自然界的模式可被经典 AI 高效学习

在你的诺贝尔奖演讲中，你提出了一个我觉得非常有趣的猜想：任何能在自然界中生成或发现的模式，都可以被经典学习算法高效地发现和建模。这包括哪些系统和模式？生物学、化学、物理学，也许还有宇宙学、神经科学。我们具体在讨论什么？

In your Nobel Prize lecture, you propose what I think is a super interesting conjecture that quote any pattern that can be generated or found in nature can be efficiently discovered and modeled by a classical learning algorithm. What kind of patterns of systems might be included in that? Biology, chemistry, physics, maybe cosmology, neuroscience. What are we talking about?

当然。我觉得诺贝尔奖演讲有一个传统，就是应该有点挑衅性，我想遵循这个传统。我所说的意思是，退一步看我们所有的成果，特别是 Alpha 系列项目——比如 AlphaGo，当然还有 AlphaFold。它们的本质是构建高度组合的、高维空间的模型。如果你试图暴力求解，比如在围棋中找到最佳走法，或者找出蛋白质的精确形状，枚举所有可能性的话，宇宙的时间都不够用。所以你必须更聪明地做事，而我们在两个案例中所做的就是构建这些环境的模型。这以聪明的方式引导搜索，使其变得可行。所以想想蛋白质折叠，这显然是一个自然系统——为什么这应该是可能的？物理学是如何做到的？蛋白质在我们体内毫秒级地折叠。所以物理学以某种方式解决了这个问题，而我们现在也用计算解决了它。我认为这可能的原因是，在自然界中，自然系统具有结构，因为它们经历了塑造它们的进化过程。如果这是真的，那么你或许可以学习那个结构。

Sure. Well, look, I felt that it's sort of a tradition, I think, of Nobel Prize lectures that you're supposed to be a little bit provocative and I wanted to follow that tradition. What I was talking about there is if you take a step back and you look at all the work that we've done especially with the Alpha projects, so I'm thinking AlphaGo, of course AlphaFold. What they really are is we're building models of very combinatorially high-dimensional spaces that, if you tried to brute force a solution, find the best move in Go or find the exact shape of a protein, and if you enumerated all the possibilities, there wouldn't be enough time in the time of the universe. So you have to do something much smarter, and what we did in both cases was build models of those environments. And that guided the search in a smart way and that makes it tractable. So if you think about protein folding, which is obviously a natural system, why should that be possible? How does physics do that? Proteins fold in milliseconds in our bodies. So somehow physics solves this problem that we've now also solved computationally. And I think the reason that's possible is that in nature, natural systems have structure because they were subject to evolutionary processes that shape them. And if that's true, then you can maybe learn what that structure is.

你暗示过这一点，几乎可以粗略地说：任何能被进化的东西都可以被高效建模。我觉得这有些道理。

You've hinted at it, which is almost like crudely stated: anything that can be evolved can be efficiently modeled. I think there's some truth to that.

是的，我有时称之为“最稳定者生存”之类的说法，因为当然有生物的进化，但如果你考虑地质时间，山的形状是由风化过程经过数千年塑造的。你甚至可以扩展到宇宙学：行星的轨道、小行星的形状。这些都经历了多次作用的过程而幸存下来。所以如果这是真的，那么应该存在某种模式，你可以逆向学习，实际上是一种流形，帮助你搜索到正确的解、正确的形状，并让你高效地预测它，因为它不是一个随机模式。所以对于人造事物或抽象事物，比如分解大数，可能是不可能的，因为除非数字空间中有模式（也许有），但如果不存在且是均匀的，那么就没有模式可学，没有模型可学来帮助搜索。所以你只能暴力求解。在这种情况下，你可能需要量子计算机之类的东西。但在我们感兴趣的大多数自然事物中，它们并非如此。它们有结构，这些结构因某种原因进化并随时间幸存。如果这是真的，我认为这有可能被神经网络学习。

Yeah, I sometimes call it survival of the stablest or something like that, because of course there's evolution for living things, but there's also, if you think about geological time, the shape of mountains has been shaped by weathering processes over thousands of years. You can even take it cosmological: the orbits of planets, the shapes of asteroids. These have all survived processes that have acted on them many times. So if that's true, then there should be some sort of pattern that you can reverse learn, and a kind of manifold really that helps you search to the right solution, the right shape, and actually allow you to predict things about it in an efficient way, because it's not a random pattern. So it may not be possible for man-made things or abstract things like factorizing large numbers, because unless there are patterns in the number space (which there might be), but if there's not and it's uniform, then there's no pattern to learn, no model to learn that will help you search. So you have to do brute force. In that case, you maybe need a quantum computer or something like that. But in most things in nature that we're interested in, they are not like that. They have structure that evolved for a reason and survived over time. And if that's true, I think that's potentially learnable by a neural network.

这就像自然在进行一个搜索过程，而令人着迷的是，在这个搜索过程中，它创造了可以被高效建模的系统。没错。太有趣了。

It's like nature is doing a search process and it's so fascinating that in that search process it's creating systems that could be efficiently modeled. That's right. So interesting.

所以它们可以被高效地重新发现或恢复，因为自然不是随机的。我们周围看到的一切，包括更稳定的元素，所有这些都受到某种选择过程的压力。

So they can be efficiently rediscovered or recovered, because nature is not random. Everything that we see around us, including the elements that are more stable, all of those things are subject to some kind of selection process pressure.

你认为，因为你也是理论计算机科学和复杂性的爱好者，我们能否提出一种复杂性类，比如复杂性动物园中的一类，也许是可学习系统的集合，可学习的自然系统的集合，LNS？这是一类新的系统，实际上可以通过经典系统以这种方式学习。可以被高效建模的自然系统。

Do you think, because you're also a fan of theoretical computer science and complexity, do you think we can come up with a kind of complexity class, like a complexity zoo type of class, where maybe it's the set of learnable systems, the set of learnable natural systems, LNS? This is a new class of systems that could be actually learnable by classical systems in this kind of way. Natural systems that can be modeled efficiently.

是的，我一直对 P vs NP 问题以及什么能被经典系统（非量子系统，实际上是图灵机）建模着迷。这正是我实际上在做的，在我为数不多的空闲时间里和几位同事一起，研究是否应该有一类新的问题，可以通过这种神经网络过程解决，并映射到这些自然系统上，也就是存在于物理学中且有结构的事物。

Yeah, I mean I've always been fascinated by the P vs NP question and what is modelable by classical systems, non-quantum systems, Turing machines in effect. And that's exactly what I'm working on actually, in my few moments of spare time with a few colleagues, about whether there should be a new class of problem that is solvable by this type of neural network process and kind of mapped onto these natural systems, so the things that exist in physics and have structure.

所以我认为这可能是一种非常有趣的新思考方式。这与我思考物理学的方式一致，即我认为信息是首要的。信息是宇宙最基本的单位，比能量和物质更基本。我认为它们都可以相互转化，但我把宇宙看作一种信息系统。

So I think that could be a very interesting new way of thinking about it. And it sort of fits with the way I think about physics in general, which is that I think information is primary. Information is the most fundamental unit of the universe, more fundamental than energy and matter. I think they can all be converted into each other, but I think of the universe as a kind of informational system.

所以当你把宇宙看作信息系统时，P vs NP 问题就是一个物理学问题。

So when you think of the universe as an informational system, then the P vs NP question is a physics question.

没错。而且这个问题实际上可以帮助我们解决整个事情。

That's right. And it's a question that can help us actually solve the entirety of this whole thing going on.

是的，我认为如果你把物理学看作信息学，这实际上是最基本的问题之一，答案将会非常有启发性。更具体地说到 P vs NP 问题。我们说的有些东西现在听起来有点疯狂。就像克里斯蒂安·埃丁森诺贝尔奖演讲中有争议的话听起来很疯狂，然后你和约翰·詹珀解决了这个问题并获得了诺贝尔奖。所以，让我只谈 P=NP。你认为我们讨论的这些东西中，是否有可能通过提前进行多项式时间或常数时间的计算，构建一个巨大的模型，从而以理论计算机科学的方式解决一些极其困难的问题？

Yeah, I think it's one of the most fundamental questions actually if you think of physics as informational, and the answer to that I think is going to be very enlightening. More specific to the P vs NP question. This again, some of the stuff we're saying is kind of crazy right now. Just like the Christian Edinson Nobel Prize speech controversial thing that he said sounded crazy and then you went and got a Nobel Prize for this with John Jumper solved the problem. So, let me just stick to the P equals NP. Do you think there's something in this thing we're talking about that could be shown if you can do something like polynomial time or constant time compute ahead of time and construct this gigantic model then you can solve some of these extremely difficult problems in a theoretical computer science kind of way.

是的，我认为实际上有一大类问题可以用这种方式来表述。就像我们做 AlphaGo 和 AlphaFold 的方式，你建模系统的动力学、系统的属性、你想要理解的环境，然后这使得寻找解决方案或预测下一步变得高效，基本上是多项式时间，因此经典系统（神经网络就是其中之一）可以处理。它运行在普通计算机上，对吧？经典计算机，实际上是图灵机。我认为这是最有趣的问题之一：这种范式能走多远？我认为我们已经证明，而且整个 AI 界也证明，经典系统、图灵机可以比我们之前想象的走得更远。它们可以做像建模蛋白质结构和下围棋超过世界冠军水平这样的事情。而很多人可能在 10 或 20 年前认为这还需要几十年，或者可能需要某种量子机器、量子系统才能做蛋白质折叠之类的事情。所以我认为我们甚至还没有触及经典系统所能做到的表面。当然，建立在经典计算机之上的神经网络系统上的 AGI 将是这一点的终极体现。我认为这种系统的极限、边界，它能做什么，是一个非常有趣的问题，直接关系到 P=NP 问题。

Yeah, I think that there are actually a huge class of problems that could be couched in this way. The way we did AlphaGo and the way we did AlphaFold, where you model what the dynamics of the system is, the properties of that system, the environment that you're trying to understand, and then that makes the search for the solution or the prediction of the next step efficient, basically polynomial time, so tractable by a classical system, which a neural network is. It runs on normal computers, right? Classical computers, Turing machines in effect. And I think it's one of the most interesting questions there is: how far can that paradigm go? You know, I think we've proven, and the AI community in general, that classical systems, Turing machines, can go a lot further than we previously thought. They can do things like model the structures of proteins and play Go better than world champion level. And a lot of people would have thought maybe 10, 20 years ago that was decades away, or maybe you would need some sort of quantum machines, quantum systems, to be able to do things like protein folding. And so I think we haven't really even scratched the surface yet of what classical systems, so-called, could do. And of course, AGI being built on a neural network system on top of a classical computer would be the ultimate expression of that. And I think the limit, the bounds of that kind of system, what it can do, is a very interesting question and directly speaks to the P equals NP question.

再假设一下，你认为可能超出这个范围的是什么？也许是涌现现象，比如你看元胞自动机，一些极其简单的系统然后出现复杂性，对吧？也许那会超出范围，或者你甚至猜测那也可能适合用经典机器进行高效建模？

What do you think again hypothetical might be outside of this maybe emergent phenomena like if you look at cellular automata, some of the extremely simple systems and then some complexity emerges, yes? Maybe that would be outside or even would you guess even that might be amenable to efficient modeling by a classical machine?

