进行训练的大型语言模型。

So in some sense, they don’t have to transfer anything.

所以从某种意义上说，他们不需要转移任何东西。

They’re already trained.

他们已经受过训练。

But, people in AI or machine learning talk about things that are in distribution and out of distribution, and that means that is this thing that we’re asking the models to do similar to things that it’s seen in its training data, or is wholly different?

但是，人工智能或机器学习领域的人们谈论的是分布内和分布外的事物，这意味着我们要求模型做的事情与训练数据中看到的事情类似，还是完全不同？

Where you know, “trained on everything” I think has some meaning in a way.

你知道，“接受过一切训练”我认为在某种程度上是有一定意义的。

If you say it’s been trained on everything that has to do with being human, clearly that’s not the case.

如果你说它接受了与人类有关的一切训练，显然事实并非如此。

But if you say it’s been trained on everything having to do with math or with code, I don’t know.

但如果你说它接受了与数学或代码有关的所有方面的训练，我不知道。

Is all of mathematical knowledge out there in some kind of textual or video format?

所有数学知识都以某种文本或视频格式存在吗？

Well, you’re asking me.

Well, you’re asking me.

I, so the thing that is roiling our community in math lately as we try to make sense of what just happened is we used to think, “Okay, these machines are very good at searching,” or, “These programs are good at searching big spaces.” They have a tremendous amount of knowledge because, as you say, they’ve ingested the whole internet and the Library of Congress, and anything you can read, they’ve read.

我，所以当我们试图理解刚刚发生的事情时，最近在数学界引起轰动的事情是我们过去常想，“好吧，这些机器非常擅长搜索”，或者“这些程序擅长搜索大空间”。他们拥有大量的知识，因为正如你所说，他们吸收了整个互联网和国会图书馆，以及任何你能读到的东西，他们都读过。

Yeah.

是的。

No, I think that’s right.

不，我认为这是对的。

I don’t…

我不……

You know, who knows how it happened because we can’t really look at the innards of the- these models very well for many reasons.

你知道，谁知道它是如何发生的，因为由于多种原因，我们无法真正了解这些模型的内部结构。

But it is creative to bring two unexpected things together and have something that’s actually working.

但将两个意想不到的东西结合在一起并得到真正有效的东西是有创意的。

I consider that creative.

我认为很有创意。

But, it sort of reminds me in a way, there was a math discovery program way back in the ‘70s maybe done by this guy, Douglas Lenat.

但是，这在某种程度上让我想起，早在 70 年代就有一个数学发现项目，可能是由这个人 Douglas Lenat 完成的。

It was called EURISKO, I think.

我想它叫 EURISKO。

And basically it was trying to find new ideas in math.

基本上它是试图在数学中寻找新的想法。

And it explicitly tried to bring together things and stick them together, and it would generate hundreds and hundreds and hundreds and hundreds of these things.

它明确地试图将事物聚集在一起并将它们粘在一起，并且它会产生成百上千的这样的东西。

Yeah, let’s get into that now because that’s really where I wanted to go with you.

是的，我们现在开始讨论这个问题，因为这就是我真正想和你一起去的地方。

It’s a nice phrase, the science of AI.

这是一个很好的短语，人工智能的科学。

I’d like to encourage people to look at this article of yours, Melanie, about the six principles to assess cognitive capacity of AI.

我想鼓励人们阅读你的这篇文章，梅兰妮，关于评估人工智能认知能力的六项原则。

But just, as a teaser, could you enunciate what are those six and say a little about them?

但是，作为一个预告，您能否阐明这六个是什么并简单介绍一下它们？

Sure.

当然。

So the first one is to be aware of your own anthropomorphic cognitive biases.

所以第一个就是要意识到你自己的拟人化认知偏见。

So we tend to project human likeness onto things that talk to us in fluent English.

因此，我们倾向于将人类的相似性投射到那些用流利的英语与我们交谈的事物上。

So people very much think that these models have human-like qualities when maybe they actually don’t.

因此，人们非常认为这些模型具有类似人类的品质，但实际上可能并非如此。

We all have very direct experience with number six, don’t we?

我们都对第六有非常直接的体验，不是吗？

When we see the hallucinations, it starts to make you wonder what’s really going on with these systems, and it’s true you learn a lot from the errors.

当我们看到幻觉时，它开始让你想知道这些系统到底发生了什么，而且确实你从错误中学到了很多东西。

Yeah, people celebrate their positive results and they try to explain away their negative results, but it’s important to really understand what’s going on by looking at where it fails.

是的，人们庆祝他们的积极结果，并试图解释他们的消极结果，但重要的是通过查看失败的地方来真正了解发生了什么。

So one example that you give in your article, this is not about AI, but this is about the kind of lesson from biology or from psychology that subtle things can be happening that you need to have an alert and skeptical mind to notice what might really be going on.

所以你在文章中举的一个例子，这不是关于人工智能的，而是关于生物学或心理学的一课，即微妙的事情可能会发生，你需要有一个警觉和怀疑的头脑来注意到可能真正发生的事情。

So could you just regale us with the old story of Clever Hans?