是的，我认为这些系统正好处于边界上。所以我认为大多数涌现系统、元胞自动机之类的东西，都可以用经典系统建模。你只需对它进行前向模拟，可能就足够高效了。当然，还有像混沌系统这样的问题，初始条件非常重要，然后你会得到一些不相关的最终状态；这些可能很难建模。所以我认为这些是开放性问题。但当你退一步，看看我们用这些系统做了什么，解决了哪些问题，然后看看像视频生成、渲染物理和光照之类的东西，物理学中真正核心的基础，这非常有趣。我认为这告诉我们一些关于宇宙结构的基本信息。所以从某种意义上说，这就是我想构建 AGI 的原因：帮助我们作为科学家回答这些问题，比如 P vs NP。

Yeah, I think those systems would be right on the boundary. So I think most emergent systems, cellular automata things, could be modelable by a classical system. You just sort of do a forward simulation of it and it probably be efficient enough. Of course, there's the question of things like chaotic systems where the initial conditions really matter and then you get to some uncorrelated end state; those could be difficult to model. So I think these are kind of the open questions. But I think when you step back and look at what we've done with the systems and the problems that we've solved, and then you look at things like video generation, rendering physics and lighting, really core fundamental things in physics, it's pretty interesting. I think it's telling us something quite fundamental about how the universe is structured, in my opinion. So in a way, that's what I want to build AGI for: to help us as scientists answer these questions, like P vs NP.

是的，我认为我们可能会不断惊讶于经典计算机可以建模的东西。我的意思是，AlphaFold 3 在相互作用方面令人惊讶，你能在那个方向上取得任何进展。AlphaGenome 令人惊讶，你能将遗传密码映射到功能，有点像处理涌现现象。你认为有那么多组合选项，然后你就能找到可以被高效建模的核心。

Yeah, I think we might be continuously surprised about what is modelable by classical computers. I mean AlphaFold 3 on the interaction side is surprising that you can make any kind of progress on that direction. AlphaGenome is surprising that you can map the genetic code to the function, kind of playing with the emergent kind of phenomena. You think there's so many combinatorial options and then here you go, you can find the kernel that is efficiently modeled.

是的。因为存在某种结构，某种景观，比如能量景观或其他什么，你可以沿着某个梯度前进。当然，神经网络非常擅长的是跟随梯度。所以如果有一个梯度可以跟随，并且你能正确指定目标函数，你就不必处理所有那些复杂性，我认为这可能是我们几十年来天真地思考这些问题的方式：如果你只是枚举所有可能性，看起来完全不可行。有很多很多这样的问题。然后你想，嗯，有 10^300 种可能的蛋白质结构，有 10^170 种可能的围棋局面，这些都远远超过宇宙中的原子数。那么一个人怎么可能找到正确的解决方案或预测下一步呢？但事实证明这是可能的。当然，现实、自然界确实做到了：蛋白质确实折叠了。所以这给了你信心，如果我们理解了物理学是如何做到这一点的，那么我们就可以模仿这个过程，建模这个过程，并且应该可以在我们的经典系统上实现。这基本上就是猜想的内容。

Yes. Because there's some structure, there's some landscape, you know, in the energy landscape or whatever it is, that you can follow some gradient. And of course what neural networks are very good at is following gradients. And so if there's one to follow and you can specify the objective function correctly, you don't have to deal with all that complexity, which I think is how we maybe have naively thought about it for decades: those problems, if you just enumerate all the possibilities, it looks totally intractable. And there are many, many problems like that. And then you think, well, it's like 10^300 possible protein structures, it's 10^170 possible Go positions, all of these are way more than atoms in the universe. So how could one possibly find the right solution or predict the next step? And it turns out that it is possible. And of course reality, nature does do it: proteins do fold. So that gives you confidence that there must be, if we understood how physics was doing that, then we could mimic that process, model that process, and it should be possible on our classical systems. That is basically what the conjecture is about.

当然还有非线性动力系统，高度非线性的动力系统，一切涉及流体的东西。

And of course there's nonlinear dynamical systems, highly nonlinear dynamical systems, everything involving fluid.

是的。

Yes.

对。

Right.

你知道，我最近和陶哲轩谈过，他在数学上处理系统的一个非常困难的方面，这些系统中有一些奇点破坏了数学，我们人类很难对高度非线性的动力系统做出任何清晰的预测。但再次，正如你所说，我们可能会非常惊讶于经典学习系统甚至能对流体做些什么。

You know, I recently had a conversation with Terence Tao, who mathematically contends with a very difficult aspect of systems that have some singularities in them that break the mathematics, and it's just hard for us humans to make any kind of clean predictions about highly nonlinear dynamical systems. But again, to your point, we might be very surprised what classical learning systems might be able to do about even fluid.

是的，完全正确。

Yes, exactly.

我是说流体动力学、纳维-斯托克斯方程，这些传统上被认为是非常非常困难、棘手的问题，在经典系统上难以处理。它们需要巨大的算力，比如天气预报系统这类东西都涉及流体动力学计算。但再看看像 Veo 这样的视频生成模型，它能很好地模拟液体，出奇地好，还有材质、镜面光照。我喜欢那些视频，比如有人生成透明液体通过液压机被挤压出来的画面。我早年做游戏时写过物理引擎和图形引擎，我知道编写能实现这些的程序有多艰难，然而这些系统仅仅通过观看 YouTube 视频就逆向工程出了这些。所以很可能它正在提取这些材料行为背后的某种底层结构。也许存在某种低维流形，如果我们真正理解其内部机制，就能学习到。这或许对大多数现实都成立。

I mean fluid dynamics, Navier-Stokes equations, these are traditionally thought of as very, very difficult, intractable problems on classical systems. They take enormous amounts of compute, you know, weather prediction systems, these kind of things all involve fluid dynamics calculations. But again, if you look at something like Veo, our video generation model, it can model liquids quite well, surprisingly well, and materials, specular lighting. I love the ones where people have generated videos with clear liquids going through hydraulic presses and being squeezed out. I used to write physics engines and graphics engines in my early days in gaming, and I know it's just so painstakingly hard to build programs that can do that, and yet somehow these systems are reverse engineering from just watching YouTube videos. So presumably what's happening is it's extracting some underlying structure around how these materials behave. So perhaps there is some kind of lower-dimensional manifold that can be learned if we actually fully understood what's going on under the hood. That's maybe true of most of reality.

是的。我一直对 Veo 的这个方面印象深刻。我想很多人会强调不同的方面，包括喜剧效果、表情包之类的东西，还有超逼真地捕捉人类的能力，既引人入胜又接近现实，再加上原生音频。这些都是 Veo 的奇妙之处。但你提到的正是物理这一点——它并不完美，但已经相当出色了。然后真正有趣的科学问题是：它为了做到这一点，对我们的世界理解了什么？因为对扩散模型持怀疑态度的人会说，它根本不可能理解任何东西。但似乎——我是说，我不认为没有理解就能生成那样的视频。然后我们自己对“理解”的哲学概念就被摆到了台面上。你认为 Veo 在多大程度上理解我们的世界？

Yeah. I've been continuously impressed by this aspect of Veo. I think a lot of people highlight different aspects, including the comedic and the meme and all that kind of stuff, and then the ultra-realistic ability to capture humans in a really nice way that's compelling and feels close to reality, and then combine that with native audio. All of those are marvelous things about Veo. But the exactly the thing you're mentioning, which is the physics—it's not perfect, but it's pretty damn good. And then the really interesting scientific question is: what is it understanding about our world in order to be able to do that? Because the cynical take with diffusion models is there's no way it understands anything. But it seems—I mean, I don't think you can generate that kind of video without understanding. And then our own philosophical notion of what it means to understand is brought to the surface. To what degree do you think Veo understands our world?

我认为，就它能以连贯的方式预测下一帧而言，这是一种理解的形式，对吧？不是拟人化的那种——不是对正在发生的事情有某种深刻的哲学理解。我不认为这些系统有那种理解。但它们确实对动力学建模得足够好，可以相当准确地生成 8 秒一致的视频，肉眼乍看很难分辨出问题。想象一下再过两三年会怎样。这就是我在想的——考虑到我们一两年前的早期版本，那将多么不可思议。进步的速度令人难以置信。而且我想，像你一样，很多人喜欢那些单口喜剧——它们确实很好地捕捉了很多人类动态、肢体语言——但实际上最让我印象深刻和着迷的是物理行为、光照、材质和液体。它能做到这一点非常惊人。我认为这表明它至少有一些直观物理的概念，对吧？事物应该如何直观地运作，也许就像人类小孩理解物理的方式，而不是像博士生那样能解出所有方程。这更像是一种直观的物理理解。

I think to the extent that it can predict the next frames in a coherent way, that is a form of understanding, right? Not in the anthropomorphic version—it's not some kind of deep philosophical understanding of what's going on. I don't think these systems have that. But they certainly have modeled enough of the dynamics, put it that way, that they can pretty accurately generate 8 seconds of consistent video that by eye, at a glance, is quite hard to distinguish what the issues are. And imagine that in two or three more years' time. That's the thing I'm thinking about—how incredible that will look, given where we've come from, the early versions one or two years ago. The rate of progress is incredible. And I think, like you, a lot of people love all the stand-up comedians—that actually captures a lot of human dynamics very well, body language—but actually the thing I'm most impressed with and fascinated by is the physics behavior, the lighting and materials and liquids. And it's pretty amazing that it can do that. And I think that shows that it has some notion of at least intuitive physics, right? How things are supposed to work intuitively, maybe the way that a human child would understand physics, as opposed to a PhD student really being able to unpack all the equations. It's more of an intuitive physics understanding.

嗯，那种直观的物理理解——那是基础层，是人们有时称之为常识的东西。它确实理解了一些东西。我想这真的让很多人惊讶。这让我震惊，我原本以为没有理解就不可能生成那种程度的真实感。

Well, that intuitive physics understanding—that's the base layer, that's the thing people sometimes call common sense. It really understands something. I think that really surprised a lot of people. It blows my mind that I just didn't think it would be possible to generate that level of realism without understanding.

有一种观点认为，只有通过具身 AI 系统，即与物理世界交互的机器人，才能理解物理世界。那是构建对世界理解的唯一途径。但 Veo 似乎直接挑战了这一点。

There's this notion that you can only understand the physical world by having an embodied AI system, a robot that interacts with that world. That's the only way to construct an understanding of that world. But Veo is directly challenging that, it feels like.

是的，这非常有趣。即使你 5 或 10 年前问我，我也会说——尽管我沉浸在这其中——我会说，‘嗯，是的，你可能需要理解直观物理。比如，如果我把这个杯子推下桌子，它可能会碎，液体也会洒出来。’所以我们都懂这些。但我曾认为——而且神经科学中有很多理论叫‘感知中的行动’，认为你需要在世界中行动才能真正深刻地感知它。有很多理论认为需要具身智能或机器人，或者至少是模拟行动，才能理解像直观物理这样的东西。但似乎通过被动观察就能理解，这让我非常惊讶。而且我认为，这暗示了关于现实本质的某种底层东西，在我看来，超越了它生成的酷炫视频。当然，下一阶段可能是让这些视频变得可交互，这样人们可以真正进入其中并在里面移动，这将会非常震撼，尤其考虑到我的游戏背景。所以你可以想象——然后我认为我们开始接近我所说的世界模型，一个关于世界如何运作、世界机制、世界物理以及其中事物的模型。当然，这正是真正的 AGI 系统所需要的。

Yes, and it's very interesting. Even if you were to ask me 5 or 10 years ago, I would have said—even though I was immersed in all of this—I would have said, 'Well, yeah, you probably need to understand intuitive physics. Like, if I push this glass off the table, it will maybe shatter, and the liquid will spill out.' So we know all of these things. But I thought that—and there's a lot of theories in neuroscience called 'action in perception' where you need to act in the world to truly perceive it in a deep way. And there were a lot of theories about needing embodied intelligence or robotics, or maybe at least simulated action, so that you would understand things like intuitive physics. But it seems that you can understand it through passive observation, which is pretty surprising to me. And again, I think it hints at something underlying about the nature of reality, in my opinion, beyond just the cool videos it generates. And of course, the next stage is maybe even making those videos interactive, so one can actually step into them and move around them, which would be really mind-blowing, especially given my games background. So you can imagine—and then I think we're starting to get towards what I would call a world model, a model of how the world works, the mechanics of the world, the physics of the world, and the things in that world. And of course, that's what you would need for a true AGI system.

我得跟你聊聊电子游戏。你有点爱搞怪。我觉得你在 Twitter/X 上越来越享受了，这很好。有个叫 Jimmy Apples 的人发推说：‘让我用我的 Veo 视频玩电子游戏吧。Google 做出来的可玩世界模型太棒了，拼写是 w-e-n 问号？’然后你引用转发说：‘那岂不是很有意思？’那么，用 AI 构建游戏世界有多难？也许你能展望一下未来 5 到 10 年的电子游戏？你觉得会是什么样子？

I have to talk to you about video games. So, you were being a bit trolly. I think you're having more and more fun on Twitter on X, which is great to see. So, a guy named Jimmy Apples tweeted, 'Let me play a video game of my Veo videos already. Google cooked so good playable world models when spelled we n question mark.' And then you quote tweeted that with, 'Now, wouldn't that be something?' So, how hard is it to build game worlds with AI? Maybe can you look out into the future of video games 5 or 10 years out? What do you think that looks like?

嗯，游戏确实是我的初恋，为游戏做 AI 是我十几岁时做的第一份专业工作，也是我构建的第一个主要 AI 系统。我一直想有一天再回到那里，满足那个心愿。

Well, games were my first love really, and doing AI for games was the first thing I did professionally in my teenage years, and was the first major AI systems that I built. And I always want to scratch that itch one day and come back to that.

我常常梦想，如果我在 90 年代就能拥有今天这样的 AI 系统，我会做出什么。我觉得可以打造出绝对令人惊叹的游戏。下一步，我一直喜欢制作开放世界游戏。这类游戏有一个模拟环境，里面有 AI 角色，玩家与之互动，模拟会适应玩家的玩法。我一直觉得这是最酷的游戏，比如我参与制作的《主题公园》，每个人的游戏体验都是独一无二的。你是在共同创造游戏。我们设定参数和初始条件，你作为玩家沉浸其中，与模拟共同创造。但编程开放世界游戏非常困难。你需要为玩家选择的任何方向生成内容，并且无论玩家选择什么，都要保证内容引人入胜。构建像元胞自动机这样的系统一直很难，它们能产生一些涌现行为，但总是有点脆弱和有限。现在，我们可能在未来 5 到 10 年内迎来转折点，AI 系统能够真正围绕你的想象力创造，动态改变故事和叙事，无论你选择什么都能让它充满戏剧性。这就像终极的“选择你的冒险”游戏。我认为我们触手可及，想象一下一个互动版的电影，再快进 5 到 10 年，想象它会变得多好。

I think I'd sort of dream about what I would have done back in the '90s if I'd had access to the kind of AI systems we have today. I think you could build absolutely mind-blowing games. The next stage is I always used to love making open world games. They're games where there's a simulation, AI characters, and the player interacts with that simulation, which adapts to the way the player plays. I always thought they were the coolest games because, like Theme Park that I worked on, everybody's game experience would be unique. You're co-creating the game. We set up parameters and initial conditions, and you as the player are immersed and co-creating with the simulation. But it's very hard to program open world games. You need to create content whichever direction the player goes, and you want it to be compelling no matter what the player chooses. It was always quite difficult to build things like cellular automata, which created some emergent behavior, but they're always a little fragile and limited. Now we're maybe on the cusp in the next 5 to 10 years of having AI systems that can truly create around your imagination, dynamically change the story and narrative, and make it dramatic no matter what you choose. It's like the ultimate choose-your-own-adventure game. I think we're within reach if you think of an interactive version of a movie, and then wind that forward 5 to 10 years, imagine how good it's going to be.

你说了很多非常有趣的东西。首先，你描述的开放世界内置了深度个性化。这不仅仅是开放世界，你可以打开任何一扇门，门后都有东西。而是你选择打开哪扇门，以不受约束的方式定义了你看到的世界。有些游戏试图给你选择，但那只是选择的幻觉。比如我最近玩的《史丹利的寓言》，它真的只有几扇门，带你走一条叙事线。《史丹利的寓言》是个很棒的游戏，我推荐大家玩。它嘲笑了选择的幻觉，还有关于自由意志的哲学思考。但我喜欢的一个游戏是《上古卷轴：匕首雨》。我相信他们真的用了随机生成的地牢。你走进去，他们给你一种开放世界的感觉。你提到了互动性。你不需要太多互动。当你打开门，你看到的一切都是为你随机生成的。这已经是一种不可思议的体验，因为你可能是唯一看到它的人。

So you said a lot of super interesting stuff. One, the open world built into that is a deep personalization the way you've described it. So it's not just that it's open world like you can open any door and there'll be something there. It's that the choice of which door you open in an unconstrained way defines the worlds you see. Some games try to do that to give you choice, but it's really just an illusion of choice. Like Stanley Parable, I recently played. It's really just a couple of doors and it takes you down a narrative. Stanley Parable is a great video game, I recommend people play it. It mocks the illusion of choice and has philosophical notions of free will. But I do like one of my favorite games, Elder Scrolls: Daggerfall. I believe they really played with random generation of dungeons. You can step in and they give you this feeling of an open world. You mentioned interactivity. You don't need to interact that much. When you open the door, whatever you see is randomly generated for you. That's already an incredible experience because you might be the only person to ever see that.

没错。但你想要的是比随机生成更好一点，也比简单的 A/B 硬编码选择更好。那并不是真正的开放世界，正如你所说，它只是给你选择的幻觉。你想要的是在那个游戏环境中能够做任何事。我认为唯一的方法是拥有即时生成的系统。当然，你不能创造无限的游戏资产；现在的 3A 游戏制作已经够贵了。这在 90 年代我制作这些游戏时就很明显了。我认为《黑与白》是我参与制作的游戏，早期阶段，它可能仍然拥有最好的 AI 学习 AI。那是一个早期的强化学习系统。你照顾这个神话生物，培养它。根据你对待它的方式，它会以同样的方式对待那个世界里的村民。如果你对它刻薄，它就会刻薄。如果你对它好，它就会保护。所以它真的反映了你的玩法。实际上，我职业生涯初期通过游戏进行的模拟和 AI 工作，以及我今天所做的一切，仍然是那些早期更硬编码的 AI 方式的延续，直到现在完全通用的学习系统，它们试图实现同样的目标。

Exactly. But what you'd like is a little better than just random generation, and also better than a simple A/B hardcoded choice. That's not really open world, as you say, it's just giving you the illusion of choice. What you want is to be able to do potentially anything in that game environment. I think the only way to do that is to have generated systems that generate on the fly. Of course, you can't create infinite amounts of game assets; it's expensive enough already how AAA games are made today. That was obvious to us back in the '90s when I was working on all these games. I think Black & White was the game I worked on, early stages, that had the still probably the best AI learning AI in it. It was an early reinforcement learning system. You looked after this mythical creature, growing and nurturing it. Depending on how you treated it, it would treat the villagers in that world the same way. If you were mean to it, it would be mean. If you were good, it would be protective. So it was really a reflection of the way you played. Actually, all of my work on simulations and AI through games at the beginning of my career, and really the whole of what I do today, is still a follow-on from those early more hardcoded ways of doing AI to now fully general learning systems that are trying to achieve the same thing.

看着你和埃隆显然都渴望创造游戏，这很有趣、好笑又好玩，因为你们都是游戏玩家。你在许多科学领域取得了令人难以置信的成功，比如严肃的成人事物，但可悲的一点是，你可能没有时间真正去创造一款游戏。你最终可能会创造工具，让别人来创造游戏。你不得不看着别人创造你一直梦想的东西。你认为有可能在你极其繁忙的日程中，找到时间创造像《黑与白》这样的东西吗？一款真正的电子游戏，让你童年的梦想成真？

It's been interesting, hilarious, and fun to watch you and Elon obviously itching to create games because you're both gamers. One of the sad aspects of your incredible success in so many domains of science, like serious adult stuff, is that you might not have time to really create a game. You might end up creating the tooling that others would use to create the game. You have to watch others create the thing you've always dreamed of. Do you think it's possible you can somehow in your extremely busy schedule actually find time to create something like Black & White, an actual video game where you could make the childhood dream become reality?

有两种思考方式。也许随着氛围编程变得更好，我有可能在业余时间做到这一点。我很兴奋，如果我有时间做一些氛围编程，那会是我的项目。我真的很想这么做。另一件事是，也许在 AGI 被安全地引入世界之后，我会休个假。那和我的物理学理论工作，就像我们一开始讨论的。那将是我的两个后 AGI 项目。我们就这么称呼吧。

There are two ways to think about that. Maybe with vibe coding as it gets better, there's a possibility that I could do that in my spare time. I'm quite excited about that as a project if I got the time to do some vibe coding. I'm actually itching to do that. The other thing is, maybe it's a sabbatical after AGI has been safely stewarded into the world. That and working on my physics theory, as we talked about at the beginning. Those would be my two post-AGI projects. Let's call it that way.

我很想看看你在后 AGI 时代会选择哪款游戏。解决人类历史上一些最聪明的人争论的问题，比如 P vs NP，或者制作一个酷炫的电子游戏。

I would love to see which game post-AGI you choose. Solving the problem that some of the smartest people in human history contended with, like P vs NP, or creating a cool video game.

嗯，在我的世界里，它们是相关的，因为那会是一个尽可能逼真的开放世界模拟游戏。宇宙是什么？这指向同一个问题，对吧？P vs NP。我认为所有这些事情都是相关的，至少在我的脑海里是这样。

Well, in my world they'd be related because it would be an open world simulated game as realistic as possible. What is the universe? That's speaking to the same question, right? P vs NP. I think all these things are related, at least in my mind.

我是说，从非常严肃的角度来看，电子游戏有时被贬低为只是一种有趣的消遣活动。但随着 AI 越来越多地承担那些困难、乏味的任务——也就是我们在现代世界称之为“工作”的东西——电子游戏可能成为我们寻找意义、寻找如何度过时间的方式。你可以创造出极其丰富、有意义的体验。这其实就是人类生活的本质。而在电子游戏中，你可以创造出更复杂、更多样的生活方式。

I mean in a really serious way, like video games are sometimes looked down upon as just this fun side activity. But especially as AI does more and more of the difficult, boring tasks—something we in the modern world call work—video games are the thing in which we may find meaning, in which we may find what to do with our time. You could create incredibly rich, meaningful experiences. Like that's what human life is. And then in video games, you can create more sophisticated, more diverse ways of living.