那么你能给我们讲讲聪明的汉斯的老故事吗？

So Clever Hans was a horse who lived in the early 1900s in Germany.

聪明的汉斯是一匹生活在 1900 年代初德国的马。

And Clever Hans was able to answer arithmetic questions.

聪明的汉斯能够回答算术问题。

So you’d say like, “What’s 14 plus 12?” And he would tap his hoof that many times.

所以你会说，“14 加 12 等于多少？”他会多次用蹄子敲击。

Looked like a genius horse.

看起来就像一匹天才马。

And people including many scientists living back then, were very convinced that this was an animal who could do mathematics, who could count, who could reason about simple problems in the way that humans do.

人们，包括当时的许多科学家，都非常确信这是一种可以做数学、可以计数、可以像人类一样推理简单问题的动物。

And then he fails.

然后他失败了。

And it turns out what he’s doing is he’s reading subtle cues on the face of the person who’s asking the question.

事实证明，他所做的就是从提问者的脸上读出微妙的暗示。

It turns out that if the person who’s asking the question doesn’t know the answer already, he also fails.

事实证明，如果提出问题的人不知道答案，他也会失败。

And so in this parable then, as far as like when we are impressed by something seemingly genius that AI is doing, what is our lesson?

那么，在这个寓言中，当我们对人工智能正在做的看似天才的事情印象深刻时，我们的教训是什么？

That, that we should be doing controlled experiments, or what?

那，我们应该做对照实验，还是什么？

Right.

正确的。

So, an AI system was shown to be really good at reasoning about diagrams in scientific papers, let’s say, I think this is, actually a real example, and could answer questions about them.

因此，人工智能系统被证明非常擅长推理科学论文中的图表，比如说，我认为这实际上是一个真实的例子，并且可以回答有关它们的问题。

But then the control experiment was give the questions without showing the diagrams.

但随后的对照实验给出了问题，但没有显示图表。

Seems crazy, right?

看起来很疯狂，对吧？

How could you answer questions about a diagram without seeing the diagram?

在没有看到图表的情况下，你如何能回答有关图表的问题？

And it turned out that the AI could do this task because somehow there was some kind of spurious association between the words in the questions and the correct answer.

事实证明，人工智能可以完成这项任务，因为问题中的单词和正确答案之间存在某种虚假的关联。

So that seems like a case of poor experimental design on whoever was doing the benchmark attempt in retrospect.

因此，回想起来，这似乎是一个对进行基准测试的人来说实验设计不佳的例子。

In retrospect, and in retrospect this happens all the time in psychology and other fields, I’m sure too, poor experimental design.

回想起来，这种情况在心理学和其他领域时常发生，我确信这也是糟糕的实验设计。

Experimental design is a very hard thing and there’s all kinds of confounding possibilities.

实验设计是一件非常困难的事情，并且存在各种令人困惑的可能性。

So this is why the notion of replication in science became so important.

这就是为什么科学中的复制概念变得如此重要。

If one group does an experiment and they get a result, we shouldn’t necessarily believe that result.

如果一组人做了一项实验并得到了结果，我们不一定相信这个结果。

That result might be due to some other aspect of their experimental design that wasn’t intended.

这个结果可能是由于他们的实验设计的其他一些非预期的方面造成的。

That’s why it’s very important for independent groups to replicate studies.

这就是为什么独立团体重复研究非常重要。

This isn’t something that people in AI do very much.

这并不是人工智能领域的人经常做的事情。

No, and why not?

不，为什么不呢？

Is it that the replication is not very glamorous because you’re coming in second like there’s no incentive.

是不是复制不是很迷人，因为你排在第二，就像没有激励一样。

That’s true in all parts of science, right?

这在科学的各个方面都是如此，对吗？

Yeah.

是的。

I think that’s true in all parts of science.

我认为科学的各个领域都是如此。

But it’s also because I think most of AI research is done by people whose background is in computer science or a related field that’s not focused on experimental methodology.

但这也是因为我认为大多数人工智能研究都是由计算机科学或相关领域背景的人完成的，这些领域不专注于实验方法。

I’m a computer scientist.

我是一名计算机科学家。

I never had to take a course in experimental methodology.

我从来不需要参加实验方法学课程。

No such course was ever offered to me in my department.

我的部门从未向我提供过此类课程。

It wasn’t seen as part of what computer science was all about, and I think that’s one of the things that’s lacking in today’s AI discussion.

它并没有被视为计算机科学的一部分，我认为这是当今人工智能讨论中所缺乏的事情之一。

How can we trust the results of these experiments and studies that are done that show that AI can do all these different things?

我们如何相信这些实验和研究的结果表明人工智能可以做所有这些不同的事情？

Fascinating.

迷人。

So it seems to me that there’s this cognitive science version of the interference of the observer that everyone talks about in quantum mechanics, right?