对吧？我也这么认为。那些热爱游戏的人，包括我，它几乎能让你的想象力自由驰骋。我以前非常喜欢游戏，也喜欢做游戏，因为它是融合——尤其是在 90 年代和 21 世纪初，也许是 80 年代游戏行业的黄金时代——一切都在被探索，新的游戏类型不断被发现。我们不仅仅是在制作游戏，我们感觉自己在创造一种前所未有的新娱乐媒介，尤其是那些开放世界游戏和模拟游戏，玩家作为共同创作者参与故事。没有其他媒体，娱乐媒体，能做到这一点，让观众真正共同创作故事。当然，现在还有多人在线游戏，它可以是一种非常社交的活动，可以在其中探索各种有趣的世界。但另一方面，享受和体验现实世界也非常重要。但问题在于，我认为我们将不得不再次面对一个根本问题：现实的本质是什么？这些越来越逼真的模拟、多人在线游戏，和我们现实世界中的活动，区别究竟在哪里？

Right? I think so. I mean, those of us who love games, and I still do, it's almost can let your imagination run wild, right? Like I used to love games and working on games so much because it's the fusion—especially in the '90s and early 2000s, the sort of golden era maybe the '80s of the games industry—and it was all being discovered, new genres were being discovered. We weren't just making games; we felt we were creating a new entertainment medium that never existed before, especially with these open world games and simulation games where you as the player were co-creating the story. There's no other media, entertainment media, where you do that, where you as the audience actually co-create the story. And of course now with multiplayer games as well, it can be a very social activity and can explore all kinds of interesting worlds in that. But on the other hand, it's very important to also enjoy and experience the physical world. But the question is then, I think we're going to have to kind of confront the question again of what is the fundamental nature of reality? What is going to be the difference between these increasingly realistic simulations and multiplayer ones and what we do in the real world?

是的，体验现实世界、大自然显然有巨大的价值。像我们今天这样直接面对面地体验他人，也有巨大的价值。

Yeah, there's clearly a huge amount of value to experiencing the real world, nature. There's also a huge amount of value in experiencing other humans directly in person, the way we're sitting here today.

但我们需要真正科学严谨地回答为什么。

But we need to really scientifically rigorously answer the question why.

是的。以及其中的哪些方面可以映射到虚拟世界。

Yeah. And which aspect of that can be mapped into the virtual world.

没错。仅仅说‘你应该去接触自然、亲近自然’是不够的。关键在于，为什么那是有价值的？

Exactly. It's not enough to say, 'Yeah, you should go touch grass and hang out in nature.' It's like, why exactly is that valuable?

是的。我想这可能就是从我职业生涯开始就一直萦绕在我心头、让我着迷的东西。如果你想想我做过的所有不同事情，它们都以那种方式相关联。这种模拟、现实的本质，以及什么可以被建模的边界。

Yes. And I guess that's maybe the thing that's been haunting me, obsessing me from the beginning of my career. If you think about all the different things I've done, they're all related in that way. This simulation, nature of reality, and what is the bounds of what can be modeled.

抱歉问个有点傻的问题，但到目前为止，有史以来最伟大的电子游戏是什么？你心目中的是哪个？

Sorry for the ridiculous question, but so far, what is the greatest video game of all time? What's up there?

嗯，我历来最喜欢的游戏是《文明》。我得说，《文明 1》和《文明 2》是我有史以来最喜欢的游戏。

Well, my favorite one of all time is Civilization. I have to say that, that was Civilization 1 and Civilization 2. My favorite games of all time.

我只能假设你避开了最新的一代，因为它可能会让你消失去休假。

I can only assume you've avoided the most recent one because it would probably be your sabbatical that you would disappear.

是的，没错。这些《文明》游戏非常耗时，所以我得小心对待。

Yes, exactly. They take a lot of time, these Civilization games. So, I got to be careful with them.

有趣的问题。你和埃隆似乎都是资深玩家。擅长游戏和成为优秀的 AI 公司领导者之间有关联吗？

Fun question. You and Elon seem to be somehow solid gamers. Is there a connection between being great at gaming and being great leaders of AI companies?

我不知道。这是个有趣的问题。我的意思是，我们都热爱游戏，有趣的是他一开始也写过游戏。这可能尤其与我成长的时代有关，当时家用电脑刚刚兴起，你知道，在 80 年代末和 90 年代，尤其是在英国。我有一台 Spectrum，然后是一台 Commodore Amiga 500，那是我最喜欢的电脑，也是我学习所有编程的原因。当然，编程最有趣的事情就是编写游戏。所以我认为这是学习编程的好方法，可能现在依然如此。然后我自然地将它引向了 AI 和模拟的方向，这样我就能将对游戏的兴趣和更广泛的科学兴趣结合在一起。最后，我认为游戏的一大优点是它融合了艺术设计和最前沿的编程。同样，在 90 年代，所有最有趣的技术进步都发生在游戏领域，无论是 AI、图形、物理引擎、硬件，甚至 GPU 最初也是为游戏设计的。所以 90 年代推动计算发展的所有东西都归功于游戏。有趣的是，那里正是研究的前沿，并且与艺术、图形、音乐以及全新的叙事媒体进行了令人难以置信的融合。我喜欢这一点。对我来说，这种跨学科的努力是我一生都享受的事情。

I don't know. It's an interesting one. I mean, we both love games and it's interesting he wrote games as well to start off with. It's probably especially in the era I grew up in where home computers just became a thing, you know, in the late '80s and '90s, especially in the UK. I had a Spectrum and then a Commodore Amiga 500, which is my favorite computer ever, and that's why I learned all my programming. And of course it's a very fun thing to program is to program games. So I think it's a great way to learn programming, probably still is. And then of course I immediately took it in directions of AI and simulations, which so I was able to express my interest in games and my sort of wider scientific interests all together. And then the final thing I think that's great about games is it fuses artistic design, art, with the most cutting edge programming. So again, in the '90s, all of the most interesting technical advances were happening in gaming, whether that was AI, graphics, physics engines, hardware, even GPUs of course were designed for gaming originally. So everything that was pushing computing forward in the '90s was due to gaming. So interestingly that was where the forefront of research was going on and it was this incredible fusion with art, graphics, but also music and just the whole new media of storytelling. And I love that. For me, this sort of multi-disciplinary effort is again something I've enjoyed my whole life.

我不得不问你，我差点忘了众多成果中的一个，我认为最近最令人难以置信的事情之一，但不知何故还没有得到足够关注的是 AlphaEvolve。我们之前谈过一点进化，但它是谷歌 DeepMind 的一个系统，可以进化算法。这类类似进化的技术作为未来超级智能系统的一个组成部分有前景吗？对于不了解的人来说，它有点像——我不知道这样说是否公平——它是 LLM 引导的进化搜索。

I have to ask you, I almost forgot about one of the many, and I would say one of the most incredible things recently that somehow didn't yet get enough attention is AlphaEvolve. We talked about evolution a little bit, but it's the Google DeepMind system that evolves algorithms. Are these kinds of evolution-like techniques promising as a component of future superintelligence systems? So for people who don't know, it's kind of, I don't know if it's fair to say it's LLM-guided evolution search.

是的。所以进化算法在进行搜索，而 LLM 在告诉你在哪里搜索。没错。所以 LLM 提出一些可能的解决方案，然后你在其基础上使用进化计算来找到搜索空间中的新区域。所以实际上，我认为这是一个非常有前景的方向的例子，将 LLM 或基础模型与其他计算技术相结合。进化方法是一种，但你也可以想象蒙特卡洛搜索，基本上许多类型的搜索算法或推理算法，在基础模型之上或以其为基础。所以我认为，通过这些所谓的混合系统，可能有很多有趣的东西有待发现。

Yeah. So evolutionary algorithms are doing the search and LLMs are telling you where. Yes. Exactly. So LLMs are kind of proposing some possible solutions and then you use evolutionary computing on top to find some novel part of the search space. So actually I think it's an example of very promising directions where you combine LLMs or foundation models with other computational techniques. Evolutionary methods is one, but you could also imagine Monte Carlo search, basically many types of search algorithms or reasoning algorithms on top of or using the foundation models as a basis. So I actually think there's quite a lot of interesting things to be discovered probably with these sort of hybrid systems, let's call them.

但不要浪漫化进化。是的，我只是个人。但你认为那个机制本身是有价值的，因为我们之前讨论过自然系统。你认为我们理解、建模、模拟进化，然后利用我们对这种受自然启发的机制的理解，来不断改进搜索，这其中有很多容易实现的价值吗？

But not to romanticize evolution. Yeah, I'm only human. But you think there's some value in whatever that mechanism is because we already talked about natural systems. Do you think there's a lot of low-hanging fruit of us understanding, being able to model, being able to simulate evolution, and then using that, whatever we understand about that nature-inspired mechanism, to then do search better and better?

是的。所以如果你再想想我们构建的这类系统，分解到它们真正核心的基础，你会得到系统底层动态的模型。

Yes. So if you think about again breaking down the sort of systems we've built to their really fundamental core, you've got like the model of the underlying dynamics of the system.

如果你想发现一些新的、前所未见的东西，那么你需要在顶层加上某种搜索过程，带你进入搜索空间的新区域。你可以通过多种方式做到这一点。进化计算是一种。在 AlphaGo 中，我们只用了蒙特卡洛树搜索，正是它找到了第 37 手，一种在围棋中从未见过的新策略。这就是如何超越已知知识的方法。模型可以模拟你目前所知的一切，你拥有的所有数据，但如何超越这些呢？这开始涉及创造力的概念：这些系统如何创造新事物、发现新事物？显然，这对于科学发现或推动医学进步至关重要，这正是我们希望用这些系统做到的。你可以在这些模型之上附加一些相当简单的搜索系统，从而进入新的空间区域。当然，你还必须确保不是完全随机地搜索那个空间，那样会太大。所以你需要有一个目标函数来优化并爬山，引导搜索。但在程序空间中，有一些进化机制很有趣。程序空间是一个极其重要的空间，因为你可能可以泛化到你所知道的一切，例如变异。

And then if you want to discover something new, something novel that hasn't been seen before, then you need some kind of search process on top to take you to a novel region of the search space. You can do that in a number of ways. Evolutionary computing is one. With AlphaGo we just use Monte Carlo tree search, and that's what found move 37, the new kind of never-seen-before strategy in Go. So that's how you can go beyond potentially what is already known. The model can model everything that you currently know, all the data that you currently have, but then how do you go beyond that? That starts to speak about the ideas of creativity: how can these systems create something new, discover something new? Obviously this is super relevant for scientific discovery or pushing medicine forward, which we want to do with these systems. You can actually bolt on some fairly simple search systems on top of these models and get into a new region of space. Of course, you also have to make sure that you're not searching that space totally randomly; it would be too big. So you have to have some objective function that you're trying to optimize and hill climb towards, and that guides the search. But there are some mechanisms of evolution that are interesting, maybe in the space of programs. The space of programs is an extremely important space because you can probably generalize to everything you know, for example mutation.

你可以偶尔把东西组合起来。

You could every once in a while combine things.

组合东西，改变事物的子组件。是的。所以你知道，进化真正擅长的不仅仅是自然选择。它是组合事物并构建越来越复杂的层级系统。

Combine things, alter subcomponents of a thing. Yes. So you know, what evolution is really good at is not just natural selection. It's combining things and building increasingly complex hierarchical systems.

所以那个组件非常有趣，尤其是在程序空间中的 AlphaEvolve。

So that component is super interesting, especially with AlphaEvolve in the space of programs.

是的，没错。所以你可以从进化系统中获得一些额外的特性，即一些新的涌现能力可能会出现，就像生命那样。有趣的是，对于没有大语言模型和现代 AI 的传统进化计算方法，问题在于它们在 90 年代和 21 世纪初被研究得很透彻，并显示出一些有希望的结果，但它们永远无法解决如何进化出新特性、新涌现能力的问题。你总是得到你放入系统的那部分特性的子集。但也许如果我们把它们与这些基础模型结合起来，或许可以克服这个限制。显然，自然进化做到了，因为它确实进化出了新能力，对吧？从细菌到我们现在。所以很明显，进化系统必须能够生成新的模式，回到我们讨论的第一件事，以及新的能力和涌现属性。也许我们正处于发现如何做到这一点的边缘。

Yeah, exactly. So you can get a bit of an extra property out of evolutionary systems, which is that some new emergent capability may come about, of course like happened with life. Interestingly, with naive traditional evolutionary computing methods without LLMs and modern AI, the problem was they were very well studied in the 90s and early 2000s and showed some promising results, but they could never work out how to evolve new properties, new emergent properties. You always had a sort of subset of the properties that you put into the system. But maybe if we combine them with these foundation models, perhaps we can overcome that limitation. Obviously natural evolution clearly did, because it did evolve new capabilities, right? From bacteria to where we are now. So clearly it must be possible with evolutionary systems to generate new patterns, going back to the first thing we talked about, and new capabilities and emergent properties. And maybe we're on the cusp of discovering how to do that.

是啊，听着，AlphaEvolve 是我见过的最酷的东西之一。在我家里的桌子上，大部分时间我都在电脑前编程。三个屏幕旁边放着一个提塔利克鱼的颅骨，那是早期从水里爬到陆地上的生物之一。我就那么看着那个小家伙。就像，无论进化的计算机制是什么，它都相当不可思议。真的，真的不可思议。现在，这是否正是我们进行搜索所需要的东西，但永远不要低估自然的力量以及它在这里所做的事情。

Yeah, listen, AlphaEvolve is one of the coolest things I've ever seen. On my desk at home, most of my time is spent behind computers just programming. And next to the three screens is a skull of a Tiktaalik, which is one of the early organisms that crawled out of the water onto land. And I just kind of watch that little guy. It's like, whatever the computation mechanism of evolution is, it's quite incredible. It's truly truly incredible. Now whether that's exactly the thing we need to do to do our search, but never dismiss the power of nature and what it did here.

是的。这太神奇了，它实际上是一个相对简单的算法，却能生成所有这些巨大的复杂性，显然是在 40 亿年的时间里运行的。但你可以把它看作是一个搜索过程，在宇宙的物理基质上运行了很长的计算时间，然后生成了所有这些令人难以置信的丰富多样性。

Yeah. And it's amazing, which is a relatively simple algorithm effectively, and it can generate all of this immense complexity emerges, obviously running over 4 billion years of time. But you can think about that as again a search process that ran over the physics substrate of the universe for a long amount of computational time, but then it generated all this incredible rich diversity.

我想问你的问题太多了。但有一个，你确实有一个梦想。你想尝试建模的自然系统之一是细胞。那是一个美丽的梦想。我可以问你关于那个的问题。我也为了那个目的，从 AI 科学家的角度广泛地谈一下。有一篇来自 Daniel Kokotajlo、Scott Alexander 等人的文章，概述了通往 ASI 的步骤，里面有很多有趣的想法。其中之一包括一个超人类编码员和一个超人类 AI 研究员。其中有一个术语叫研究品味，非常有趣。那么在你所看到的一切中，你认为 AI 系统是否可能拥有研究品味，像 AI 联合科学家那样帮助你，引导人类杰出的科学家，然后可能自己找出你想要产生真正新颖想法的方向？因为这似乎是做好科学的一个非常重要的组成部分。

So many questions I want to ask you. But one, you do have a dream. One of the natural systems you want to try to model is a cell. That's a beautiful dream. I could ask you about that. I also just for that purpose on the AI scientist front broadly. So there's an essay from Daniel Kokotajlo, Scott Alexander, and others that outlines steps along the way to get to ASI and has a lot of interesting ideas in it. One of which is including a superhuman coder and a superhuman AI researcher. And in that, there's a term of research taste that's really interesting. So in everything you've seen, do you think it's possible for AI systems to have research taste to help you in the way that AI co-scientist does, to help steer human brilliant scientists, and then potentially by itself to figure out what are the directions where you want to generate truly novel ideas? Because that seems to be a really important component of how to do great science.

是的，我认为这将是最难模仿或建模的事情之一：这种品味或判断力的概念。我认为这正是区分伟大科学家和优秀科学家的东西。所有专业科学家在技术上都很优秀，否则他们不会在学术界走那么远。但你是否具备品味，嗅出正确的方向、正确的实验、正确的问题是什么？所以选择正确的问题是科学中最难的部分。以及做出正确的假设。这正是当今系统绝对无法做到的。所以我常说，提出一个猜想，一个真正好的猜想，比解决它更难。所以我们可能很快就会有能够解决相当困难猜想的系统。你知道，IMO 奥林匹克问题，去年 AlphaProof 在这些非常难的问题上获得了银牌。也许最终我们将能够解决千禧年大奖之类的问题。但是一个系统能否提出一个值得研究的猜想，让像陶哲轩这样的人会说，‘你知道吗，这是一个关于数学本质或数字本质或物理本质的非常深刻的问题’？那要难得多，是一种创造力，而我们并不真正——系统显然做不到，我们也不太确定那会是什么机制。这种想象力的飞跃，就像爱因斯坦在提出狭义相对论和广义相对论时，凭借他当时的知识所做到的。

Yeah, I think that's going to be one of the hardest things to mimic or model: this idea of taste or judgment. I think that's what separates the great scientists from the good scientists. All professional scientists are good technically, otherwise they wouldn't have made it that far in academia. But then do you have the taste to sniff out what the right direction is, what the right experiment is, what the right question is? So picking the right question is the hardest part of science. And making the right hypothesis. That's what today's systems definitely cannot do. So I often say it's harder to come up with a conjecture, a really good conjecture, than it is to solve it. So we may have systems soon that can solve pretty hard conjectures. You know, IMO Olympiad problems, where AlphaProof last year got a silver medal in those really hard problems. Maybe eventually we'll be able to solve a Millennium Prize kind of problem. But could a system have come up with a conjecture worthy of study that someone like Terence Tao would have gone, 'You know what, that's a really deep question about the nature of maths or the nature of numbers or the nature of physics'? That is far harder, a type of creativity, and we don't really—systems clearly can't do that, and we're not quite sure what that mechanism would be. This kind of leap of imagination like Einstein had when he came up with special relativity and then general relativity with the knowledge he had at the time.

至于猜想，你想提出一个有趣且可证明的东西。

As for conjecture, you want to come up with a thing that's interesting and amenable to proof.

是的。

Yes.

所以，提出一个极其困难的事情很容易，提出一个极其容易的事情也很容易。关键在于那个边缘，那个甜蜜点，即推动科学进步，理想情况下将假设空间一分为二，对吧？无论结果是否成立，你都能学到非常有用的东西。这很难，而且还要让假设可证伪，并在你现有技术范围内实现。所以这实际上是一个非常具有创造性的过程，我认为仅仅在模型上进行简单的搜索是不够的。

So like it's easy to come up with a thing that's extremely difficult. It's easy to come up with a thing that's extremely easy. At that very edge, that sweet spot, right, of basically advancing the science and splitting the hypothesis space into two ideally, right? Whether if it's true or not true, you've learned something really useful. And that's hard, and making something that's also falsifiable and within the technologies that you currently have available. So it's a very creative process actually, highly creative process that I think just a kind of naive search on top of a model won't be enough for that.

好的。将假设空间一分为二的想法非常有趣。我听过你说，基本上没有失败，或者如果你把问题构建好、把实验构建好、把设计做好，失败是极其有价值的，失败和成功都有用。所以也许是因为它把假设一分为二，就像二分搜索一样。

Okay. The idea of splitting the hypothesis space in two is super interesting. So I've heard you say that there's basically no failure, or failure is extremely valuable if it's done, if you construct the questions right, if you construct the experiments right, if you design them right, that failure and success are both useful. So perhaps because it splits the hypothesis basically in two, it's like a binary search.

没错。所以当你做真正的基础研究时，只要你在选择能有效分割假设空间的实验和假设，就没有真正的失败。你能学到东西，从一个不成功的实验中也能学到同样有价值的东西。如果你实验设计得好，假设有趣，它会告诉你很多下一步该怎么做。然后你实际上是在进行一个搜索过程，并以非常有用的方式利用这些信息。

That's right. So when you do real blue sky research, there's no such thing as failure really, as long as you're picking experiments and hypotheses that meaningfully split the hypothesis space. So you learn something, you can learn something equally valuable from an experiment that doesn't work. That should tell you, if you've designed the experiment well and your hypotheses are interesting, it should tell you a lot about where to go next. And then you're effectively doing a search process and using that information in very helpful ways.

那么回到你模拟细胞的梦想，要实现它，我们面临哪些重大挑战？我们或许应该强调，可以说 AlphaFold 解决了蛋白质折叠问题，还有很多令人惊叹的事情可以谈，包括你们开源的一切。AlphaFold 3 正在处理蛋白质、RNA、DNA 的相互作用，这非常复杂且迷人，适合建模。AlphaGenome 预测小的遗传变化（如单点突变）如何与实际功能相关联。所以这些似乎正逐步逼近更复杂的事物，比如细胞，但细胞有很多非常复杂的组成部分。

So to go to your dream of modeling a cell, what are the big challenges that lay ahead for us to make that happen? We should maybe highlight that AlphaFold solved, if it's fair to say, protein folding, and there's so many incredible things we could talk about there including the open sourcing everything you've released. AlphaFold 3 is doing protein, RNA, DNA interactions, which is super complicated and fascinating. That's amenable to modeling. AlphaGenome predicts how small genetic changes, like single mutations, link to actual function. So those seem like it's creeping along to more complicated things like a cell, but a cell has a lot of really complicated components.

是的。在我的职业生涯中，我一直试图这样做：我有一些非常宏大的梦想，然后我试着把它们分解。拥有一个疯狂而雄心勃勃的梦想很容易，但诀窍在于如何将其分解为可管理、可实现且本身就有意义和用处的中间步骤。所以虚拟细胞，也就是我所说的模拟细胞的项目，我有这个想法大概已经 25 年了。我经常和保罗·纳斯讨论，他算是我在生物学方面的导师。他领导弗朗西斯·克里克研究所，并在 2001 年获得了诺贝尔奖。我们从 90 年代就开始讨论，我每 5 年都会重新审视：要模拟一个细胞的完整内部，需要什么？这样你就可以在虚拟细胞上进行实验，这些计算机实验能帮你节省大量湿实验时间。这就是梦想：也许你可以将实验速度提高 100 倍，大部分在计算机中搜索，然后在湿实验室中验证。所以也许现在终于，我一直在构建这些组件，AlphaFold 是其中之一，它最终能让你模拟完整的相互作用，即细胞的完整模拟。我可能会从酵母细胞开始，部分原因是保罗·纳斯研究的就是它。酵母细胞是一个完整的单细胞生物，所以它是最简单的单细胞生物，而且它不仅仅是一个细胞，它是一个完整的生物体。酵母被研究得很透彻，所以它是完整模拟模型的好候选。现在 AlphaFold 解决了蛋白质 3D 结构的静态图像问题。但我们知道，在生物学中，所有有趣的事情都发生在动态和相互作用中，而 AlphaFold 3 正是朝着模拟这些相互作用迈出的第一步。所以首先是成对相互作用：蛋白质与蛋白质、蛋白质与 RNA 和 DNA。但下一步可能是模拟整个通路，比如涉及癌症的通路之类的。然后最终你也许能模拟整个细胞。

Yeah. So what I've tried to do throughout my career is I have these really grand dreams and then I try to break them down. It's easy to have a crazy ambitious dream, but the trick is how do you break it down into manageable, achievable interim steps that are meaningful and useful in their own right. So virtual cell, which is what I call the project of modeling a cell, I've had this idea of wanting to do that for maybe more like 25 years. I used to talk with Paul Nurse, who is a bit of a mentor of mine in biology. He runs the Francis Crick Institute and won the Nobel Prize in 2001. We've been talking about it since the '90s, and I used to come back to it every 5 years: what would you need to model the full internals of a cell so that you could do experiments on the virtual cell, and those experiments in silico would be useful for you to save a lot of time in the wet lab? That would be the dream: maybe you could 100x speed up experiments by doing most of it in silico, the search in silico, and then you do the validation step in the wet lab. So maybe now finally, I was trying to build these components, AlphaFold being one, that would allow you eventually to model the full interaction, a full simulation of a cell. I'd probably start with a yeast cell, partly because that's what Paul Nurse studied. A yeast cell is like a full organism that's a single cell, so it's the simplest single-cell organism, and it's not just a cell, it's a full organism. Yeast is very well understood, so that would be a good candidate for a full simulated model. Now AlphaFold is the solution to the static picture of what a protein looks like in 3D structure. But we know that in biology, all the interesting things happen with the dynamics, the interactions, and that's what AlphaFold 3 is: the first step towards modeling those interactions. So first, pairwise interactions: proteins with proteins, proteins with RNA and DNA. But then the next step after that would be modeling maybe a whole pathway, like the pathway involved in cancer or something like that. And then eventually you might be able to model a whole cell.