所以在我看来，每个人在量子力学中都在谈论观察者干扰的认知科学版本，对吗？

The observer themselves is interfering with the experiment or the outcome of the experiment, and that is such an interesting role.

观察者本身正在干扰实验或实验结果，这是一个非常有趣的角色。

Of course, this Clever Hans is very famous, and I agree that that is a very clever horse for being able to read the social cues.

当然，这匹聪明的汉斯非常有名，我同意这是一匹非常聪明的马，能够解读社交线索。

Yeah.

是的。

It’s a whole dimension that many of us in the theoretical sciences and math don’t get trained in, as Melanie freely admits.

正如梅勒妮坦率地承认的那样，我们许多理论科学和数学领域的人都没有接受过这方面的培训。

You know, I never took a course in experimental design.

你知道，我从未上过实验设计课程。

You as a physicist, I assume you had to take some experimental physics, but…

作为一名物理学家，我认为你必须学习一些实验物理学，但是……

Yeah.

是的。

It doesn’t really weigh in my actual work.

这对我的实际工作来说并没有多大影响。

It’s really not experimental.

这确实不是实验性的。

Yeah.

是的。

So I would not be a very good architect of a good experiment.

所以我不会成为一个很好的实验的很好的架构师。

Well, and it seems like it is, something that’s a very live issue because these days the AI companies frequently use benchmarks to show how – well, to assess how – how far along are their systems on this quest for either artificial general intelligence or superhuman intelligence, that sort of thing.

嗯，看起来确实如此，这是一个非常现实的问题，因为如今人工智能公司经常使用基准来展示——好吧，评估如何——他们的系统在追求通用人工智能或超人类智能之类的事情上走了多远。

Or even just to out-compete the other AI companies.

或者甚至只是为了超越其他人工智能公司。

We would like to know what the capacities are of these new machine learning systems and other AIs.

我们想知道这些新的机器学习系统和其他人工智能的能力是什么。

Well, I think it might be that it’s just, I don’t think we really know how to evaluate human intelligence, or to really know what somebody’s doing when they’re thinking.

嗯，我认为这可能只是，我认为我们并不真正知道如何评估人类智力，或者真正知道某人在思考时在做什么。

I don’t think we know about ourselves.

我认为我们不了解自己。

I don’t think we can self-report very well.

我认为我们不能很好地进行自我报告。

I can’t say to you, “Oh, this is how it’s working in here right now as I’m constructing this sentence.

我不能对你说：“哦，这就是我现在构建这个句子时的情况。

I listened to it, and this was the process.” I don’t know, right?

我听了，就是这个过程。”我不知道，对吧？

It’s just natural.

这很自然。

It just comes out.

它刚刚出来。

And I’m not that privy to the inner workings, and I feel the AI similarly.

我对内部运作不太了解，而且我对人工智能也有类似的感觉。

A lot of people have said, I’ve had conversations on our show before with other cognitive scientists and computer scientists and they say it’s really hard for the AI to answer questions, ’cause a lot of people say, “Why don’t you just ask it?” And it can’t self-reflect either in an accurate way.

很多人说过，我之前在节目中与其他认知科学家和计算机科学家进行过对话，他们说人工智能很难回答问题，因为很多人说，“你为什么不直接问它呢？”它也不能以准确的方式进行自我反思。

This whole thought, the mystery of the black box.

这整个想法，就是黑匣子之谜。

We use the term black box so often for the AI, but of course, our own intelligence is a black box, not just from mine to you, but even me to myself, as you’re emphasizing.

我们经常用“黑匣子”这个词来形容人工智能，但当然，我们自己的智能也是一个黑匣子，不仅从我到你，甚至从我到我自己，正如你所强调的那样。

But it makes me wonder if there’s a role for magicians because, you know, magicians or sleight-of-hand people are so good at showing us our own psychophysical limitations.

但这让我想知道魔术师是否有一个角色，因为你知道，魔术师或魔术师非常擅长向我们展示我们自己的心理物理学局限性。

How easily we’re fooled, or the sorts of cognitive errors we tend to make, and there are people who are analogous to the magicians who show the deficits and common sense of the AIs, right?

我们是多么容易被愚弄，或者我们容易犯什么样的认知错误，有些人就像魔术师一样，表现出了人工智能的缺陷和常识，对吗？

They’re sort of playing games that are almost like magic tricks on the AIs.

他们玩的游戏几乎就像人工智能上的魔术。

I wonder how revealing those will be, you know, in a serious scientific way.

我想知道这些将如何以严肃的科学方式揭示一切。

Welcome back to The Joy of Why .

欢迎回到“为什么的喜悦”。

We’re joined today by Santa Fe Institute computer scientist Melanie Mitchell.

今天，圣达菲研究所计算机科学家 Melanie Mitchell 也加入了我们的行列。

You have been a college professor for much of your life.

您一生的大部分时间都是大学教授。

When you’re working with students they can get the answers right, but as you start to probe what they actually understand, you start to realize that they might be getting the right answers for the wrong reasons.