还有另一个复杂性：细胞中的事件发生在不同的时间尺度上。这棘手吗？比如蛋白质折叠非常快，但有些机制需要很长时间。所以相互作用的层次有不同的时间尺度，你必须能够建模。

Also, there's another complexity here: stuff in a cell happens at different time scales. Is that tricky? Like protein folding is super fast, but some mechanisms take a long time. So the levels of interaction have different temporal scales that you have to be able to model.

那会很困难。所以你可能需要几个模拟系统，它们能在这些不同的时间动态下相互作用，或者至少是一个层次系统。这样你可以在不同的时间阶段上下跳跃。这里的挑战之一是避免过度建模，例如模拟其中的量子力学方面。你想要跳过，只建模真正高层次的东西，从而得到对将要发生事情的很好估计。所以在建模任何自然系统时，你必须做出决定：你要建模到哪个粒度级别，才能捕捉到你感兴趣的动力。所以对于细胞，我希望是蛋白质级别，而不必深入到原子级别。当然，这正是 AlphaFold 发挥作用的地方。所以那会是基础，然后你构建这些更高级别的模拟，以它们为构建块，然后得到涌现行为。

So that would be hard. So you'd probably need several simulated systems that can interact at these different temporal dynamics, or at least maybe it's like a hierarchical system. So you can jump up and down the different temporal stages. One of the challenges here is to avoid over-modeling, for example, simulating the quantum mechanical aspects of any of this. You want to skip ahead to just model the really high-level things that get you a really good estimate of what's going to happen. So you've got to make a decision when modeling any natural system: what is the cutoff level of granularity that you're going to model it to, that then captures the dynamics that you're interested in. So probably for a cell, I would hope that would be the protein level, and that one wouldn't have to go down to the atomic level. Of course, that's where AlphaFold kicks in. So that would be kind of the basis, and then you'd build these higher-level simulations that take those as building blocks, and then you get the emergent behavior.

提前为这些深奥的问题道歉，但我们能否模拟和建模生命的起源？即从非生命物质模拟出第一个生命体的诞生。

Apologize for the pthead questions ahead of time, but will we be able to simulate and model the origin of life? So being able to simulate the first from non-living organisms, the birth of a living organism.

我认为这是最深刻、最迷人的问题之一。我热爱生物学领域。尼克·莱恩（Nick Lane）这位顶尖专家写了一本很棒的书《进化的十大发明》。这本书非常精彩，也谈到了大过滤器可能是什么，是在过去还是在我们前面。我认为它们很可能在过去。如果你读那本书，就会发现产生任何生命都极其不可能，而从单细胞到多细胞的飞跃似乎大得难以置信，在地球上花了大约十亿年。这说明这有多难。

I think that's one of the deepest and most fascinating questions. I love that area of biology. There's a great book by Nick Lane, one of the top experts, called The 10 Great Inventions of Evolution. It's fantastic and speaks to what the great filters might be, whether they're in the past or ahead of us. I think they're most likely in the past. If you read that book, it shows how unlikely it is to have any life at all, and then the jump from single cell to multicell seems unbelievably big, taking about a billion years on Earth. That shows how hard it was.

细菌在捕获线粒体之前，很长一段时间都非常快乐。

Bacteria were super happy for a very long time before they captured mitochondria somehow.

我不明白为什么人工智能不能通过某种模拟来帮助解决这个问题。这是一个在组合空间中搜索的过程。这里有化学汤，热液喷口附近的原始汤。这里有一些初始条件。你能生成一个看起来像细胞的东西吗？这可能是虚拟细胞项目之后的下一阶段：这样的东西如何从化学汤中涌现出来？

I don't see why AI couldn't help with that, through some kind of simulation. It's a search process through a combinatorial space. Here's the chemical soup, the primordial soup near hot vents. Here are some initial conditions. Can you generate something that looks like a cell? That could be a next stage after the virtual cell project: how could something like that emerge from the chemical soup?

嗯，如果生命起源也有一个“第 37 手”，我会很高兴。

Well, I would love it if there was a move 37 for the origin of life.

我认为这是最大的谜团之一。我认为最终我们会发现存在一个连续统。非生命和生命之间没有界限。如果我们能严格证明这一点，从大爆炸到今天的一切都是同一个过程。如果我们能打破我们心中构建的那道墙，即从非生命到生命的实际起源，它不是一条线，而是一个连接物理、化学和生物的连续统。没有界限。

I think that's one of the great mysteries. I think ultimately we will figure out there's a continuum. There's no line between non-living and living. If we can make that rigorous, the very thing from the Big Bang to today has been the same process. If we can break down that wall we've constructed in our minds of the actual origin from non-living to living, it's not a line but a continuum that connects physics, chemistry, and biology. There's no line.

这就是我一生致力于人工智能和 AGI 的全部原因。我认为它可以成为帮助我们回答这类问题的终极工具。我不明白为什么普通人不多担心这些事情。我们怎么能没有一个好的生命和非生命的定义，没有时间本质的定义，更不用说意识、引力和量子力学的怪异了？这对我来说一直像在尖叫，而且越来越响。就像在问：这到底是怎么回事？我是在最深刻的意义上说的：现实的本质，这一定是能回答所有这一切的终极问题。想想看，我们可以互相凝视，观察所有生物，用显微镜检查，把它们拆解到几乎原子级别，却仍然无法清楚地回答如何定义生命，这太疯狂了。

This is my whole reason for working on AI and AGI my whole life. I think it can be the ultimate tool to help us answer these kinds of questions. I don't understand why the average person doesn't worry about this stuff more. How can we not have a good definition of life and non-living, the nature of time, let alone consciousness, gravity, and quantum mechanics weirdness? It's always been screaming at me, and it's getting louder. It's like, what is going on here? I mean that in the deepest sense: the nature of reality, which has to be the ultimate question that would answer all these things. It's crazy that we can stare at each other and all these living things, inspect them with microscopes, take them apart almost to the atomic level, and yet we still can't answer clearly how to define living.

是的，这有点神奇。生命你可以通过谈论来逃避思考，但意识——我们显然有主观的意识体验，我们处于自己世界的中心，它感觉起来像某种东西。你怎么能不对这一切的神秘性尖叫呢？

Yeah, it's kind of amazing. Living you can kind of talk your way out of thinking about, but consciousness—we have this obviously subjective conscious experience, we're at the center of our own world, and it feels like something. How are you not screaming at the mystery of it all?

人类长期以来一直在应对周围世界的神秘。有很多谜团：太阳和雨是怎么回事？那是关于什么的？去年下了很多雨，今年没有。我们做错了什么？人类问这个问题已经很久了。

Humans have been contending with the mystery of the world around them for a long time. There are a lot of mysteries: what's up with the sun and the rain? What's that about? Last year we had a lot of rain, this year we don't. What did we do wrong? Humans have been asking that for a long time.

没错。所以我们发展了很多机制来应对这些我们能看见但无法完全理解的深层谜团。我们必须继续日常生活，让自己忙碌起来，在某种程度上分散注意力。

Exactly. So we've developed a lot of mechanisms to cope with these deep mysteries that we can see but can't fully understand. We have to get on with daily life and keep ourselves busy, in a way distracted.

天气是人类历史上最重要的问题之一。它仍然是闲聊的首选话题，尤其是在英国。而且众所周知，这是一个极其难以建模的系统。即使是这个系统，Google DeepMind 也取得了进展。我们创造了世界上最好的天气预报系统，比在大型超级计算机上运行、需要数天的传统流体动力学系统更好。我们成功地用神经网络系统（比如我们的 GraphCast 系统）模拟了许多天气动态。有趣的是，这些动态即使非常复杂、近乎混沌，也能被建模。最近我们有了飓风路径的预测。这对世界非常有用和重要，而且快速准确地完成也很重要。这是一个很有前景的方向，用于模拟和运行非常复杂的现实世界系统的前向预测。

Weather is one of the most important questions in human history. It's still the go-to small talk topic, especially in England. And famously, it's an extremely difficult system to model. Even that system, Google DeepMind has made progress on. We've created the best weather prediction systems in the world, better than traditional fluid dynamics systems that run on massive supercomputers and take days. We've managed to model a lot of weather dynamics with neural network systems, like our GraphCast system. It's interesting that those dynamics can be modeled even though they're very complicated, almost chaotic. Recently we had cyclone prediction for hurricane paths. It's super useful and important for the world, and it's important to do it quickly and accurately. It's a promising direction for simulating and running forward predictions of very complicated real-world systems.

我应该提一下，我在德克萨斯有机会遇到一个叫“风暴追逐者”的群体。他们令人难以置信的是非常精通技术，因为他们必须使用模型来预测风暴的位置。这是一种美丽的结合：疯狂到冲进风暴眼，同时利用尖端科学来保护生命和预测极端事件。他们可能实际上在使用 DeepMind 的系统。

I should mention that I got a chance in Texas to meet a community called the storm chasers. What's incredible about them is they're extremely tech-savvy because they have to use models to predict where the storm is. It's a beautiful mix of being crazy enough to go into the eye of the storm and using cutting-edge science to protect their lives and predict extreme events. They might actually be using DeepMind's system.

是的，希望如此。我很想加入他们的一次追逐。看起来太棒了，真想亲身体验一次。

Yeah, hopefully they are. I'd love to join them on one of those chases. They look amazing, to actually experience it one time.

没错。而且还能体验正确预测某物会从哪里来以及如何演变。太不可思议了。

Exactly. And also to experience the correct prediction of where something will come and how it will evolve. It's incredible.

你估计我们将在 2030 年之前拥有 AGI。围绕这一点有一些有趣的问题。我们如何真正知道我们已经达到了？AGI 的“第 37 手”可能是什么？

You've estimated that we'll have AGI by 2030. There are interesting questions around that. How will we actually know that we got there? And what might be the move 37 of AGI?

我的估计是，在未来 5 年内有大约 50%的可能性。

My estimate is sort of 50% chance by in the next 5 years.