当你与学生一起工作时，他们可以获得正确的答案，但当你开始探究他们真正理解的内容时，你开始意识到他们可能会因为错误的原因而得到正确的答案。

They don’t really know what they’re doing, and that’s important if you wanna be a helpful teacher.

他们并不真正知道自己在做什么，如果你想成为一名乐于助人的老师，这一点很重要。

This brings up another point: competence versus performance.

这就引出了另一点：能力与绩效。

Can you expand on this idea and, what would it mean in the AI context?

您能否详细阐述这个想法，以及它在人工智能背景下意味着什么？

So competence versus performance is kind of an old distinction from psychology and linguistics.

因此，能力与表现是心理学和语言学的一个古老的区别。

The idea is that you might have the competence for a particular cognitive capacity, but there might be some reasons why you can’t perform the task that I’m giving you.

这个想法是，你可能有特定的认知能力，但可能有一些原因导致你无法执行我给你的任务。

Like they have the competence, they could solve the problems, but they’re just emotionally frozen.

就像他们有能力一样，他们可以解决问题，但他们只是在情感上冻结了。

There’s some performance block.

有一些性能障碍。

Okay.

好的。

So if we would say that we’re trying to work out ways of testing whether the AI understands, what would count as evidence?

因此，如果我们说我们正在尝试找出测试人工智能是否理解的方法，那么什么可以算作证据呢？

Suppose that, you’re an AI advocate who said that these new systems, because we’ve scaled them up or because we have some nice new architecture with world models or social models or whatever, we’ve now crossed a threshold where they actually understand.

假设你是一位人工智能倡导者，他说这些新系统，因为我们已经扩大了它们的规模，或者因为我们有一些带有世界模型或社会模型或其他东西的漂亮的新架构，我们现在已经跨过了他们真正理解的门槛。

It’s not just that they can compute, they understand.

他们不仅能计算，还能理解。

What would count as evidence of understanding?

什么可以算作理解的证据？

Oh gosh.

哦天哪。

I hate to get pedantic about understanding, but there’s so many different meanings of it.

我讨厌在理解上变得迂腐，但它有很多不同的含义。

We had a talk here at Santa Fe Institute from a philosopher who broke down understanding into 25 different types.

我们在圣达菲研究所接受了一位哲学家的演讲，他将理解分为 25 种不同类型。

Aha.

啊哈。

I didn’t know what I was getting myself into with the question.

我不知道我问这个问题会让自己陷入什么境地。

So there’s like P understanding and G understanding and there’s this very long typography of understanding.

所以有 P 理解和 G 理解，还有很长的理解排版。

And I’m not sure there is any sort of single notion of real understanding.

我不确定是否存在某种单一的真正理解的概念。

One of the recent things I and my collaborators have been working on is looking at different dimensions of understanding.

我和我的合作者最近所做的事情之一是研究理解的不同维度。

One example is you can get one of these language models or chatbots to generate a story.

一个例子是您可以使用这些语言模型或聊天机器人之一来生成故事。

Just generate a short story about something, and they will.

只要写一个关于某件事的短篇故事，他们就会这么做。

They’ll generate a very beautiful little coherent short story.

他们会创作出一个非常美丽、连贯的短篇故事。

But then if you start asking them questions about the story, they will often will fail in weird ways.

但如果你开始向他们询问有关故事的问题，他们通常会以奇怪的方式失败。

even though they generated it.

即使他们创造了它。

And I think the same thing is true in a lot of different tasks that they understand along one dimension but not along another dimension.

我认为在许多不同的任务中也是如此，他们沿着一个维度理解但不沿着另一个维度理解。

And in some sense deep understanding might be just you understand across many different of these dimensions.

从某种意义上说，深刻的理解可能只是你对这些维度中许多不同维度的理解。

Aha.

啊哈。

That sounds like a promising direction.

这听起来是一个很有前途的方向。

Let’s talk about tasks a little more, because that’s a phrase or a term that I’ve seen in some of your writing, the phrase, the tyranny of tasks.

让我们多谈谈任务，因为这是我在你的一些写作中看到的一个短语或术语，这个短语，任务的暴政。

What’s that about?

那是关于什么的？

I first heard that, from Shannon Vallor, a philosopher.

我第一次从哲学家香农·瓦洛尔 (Shannon Vallor) 那里听到这个说法。

The idea is that in AI, the world is divided in terms of tasks.

这个想法是，在人工智能中，世界是根据任务来划分的。

So when we think about what AI systems can do, people say, “Oh, they can make summaries.

因此，当我们思考人工智能系统可以做什么时，人们会说：“哦，他们可以做总结。

Let’s test their ability to summarize articles.” Or, “Let’s test their ability to answer questions about diagrams” or I don’t know, some other benchmark.

我们来测试一下他们总结文章的能力。”或者，“让我们测试一下他们回答有关图表的问题的能力”或者我不知道，还有其他一些基准。

Well, I mean, these days, they’ve been benchmarked a lot on International Mathematical Olympiad, very hard high school problems, then there were research level problems.

嗯，我的意思是，这些天，他们在国际数学奥林匹克竞赛中进行了很多基准测试，非常难的高中问题，然后还有研究水平的问题。

Now there’s open problems that are unsolved in math.

现在数学中存在尚未解决的开放性问题。

These are all like three levels of math benchmarks that are out there.

这些就像现有的三个级别的数学基准。

Right, their capabilities are defined in terms of these benchmarks.

是的，他们的能力是根据这些基准来定义的。

You know, one benchmark might be the bar exam for law students, and they do really well on the bar exam.

你知道，一个基准可能是法学院学生的律师考试，他们在律师考试中表现得非常好。

And so we say, “Oh, lawyers, you should be afraid.

所以我们说：“哦，律师们，你们应该害怕。

Your job is threatened because these AI systems are as getting as good as you are.” Uh, But the way that we’re defining that is by looking at how well they do on a specific set of questions or a task.

你的工作受到威胁，因为这些人工智能系统和你一样好。”呃，但是我们定义这一点的方式是通过观察他们在一组特定问题或任务上的表现如何。

And jobs as a whole are not the same as just one independent task after another.

工作作为一个整体并不等同于一项又一项独立的任务。

This is, I think it’s almost like a fallacy that if an AI system can do a bunch of tasks, it can do the job of a person that is associated with those tasks.

我认为这几乎就像一个谬论，如果人工智能系统可以完成一堆任务，它就可以完成与这些任务相关的人的工作。

Still, it does leave you wondering, like in the case of radiology, you could imagine if they are really good at that task, then what’s left for the human radiologist?

尽管如此，它确实让你想知道，就像放射学的情况一样，你可以想象如果他们真的擅长这项任务，那么人类放射科医生还剩下什么？

Should we still be in that part of the game?

我们还应该参与游戏的那部分吗？

Like in my own world of math, you know, if they’re very good at proving theorems, but they’re not so great yet at coming up with new concepts, or as we sometimes speak of it, theory building, right?

就像在我自己​​的数学世界中一样，你知道，如果他们非常擅长证明定理，但他们还不太擅长提出新概念，或者正如我们有时所说的理论构建，对吗？

There’s this big distinction between problem-solving and theory building.

解决问题和理论构建之间存在很大的区别。

So is it that we’re sort of gonna find our niche, that we can do the parts that they don’t do?

那么我们是否会找到自己的定位，我们可以做他们不做的部分？

So like in the case of radiology, they have the open-ended part but not the scan reading part?

那么就像放射学的情况一样，他们有开放式部分，但没有扫描阅读部分？

I guess that’s what I’m wondering.

我想这就是我想知道的。

Is that how it’s gonna go?

事情会这样发展吗？

Maybe.

或许。

I wouldn’t be at all surprised if jobs like yours change quite a bit because of these new tools.

如果像您这样的工作因为这些新工具而发生很大变化，我一点也不会感到惊讶。

These are going to become incredibly useful tools for mathematicians.

这些将成为数学家非常有用的工具。

So it might change your job.

所以它可能会改变你的工作。

Just like when personal computers came out, but there’s a fantastic book by um, George Lakoff and Rafael Núñez about math and where ideas in math come from, via metaphors.

就像个人电脑问世时一样，乔治·莱考夫 (George Lakoff) 和拉斐尔·努涅斯 (Rafael Núñez) 有一本很棒的书，通过隐喻讲述了数学以及数学思想的来源。

And they feel that human embodiment is a very important part of understanding and mathematics.

他们认为人类的体现是理解和数学的一个非常重要的部分。

Exactly.

确切地。

I think that’s our only hope ’cause right now they the machines don’t have great embodiment.

我认为这是我们唯一的希望，因为现在机器还没有很好的体现。

And you’re right, that a lot of great ideas in math are inspired by experience with the world.

你是对的，数学中的许多伟大想法都是受到世界经验的启发。

And that’s what I was gonna say about applied math, that I feel like that’s even more so than pure math, where we get so much inspiration from nature and from engineering and society and all that, that I think we have a lot more chance of being useful as humans in applied math.

这就是我要说的关于应用数学的内容，我觉得它比纯数学更重要，我们从自然、工程和社会等方面获得了如此多的灵感，我认为我们作为人类在应用数学方面有更多的机会发挥作用。

Well, that’s a big question I have, which is, suppose that your prediction comes right and math, pure math expires in some sense for humans.

好吧，这是我的一个大问题，那就是，假设你的预测是正确的，数学，纯数学在某种意义上对人类来说已经过期了。

What does that mean for other fields?

这对其他领域意味着什么？

Does that mean that these machines are on their way to taking over everything?

这是否意味着这些机器即将接管一切？

Or is it more like 1997 or whatever it was that Deep Blue beat Kasparov and that actually beating the best human at chess did not necessarily mean that was gonna go anywhere in other fields.

或者更像是 1997 年，或者深蓝击败了卡斯帕罗夫，实际上击败了国际象棋中最好的人类并不一定意味着它会在其他领域取得任何进展。

I don’t know.

我不知道。

What do you think?