所以说到 2030 年，我认为很有可能实现。部分原因在于你对 AGI 的定义是什么？当然，现在人们对此争论不休。我的标准一直很高：我们能否匹配大脑的认知功能？我们知道人类大脑大致上是通用的图灵机。我们用大脑创造了令人难以置信的现代文明，这也说明了大脑的通用性。要确认我们拥有了真正的 AGI，我们必须确保它具备所有那些能力。它不能是一种锯齿状智能，像今天的系统那样在某些方面非常出色，但在其他方面却有很大缺陷。这就是我们当前系统的状况——它们不一致。所以你需要智能在各个领域的一致性。然后我们还有一些缺失的能力，比如我们之前谈到的真正的发明能力和创造力。你需要看到这些。如何测试呢？我认为一种方法是对人类能完成的数万个认知任务进行暴力测试。也许还可以让系统对几百位世界顶尖专家开放，比如每个领域的陶哲轩，看看他们能否找到系统的明显缺陷。如果他们找不到，那么我认为你可以相当确信我们拥有了一个完全通用的系统。

So by 2030, let's say, I think there's a good chance that could happen. Part of it is what is your definition of AGI? Of course, people are arguing about that now. Mine's quite a high bar and always has been: can we match the cognitive functions that the brain has? We know our brains are pretty much general Turing machines, approximate. And we've created incredible modern civilization with our minds, which speaks to how general the brain is. For us to know we have a true AGI, we would have to make sure it has all those capabilities. It isn't a jagged intelligence where some things it's really good at, like today's systems, but other things it's really flawed at. That's what we currently have with today's systems—they're not consistent. So you'd want that consistency of intelligence across the board. And then we have some missing capabilities, like true invention and creativity that we were talking about earlier. You'd want to see those. How you test that? I think one way would be a brute force test of tens of thousands of cognitive tasks that humans can do. And maybe also make the system available to a few hundred of the world's top experts, the Terrence Taos of each subject area, and see if they can find an obvious flaw in the system. If they can't, then I think you can be pretty confident we have a fully general system.

也许稍微反驳一下，人类似乎真的很了不起。随着智能在所有领域的提升，我们往往会认为这是理所当然的。你提到陶哲轩——这些杰出的专家可能很快，在几周内，就会认为它能做的所有不可思议的事情都是理所当然的，然后专注于缺陷。我首先认为自己是人类。其次，我认同自己是人类。有些人听我说话，觉得我不擅长说话——结巴什么的。所以即使是人类，在跨领域也有明显的局限，甚至超出数学和物理。我想知道它是否会像围棋中的第 37 手那样带来积极的一面，而不是一万个认知任务中的一两个让人惊叹的瞬间。

Maybe to push back a little bit, it seems like humans are really incredible. As intelligence improves across all domains, we take it for granted. You mentioned Terrence Tao—these brilliant experts might quickly, in a span of weeks, take for granted all the incredible things it can do and then focus on flaws. I consider myself first of all human. Second, I identify as human. Some people listen to me talk and think I'm not good at talking—the stuttering, you know. So even humans have obvious limits across domains, even outside mathematics and physics. I wonder if it will take something like a Move 37 on the positive side versus a barrage of 10,000 cognitive tasks where it's one or two that are like 'yes, holy...'

我完全同意。所以我认为有全面测试来确保一致性，但我也在寻找像第 37 手那样的灯塔时刻。一个例子是像爱因斯坦那样提出新的物理学猜想或假说。你甚至可以非常严格地进行回溯测试：设定知识截止于 1900 年，给系统所有 1900 年之前的资料，看看它能否像爱因斯坦那样提出狭义相对论和广义相对论。那将是一个有趣的测试。另一个是：它能否发明像围棋这样的游戏？不仅仅是提出第 37 手这样的新策略，而是能否发明一种像围棋一样深奥、美学上优美、优雅的游戏？这些就是我会关注的事情。可能一个系统需要能做几件这样的事情才能被认为是通用的，而不仅仅是一个领域。那将是我们拥有 AGI 级别系统的标志。然后为了完善，你还会检查一致性，确保没有漏洞。

I think exactly. So I think there's the blanket testing to make sure you've got consistency, but I think there are lighthouse moments like Move 37 that I would be looking for. One would be inventing a new conjecture or new hypothesis about physics like Einstein did. You could even run a back test very rigorously: have a knowledge cutoff of 1900, give the system everything written up to 1900, and see if it could come up with special relativity and general relativity, like Einstein did. That would be an interesting test. Another would be: can it invent a game like Go? Not just come up with Move 37, a new strategy, but can it invent a game that's as deep, aesthetically beautiful, and elegant as Go? Those are the sorts of things I would be looking out for. Probably a system being able to do several of those things for it to be very general, not just one domain. That would be the signs that we've got a system at AGI level. Then to fill that out, you would also check the consistency, make sure there are no holes.

像新的猜想或科学发现这样的东西。那会是一种很酷的感觉。是的，那将是惊人的。所以它不仅仅是帮助我们做到这一点，而是真正提出全新的东西。

Something like a new conjecture or scientific discovery. That would be a cool feeling. Yeah, that would be amazing. So it's not just helping us do that, but actually coming up with something brand new.

而且你会亲历那个时刻。所以可能在宣布之前会有两三个月的时间。你就坐在那里，努力不发推文之类的。没错。就像，这个惊人的新物理学想法是什么？然后我们可能会与该领域的全球专家核实，验证它，并检查它的工作原理。它也会解释它的工作原理。是的，一个惊人的时刻。

And you would be in the room for that. So it would be probably 2 or 3 months before announcing it. And you would just be sitting there trying not to tweet something like that. Exactly. It's like, what is this amazing new physics idea? Then we would probably check it with world experts in that domain, validate it, and go through its workings. It would be explaining its workings too. Yeah, an amazing moment.

你是否担心我们作为人类，即使是像你这样的专家，可能会错过它？

Do you worry that we as humans, even expert humans like you, might miss it?

它可能相当复杂。我给出的类比是：我不认为它对最优秀的人类科学家来说会完全神秘，但可能有点像国际象棋。如果我和加里·卡斯帕罗夫或马格努斯·卡尔森交谈并和他们下棋，他们走出一步妙棋，我可能想不出那步棋，但他们之后可以解释为什么那步棋有意义，我们会在某种程度上理解——达不到他们的水平，但如果他们善于解释——这实际上也是智力的一部分——能够以简单的方式解释你在想什么。我认为对于最优秀的人类科学家来说，这是非常可能的。

It may be pretty complicated. The analogy I give is: I don't think it will be totally mysterious to the best human scientists, but it may be a bit like in chess. If I were to talk to Garry Kasparov or Magnus Carlsen and play a game with them, and they make a brilliant move, I might not be able to come up with that move, but they could explain afterwards why that move made sense, and we would understand it to some degree—not to the level they do, but if they were good at explaining, which is actually part of intelligence too—being able to explain in a simple way what you're thinking about. I think that would be very possible for the best human scientists.

但我想也许你可以从围棋的角度给我一些启发。我想知道是否有 AlphaGo 或加里（指李世石）的棋步，他们一开始会认为那是坏棋。

But I wonder maybe you can educate me on the side of Go. I wonder if there are moves for AlphaGo or Gary where they at first dismiss it as a bad move.

是的，当然。有可能。但之后他们会凭直觉理解为什么这步棋有效。然后经验上，游戏的好处在于你可以进行一种科学测试：它赢了吗？这告诉你那步棋是好的。然后你可以回去分析它，并对自己进一步解释原因。这就是国际象棋分析的方式。也许这就是为什么我的大脑这样工作，因为我从四岁就开始做这个——这是一种硬核训练。但即使是现在，当我生成代码时，也会发生这种微妙的争论：我可能最初认为生成的代码在某些有趣的微妙方面是不正确的，但我总是要问：这里是否有更深的洞见，而我是那个错误的人？

Yeah, sure. It could be. But then afterwards they'll figure out with their intuition why this works. And then empirically, the nice thing about games is that you can do a sort of scientific test: does it win the game or not? That tells you that move was good. Then you can go back and analyze it and explain to yourself a little bit more why. That's how chess analysis works. Perhaps that's why my brain works like that, because I've been doing that since I was four—it's sort of hardcore training. But even now, when I generate code, there is this nuanced contention happening where I might at first identify a set of generated code as incorrect in some interesting nuanced ways, but then I always have to ask: is there a deeper insight here that I'm the one who's incorrect?

随着系统变得越来越智能，你将不得不面对这个问题。就像：这是你刚刚想出的东西的一个错误还是一个特性？

And that's going to, as the systems get more and more intelligent, you're going to have to contend with that. It's like: is this a bug or a feature of what you just came up with?

是的。而且它们会变得相当复杂。

Yeah. And they're going to be pretty complicated to do.

但当然，你也可以想象 AI 系统生成代码之类的东西，然后人类程序员在 AI 工具的辅助下审查它。所以这将会很有趣，也许监控工具和生成工具是不同的 AI 工具。

But of course it will be you can imagine also AI systems that are producing that code or whatever that is and then human programmers looking at it but also not unaided with the help of AI tools as well. So it's going to be kind of an interesting you know maybe different AI tools to the ones that the more you know kind of monitoring tools to the ones that generated it.

那么如果我们看一个 AGI 系统——抱歉又提回来——但 AlphaFold 非常酷。AlphaFold 在编程方面实现了某种递归自我改进的可能性。谁能想象那个 AGI 系统，也许不是第一个版本，而是几个版本之后，会是什么样子？你认为它会很简单吗？你认为它会像一个简单的自我改进程序吗？

So if we look at a AGI system, sorry to bring it back up, but AlphaFold, super cool. So AlphaFold enables on the programming side something like recursive self-improvement potentially like what who can imagine what that AGI system maybe not the first version but a few versions beyond that. What does that actually look like? Do you think it would be simple? You think it'll be something like a self-improving program in a simple one?

我的意思是，这有可能。但我不确定这是否可取，因为那是一种硬起飞场景。但像 AlphaFold 这样的当前系统，有人类在循环中决定各种事情。它们是相互作用的独立混合系统。可以想象最终端到端地做到这一点。我不明白为什么不可能，但目前我认为系统在提出代码架构方面还不够好。这又与提出新的猜想性假设的想法有关。如果你给出非常具体的指令，它们表现很好。但如果你给出非常模糊的高层指令，目前就行不通。比如，我认为这与“发明一个像围棋一样好的游戏”有关。想象一下那个提示，那太不明确了。所以当前系统不知道如何处理，如何将其缩小到可处理的范围。类似地，比如“做一个更好的自己”也太不受约束了。但我们已经做到了，比如 AlphaFold 中更快的矩阵乘法。所以当你把它缩小到非常具体的目标时，它非常擅长渐进式改进，但目前这些更像是渐进式改进，小迭代，而如果你想要理解上的大飞跃，你需要更大的进步。

I mean, potentially that's possible. I would say I'm not sure it's even desirable because that's a kind of like hard takeoff scenario. But these current systems like AlphaFold, they have human in the loop deciding on various things. They're separate hybrid systems that interact. One could imagine eventually doing that end to end. I don't see why that wouldn't be possible but right now I think the systems are not good enough to do that in terms of coming up with the architecture of the code. And again it's a little bit connected to this idea of coming up with a new conjectural hypothesis. They're good if you give them very specific instructions about what you're trying to do. But if you give them a very vague high level instruction, that wouldn't work currently. Like, and I think that's related to this idea of like invent a game as good as Go, right? Imagine that was the prompt. That's pretty underspecified. And so the current systems wouldn't know, I think, what to do with that, how to narrow that down to something tractable. And I think there's similar like, look, just make a better version of yourself that's too unconstrained. But we've done it in, as you know with AlphaFold, like things like faster matrix multiplication. So when you hone it down to very specific thing you want it's very good at incrementally improving that but at the moment these are more like incremental improvements sort of small iterations whereas if you wanted a big leap in understanding you need a much larger advance.