你怎么认为？

It feels to me like science is much more open-ended than math in that respect.

在我看来，在这方面科学比数学更加开放。

Yeah, I believe that.

是的，我相信这一点。

I don’t think that solving all the Erdos problems means that the average person has to fear for their job.

我不认为解决了鄂尔多斯的所有问题就意味着普通人就得担心自己的工作。

Okay, now we have many different things on the table at that point.

好吧，现在我们有很多不同的事情摆在桌面上。

But even just in the world of pure brainiacs, whether it’s scientists or mathematicians, just the fact that biology there are so many things to be measured, we have so much data that we could collect that we haven’t collected, so many new ways of observing.

但即使只是在纯粹的大脑世界中，无论是科学家还是数学家，事实上，生物学有很多东西需要测量，我们有很多可以收集但尚未收集的数据，有很多新的观察方式。

I mean, that seems very inexhaustible to me compared to math.

我的意思是，与数学相比，这对我来说似乎是无穷无尽的。

I agree.

我同意。

And even in physics, I think, which is maybe closer to math, there’s so much you know, open-ended questions that aren’t well-formulated, that don’t have something like a proof that can be constructed.

我认为，即使在可能更接近数学的物理学中，也有很多你知道的开放式问题，这些问题没有得到很好的阐述，也没有可以构建的证明之类的东西。

But so, I do feel like the hope for math is to continue to take inspiration from the real world.

但所以，我确实觉得数学的希望是继续从现实世界中获取灵感。

And von Neumann had said something like that too, that when math becomes too much art for art’s sake, when it drifts too far from the source, for him the source was nature or reality, if it becomes too far removed it becomes sterile, said von Neumann.

冯·诺依曼也说过类似的话，当数学变得太多为了艺术而艺术时，当它偏离源头太远时，对他来说，源头就是自然或现实，如果它变得太远，它就会变得贫瘠，冯·诺依曼说。

I’ll just say there’s this dictum in AI which is that easy things are hard and hard things are easy.

我只想说人工智能中有这样一句话：简单的事情很困难，困难的事情很容易。

And pure math is seen by humans as like the most exalted exhibition of intelligence and brilliance.

纯数学被人类视为智慧和才华的最崇高的展示。

It’s the hard thing, and yet we know that hard things are easier for machines and easier things are harder.

这是一件困难的事情，但我们知道，困难的事情对机器来说更容易，而容易的事情则更难。

Yep, and there’s the word soft also, right?

是的，还有“软”这个词，对吧？

In science, we talk about the hard sciences and the soft sciences, and the soft sciences of economics and psychology and anthropology, and those are the really hard ones.

在科学方面，我们谈论硬科学和软科学，以及经济学、心理学和人类学的软科学，这些才是真正硬的科学。

Well, so if we meet again in five years.

好吧，如果我们五年后再见面的话。

The Joy of Gamma, or something.

伽玛的乐趣，或者其他什么。

Yes, The Joy of Omega by then, right.

是的，那时，欧米茄的喜悦，对吧。

What do you hope we would understand about AI systems by then?

您希望届时我们对人工智能系统有什么了解？

Or what kinds of tests would we want to be able to do that we can’t do today?

或者我们希望能够进行哪些目前无法进行的测试？

Yeah, I mean What I really hope will go well in the science of AI is this field called mechanistic interpretability, which is the neuroscience analog, where you’re actually looking at the activations and the weights and the, you know, all the messy innards of the system, and understanding at a higher-level sort of what they are doing.

是的，我的意思是，我真正希望在人工智能科学中取得进展的是这个被称为机械可解释性的领域，这是神经科学的类似物，你实际上是在观察激活和权重，以及系统的所有混乱的内部结构，并在更高层次上理解它们正在做什么。

Interesting that you put your finger on that because the first time I became aware of you, it was in connection with that in a broad sense.

有趣的是，你指出了这一点，因为我第一次意识到你时，它与广义上的这一点有关。

So what I’m thinking of is back when you used to work on something that in the jargon was called GAs for CAs, genetic algorithms for cellular automata, you and Jim Crutchfield were looking at this problem of evolving algorithms that could solve a certain class of problems, hard computer science problems, and you were using this evolutionary algorithm to select better and better algorithms that kept improving through a kind of selection process.

所以我想到的是，当你过去从事用行话来说被称为 CA 的 GA、细胞自动机的遗传算法的工作时，你和 Jim Crutchfield 正在研究可以解决某一类问题、计算机科学难题的进化算法问题，并且你正在使用这种进化算法来选择越来越好的算法，这些算法通过一种选择过程不断改进。

Yeah.

是的。

that’s true.

这是真的。

I hadn’t made that connection exactly, but that’s interesting.

我没有准确地建立这种联系，但这很有趣。

It is this, though.

不过，就是这个。

It’s interpretability.

这是可解释性。

And we thought of that as a kind of emergent computation.

我们认为这是一种紧急计算。

And I think these AI systems also have emergent computations that are not easy to find, but they’re there, and if we understood them better, we would understand how the system is actually working, doing what it does.

我认为这些人工智能系统也有不容易找到的新兴计算，但它们就在那里，如果我们更好地理解它们，我们就会理解系统实际上是如何工作的，做什么的。

Yeah, it’s an interesting attitude.

是的，这是一种有趣的态度。

It feels honestly to me very sweet and very old school.

老实说，对我来说，这感觉非常甜蜜，而且非常古老。

This hope that…

这希望……

Okay, you’re chuckling ’cause you see where I’m going.

好吧，你笑了，因为你知道我要去哪里。

It’s a mean thing I’m saying, but this conceit that we with our limited minds can keep doing science, you know, and we’re gonna figure out how these AIs are doing what they’re doing, and that’s what our game will continue to be just like it always has been in science.

我说的很刻薄，但我们自以为用有限的头脑可以继续做科学，你知道，我们会弄清楚这些人工智能是如何做他们正在做的事情，这就是我们的游戏将继续像科学领域一样。

That’s an interesting question.

这是一个有趣的问题。

Um, why do we do science in the first place?

嗯，我们首先为什么要做科学？

I mean, you know, we do science ’cause we wanna solve problems.

我的意思是，你知道，我们做科学是因为我们想解决问题。

That’s one thing.

这是一回事。

But we also do science ’cause we’re driven to understand things.

但我们也从事科学研究，因为我们被驱使去理解事物。

You see this in little children.

你可以在小孩子身上看到这一点。

They’re driven to understand.

他们被驱使去理解。

Often one of their first words is why.

他们的第一句话通常就是为什么。

They ask it constantly.

他们不断地问。

So I think that’s a human drive, and it’s hard to fight against that.

所以我认为这是人类的驱动力，很难对抗它。

And that’s why you and I both went into science, it’s important to us.

这就是你和我都进入科学领域的原因，这对我们很重要。

Yeah.

是的。

To me, this is the bifurcation that we’re all thinking about now.

对我来说，这是我们现在都在思考的分歧。

’Cause science has this double-edged aspect, that it gives us pleasure, we like figuring things out, there is the joy of why, and as you say, it’s deep in our species.

因为科学有这个双刃剑，它给我们带来快乐，我们喜欢弄清楚事情，有为什么的快乐，正如你所说，它深深存在于我们物种之中。

So yes, we’re curious, but then there’s the other side that for so long science has been this instrumental thing that helps us in technology and medicine.

所以，是的，我们很好奇，但另一方面，长期以来，科学一直是帮助我们发展技术和医学的工具。

Yeah, that’s a great question.

是的，这是一个很好的问题。

When I was a child, I loved logic puzzles, like the knights and the knaves.

当我还是个孩子的时候，我喜欢逻辑谜题，比如骑士和无赖。

The knights who always told the truth and the knaves who always lied.

总是说真话的骑士和总是说谎的恶棍。

There’s a fun several books by Raymond Smullyan, a mathematician who wrote a bunch of puzzles in this genre that I absolutely loved.

数学家雷蒙德·斯穆里安 (Raymond Smullyan) 写了几本有趣的书，他写了很多我非常喜欢的此类类型的谜题。

Maybe we do have to revisit this in five years because we may know by then.

也许我们确实必须在五年后重新审视这个问题，因为到那时我们可能就知道了。

Given how fast everything is going, who knows?

考虑到一切进展得有多快，谁知道呢？

I really appreciate your spending time with us.

我非常感谢您与我们共度时光。

This has been wide-ranging, a little bit amorphous conversation, but it’s just wide open and I can’t think of a better guide to it.

这是一个范围广泛、有点无定形的对话，但它只是开放的，我想不出更好的指南。

Thank you very much for joining us.

非常感谢您加入我们。

Thanks, Steve.

谢谢，史蒂夫。

It’s been great.

太棒了。

Hmm.

唔。

Hmm.

唔。

I, I just remember being a student and learning Newton’s laws for the first time, and then Kepler’s laws, which really make Newton’s laws beautiful, this application to the celestial cycles.

我，我只记得当一名学生时，第一次学习牛顿定律，然后是开普勒定律，这确实使牛顿定律变得美丽，这个应用到天体周期。

I didn’t think, “Oh, I’m not the best at this, therefore I shouldn’t learn it.” Nor did I think, unless I one day become the best at this, I cannot feel pleasure or joy in my experience of acquiring this information.”

我并没有想：“哦，我在这方面不是最擅长的，所以我不应该学它。”我也不认为，除非有一天我成为这方面的佼佼者，否则我无法在获取这些信息的经历中感到快乐或喜悦。”

I– Well, let’s explore this a little more.

我——好吧，让我们再进一步探讨一下。

I like especially your emphasis on not being the best, and how, in a way, unfraught that is.

我特别喜欢你强调“不是最好的”，以及在某种程度上，这并不令人担忧。

I, I learned as soon as I went to college what it means to not be the best.

我，我一上大学就知道什么是不是最好的。

You know, this, this fixation with being the number one, especially in an age of optimization.

你知道，这种对第一的执着，尤其是在优化时代。

There’s so many optimization algorithms.

优化算法有很多。

We talk about faster, cheaper.

我们谈论更快、更便宜。

But in our own lives, very often we’re not the best.

但在我们自己的生活中，很多时候我们并不是最好的。

I’m certainly not the best tennis player.

我当然不是最好的网球运动员。

I love to play tennis.

我喜欢打网球。

I’m not the best chess player, and I’m still happy to play chess.

我不是最好的棋手，但我仍然很高兴下棋。

And try to be the best dad, but I may not be.

并努力成为最好的爸爸，但我可能不是。

But still, all these things are worth doing for their own sake, right?

但是，所有这些事情本身都是值得做的，对吗？

They give us pleasure.

他们给我们带来快乐。

So okay.

那么好吧。

What is that doing on our show here about science?

我们的科学节目中它在做什么？

Well, I think that let’s say the meaning for some people of life has to do with acquisition, acquiring wealth.

嗯，我认为，对于某些人来说，生命的意义与获得、获得财富有关。

They’re gonna love this stuff, right?

他们会喜欢这个东西的，对吧？

’Cause there’s gonna be this new tool that simply leverages all kinds of buttons that they now have faster access to and can exploit and acquire more wealth.

因为将会出现这种新工具，它可以简单地利用各种按钮，他们现在可以更快地访问这些按钮，并可以利用和获取更多财富。

Well, that’s the question, right?

嗯，这就是问题，对吧？

I think there are two plausible scenarios.

我认为有两种可能的情况。

One is that it continues to be a tool, and we always have some essential role in science and math at the cutting edge.

一是它仍然是一种工具，我们在前沿的科学和数学领域始终发挥着一些重要作用。

The other option is, and actually in my heart I believe this is the case, that we will not be at the cutting edge, and that will happen very soon.

另一种选择是，实际上我内心相信情况就是如此，我们不会处于最前沿，但这很快就会发生。

And, so then what is the point?

那么还有什么意义呢？

Well, I think there’s a difference between, making discoveries and understanding, and I guess that’s kind of what I mean in examples.

嗯，我认为发现和理解之间是有区别的，我想这就是我在例子中的意思。

In some sense, maybe the su- supercomputer made the discovery before the person did, but we still say the person did ’cause the discovery didn’t count as a discovery until they rendered it in a way that human beings could comprehend.

从某种意义上说，也许超级计算机在人之前发现了这一点，但我们仍然说是人发现的，因为在他们以人类可以理解的方式呈现它之前，这个发现不算是一个发现。

There is one thing I think we can be very optimistic about and hopeful about, which is I think we’re gonna have a glorious golden age of science where we will understand, and discoveries by the AIs or by people in conjunction with AIs, that’s all gonna be happening in the next, whatever, five, 10, 15 years, and it’s gonna be a spectacular fireworks time for science.

我认为我们可以对一件事非常乐观和充满希望，那就是我认为我们将迎来一个辉煌的科学黄金时代，我们将理解人工智能或人们与人工智能结合的发现，这一切都将在接下来的五年、十年、十五年中发生，这将是科学的一个壮观的烟花时代。

And I think that hopefully with any luck, we’ll be alive to see all that.

我想，如果运气好的话，我们能活着看到这一切。

Yeah, there’s definitely going to be a transition period where people are moving it fast and furious, and they’re part of the story, and there’s great accomplishment, and it will be exciting to see.

是的，肯定会有一个过渡期，人们会快速而激烈地推动它，他们是故事的一部分，并且取得了巨大的成就，这将是令人兴奋的。

I know people, very accomplished, who are very excited about using it.

我认识一些非常有成就的人，他们对使用它感到非常兴奋。

Use it every day.

每天使用它。

They have multiple things going on, and they just feel like their productivity has doubled or more.

他们有很多事情要做，他们只是觉得自己的生产力提高了一倍或更多。

And they’re excited, they’re enjoying themselves.

他们很兴奋，他们很享受。

I think there’s really nothing we can do but chime in and participate in this, at least, transition phase before we’re obsolete.

我认为我们真的无能为力，只能插手并参与这个至少在我们被淘汰之前的过渡阶段。

Well, I’m getting choked up just thinking about it.

好吧，光是想想我就哽咽了。

Thanks, Janna.

谢谢，詹娜。

It’s always great to see you, and we’ll see you next time on The Joy of Why .

很高兴见到你，我们下次在《为什么的喜悦》中再见。

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您还可以对节目发表评论。

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它可以帮助人们找到这个播客。

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在 Quantamamagazine.org 上查找文章、新闻通讯、视频等。

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《The Joy of Why》是来自《Quanta》杂志的播客，《Quanta》杂志是一份由西蒙斯基金会支持的独立编辑出版物。

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西蒙斯基金会的资助决定不会影响本播客或广达杂志的主题、嘉宾或其他编辑决定的选择。

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《为什么的喜悦》由 PRX Productions 制作。