是的。但这也可以反驳硬起飞场景。它可能只是一系列渐进式改进，比如矩阵乘法，它需要花几天时间思考如何渐进式改进一件事，然后递归地进行，随着改进越来越多，速度会变慢，所以通往 AGI 的道路不会是突然的，而是随着时间的推移逐渐改进。

Yeah. But it could also be sort of to push back against hard takeoff scenario. It could be just a sequence of incremental improvements like matrix multiplication like it has to sit there for days thinking how to incrementally improve a thing and that it does so recursively and as you do more and more improvement it'll slow down so there'll be like a like the path to AGI won't be like a it'll be a gradual improvement over time.

是的。如果只是渐进式改进，那就会是这样。所以问题是，它能否提出像 Transformer 架构这样的新飞跃？它能在 2017 年我们和 Brain 团队做到这一点时也做到吗？目前还不清楚像 AlphaFold 这样的系统能否做出如此大的飞跃。所以可以肯定，这些系统很好，我们有能做渐进式爬山算法的系统，但更大的问题是，从这里开始是否只需要这些，还是我们实际上还需要一两个重大突破。

Yes. If it was just incremental improvements that's how it would look. So the question is could it come up with a new leap like the Transformers architecture right could it have done that back in 2017 when we did it and Brain did it and it's not clear that these systems like AlphaFold would be able to make such a big leap. So for sure these systems are good we have systems I think that can do incremental hill climbing and that's a kind of bigger question about is that all that's needed from here or do we actually need one or two more big breakthroughs.

而且同样的系统能否也提供突破？所以它是一系列 S 曲线，既有渐进式改进，也有偶尔的飞跃。

And can the same kind of systems provide the breakthroughs also. So make it a bunch of S-curves like incremental improvement but also every once in a while leaps.

是的。我认为没有人拥有能明确展示那些大飞跃的系统。我们有很多系统在你当前所在的 S 曲线上进行爬山。

Yeah. I don't think anyone has systems that can have shown unequivocally those big leaps that are right. We have a lot of systems that do the hill climbing of the S-curve that you're currently on.

是的。那就像第 37 手是一个飞跃。

Yeah. And that would be the move 37 is a leap.

是的。我认为那会是一个飞跃。类似这样。你认为缩放定律在预训练、后训练和测试时算力方面仍然强劲吗？反过来，你是否预期 AI 进步会遇到瓶颈？

Yeah. I think would be a leap. Something like that. Do you think the scaling laws are holding strong on the pre-training, post-training, test time compute? Do you on the flip side of that anticipate AI progress hitting a wall?

我们当然觉得在 Scaling 方面还有很大空间。实际上，所有步骤——预训练、后训练和推理时间——都有空间。所以有三种 Scaling 同时发生。这又关乎你能有多创新，我们以拥有最广泛、最深厚的研究团队而自豪。我们有出色的研究人员，比如提出 Transformer 的 Noam Shazeer，领导 AlphaGo 项目的 Dave Silver 等等。正是这个研究基础意味着，如果需要像 AlphaGo 或 Transformer 这样的新突破，我相信我们会是做到的地方。所以我其实很喜欢当难度增加的时候，因为那会从纯工程转向真正的研究，以及研究加工程，那是我们的强项。而且我认为发明东西比快速跟进更难。所以我们不知道，我估计大概五五开，是需要新东西还是现有 Scaling 就足够了。因此，以真正的实证方式，我们正在尽可能推动这两方面。新的天马行空的想法，我们大约一半的资源投入其中，同时最大化 Scaling 当前能力，我们仍然看到每个不同版本的 Gemini 取得了惊人的进展。

We certainly feel there's a lot more room just in the scaling. So actually all steps pre-training, post-training and inference time. So there's sort of three scalings that are happening concurrently. And we again there it's about how innovative you can be and we pride ourselves on having the broadest and deepest research bench. We have amazing researchers and people like Noam Shazeer who came up with Transformers and Dave Silver who led the AlphaGo project and so on. And it's that research base means that if some new breakthrough is required like AlphaGo or Transformers, I would back us to be the place that does that. So I'm actually quite like it when the terrain gets harder, right? Because then it veers more from just engineering to true research and research plus engineering and that's our sweet spot. And I think it's harder to invent things than to fast follow. So we don't know I would say it's kind of 50/50 whether new things are needed or whether the scaling of existing stuff is going to be enough. And so in true kind of empirical fashion, we're pushing both of those as hard as possible. The new blue sky ideas and maybe about half our resources are on that and then scaling to the max the current capabilities and we're still seeing fantastic progress on each different version of Gemini.

你提到深厚的研究团队很有意思，如果通往 AGI 的进步不仅仅是 Scaling 算力（工程方面），而更多是需要突破的科学方面，那么你相信 DeepMind 以及 Google DeepMind 完全有能力在这个领域大展拳脚。

That's interesting the way you put it in terms of the deep bench that if progress towards AGI is more than just scaling compute so the engineering side of the problem and is more on the scientific side where there's breakthroughs needed then you feel confident DeepMind as well Google DeepMind is well positioned to kick ass in that domain.

嗯，如果你看过去十年或十五年的历史，当今现代 AI 领域的基础突破中，可能有 80-90%最初来自 Google Brain、Google Research 和 DeepMind。所以是的，我希望这种情况能继续下去。

Well I mean if you look at the history of the last decade or 15 years it's been I maybe 80-90% of the breakthroughs that underpin modern AI field today was from originally Google Brain, Google Research and DeepMind. So yeah I would back that to continue hopefully.

那么在数据方面，你是否担心高质量数据，尤其是高质量人类数据会耗尽？

So on the data side are you concerned about running out of high quality data especially high quality human data?

我不太担心，部分原因是我认为有足够的数据，而且已经证明可以让系统变得相当好。这又回到了模拟，如果你有足够的数据来制作模拟，或者你可以创建更多来自正确分布的合成数据。显然，这是关键。

I'm not very worried about that partly because I think there's enough data or and it's been proven to get the systems to be pretty good and this goes back to simulations again if you have enough data to make simulations or so that you can create more synthetic data that are from the right distribution. Obviously, that's the key.

所以，你需要足够的真实世界数据才能创建那种生成器数据生成器，我认为我们目前正处于这一步。

So, you need enough real world data in order to be able to create those kinds of generator data generators and I think that we're at that step at the moment.

是的。

Yeah.

你在科学和生物学方面做了很多了不起的工作，用不太多的数据就取得了很大成就。

You've done a lot of incredible stuff on the side of science and biology doing a lot with not so much data.

是的。

Yeah.

我的意思是，数据量仍然很大，但我想已经足够了。

I mean, it's still a lot of data, but I guess enough.

正是如此。没错。

Take off that going. Exactly. Yeah.

那么，算力的 Scaling（规模扩张）对于构建 AGI（通用人工智能）有多关键？这是一个工程问题，也几乎是一个地缘政治问题，因为其中还涉及供应链和能源——你非常关心的一个方面，可能是核聚变。所以也要在能源方面进行创新。你认为我们会继续扩大算力吗？

So exactly, how crucial is the scaling of compute to building AGI? This is a question that's an engineering question. It's almost a geopolitical question because it also integrated into that is the supply chains and energy, a thing that you care a lot about which is potentially fusion. So innovating on the side of energy also. Do you think we're going to keep scaling compute?

我认为会，原因有几个。算力方面，训练所需的算力通常需要集中部署，所以甚至数据中心之间的带宽限制也会影响这一点。因此即使在那里也有额外的限制，这对于训练你能做到的最大模型显然很重要。但此外，由于现在 AI 系统已经融入产品并被全球数十亿人使用，你需要大量的推理算力。在此基础上，还有思考系统——去年出现的新范式，即它们在测试时获得的推理时间越长，就会变得越聪明。所有这些都需要大量算力，而且我看不到放缓的迹象。随着 AI 系统变得更好，它们会更有用，需求也会更大。所以从训练方面来看，训练实际上只是其中的一部分。它甚至可能成为所需总算力中较小的一部分。

I think so for several reasons. I think compute, there's the amount of compute you have for training, often it needs to be collocated, so actually even bandwidth constraints between data centers can affect that. So there's additional constraints even there, and that's important for training obviously the largest models you can. But there's also because now AI systems are in products and being used by billions of people around the world, you need a ton of inference compute now. And then on top of that, there's the thinking systems, the new paradigm of the last year where they get smarter the longer amount of inference time you give them at test time. So all of those things need a lot of compute, and I don't really see that slowing down. And as AI systems become better, they'll become more useful and there'll be more demand for them. So both from the training side, the training side actually is only just one part of that. It may even become the smaller part of what's needed in the overall compute that's required.

是的，这有点像那种 meme 性质的事情，就像 V3 的成功和令人难以置信的方面，人们会开玩笑说它越成功，服务器就越吃力。

Yeah, that's one sort of almost memey kind of thing which is like the success and the incredible aspects of V3, people kind of make fun of like the more successful it becomes, the servers are sweating.

是的，没错。区别在于……是的。没错。我们做了一个小视频，服务器在煎鸡蛋之类的，就是这样。我们必须想办法解决这个问题。我们做了很多有趣的硬件创新，如你所知，我们有自家的 TPU 产品线，我们也在研究纯推理的芯片，以及如何让它们更高效。我们还非常有兴趣构建 AI 系统，并已经帮助优化了能源使用，比如帮助数据中心能源（如冷却系统）提高效率、电网优化，最终还会帮助解决像等离子体约束聚变反应堆之类的问题。我们与 Commonwealth Fusion 在这方面做了很多工作，也可以想象反应堆设计。而材料设计我认为是最令人兴奋的新方向之一，比如新型太阳能材料、太阳能板材料、超室温超导体一直是我梦想中的突破，还有最优电池。我认为其中任何一项的解决方案都将对气候和能源使用产生革命性的影响，而且我们可能很快——在未来 5 年内——就能拥有能够切实帮助解决这些问题的 AI 系统。

Yes, exactly. The difference in... Yeah. Yeah. Exactly. We did a little video of the servers frying eggs and things, and that's right. And we're going to have to figure out how to do that. There's a lot of interesting hardware innovations that we do, as you know we have our own TPU line, and we're looking at inference-only things, inference-only chips, and how we can make those more efficient. We're also very interested in building AI systems and we have done to help with energy usage, so help data center energy like for the cooling systems be efficient, grid optimization, and then eventually things like helping with plasma containment fusion reactors. We've done lots of work on that with Commonwealth Fusion, and also one could imagine reactor design. And then material design I think is one of the most exciting new types of solar material, solar panel material, super room temperature superconductors has always been on my list of dream breakthroughs, and optimal batteries. And I think a solution to any one of those things would be absolutely revolutionary for climate and energy usage, and we're probably close, again in the next 5 years, to having AI systems that can materially help with those problems.

如果你要打赌，抱歉问个荒谬的问题，但你认为 20、30、40 年后的主要能源是什么？会是核聚变吗？

If you were to bet, sorry for the ridiculous question, but what is the main source of energy in like 20, 30, 40 years, do you think it's going to be nuclear fusion?

我认为我会押注核聚变和太阳能。太阳能，我的意思是，它当然是天空中的聚变反应堆，我认为真正的问题在于电池和传输。所以，以及更高效的太阳能材料，也许最终在太空中，像戴森球这样的想法。而核聚变我认为绝对可行，似乎只要我们拥有正确的反应堆设计并且能够足够快地控制等离子体等等。我认为这两个问题实际上都会得到解决，所以我们很可能至少会有这两个主要的可再生能源来源，清洁、几乎免费或者可能免费。

I think fusion and solar are the two that I would bet on. Solar, I mean, it's the fusion reactor in the sky, of course, and I think really the problem there is batteries and transmission. So, as well as more efficient solar material, perhaps eventually in space, these kind of Dyson sphere type ideas. And fusion I think is definitely doable, seems if we have the right design of reactor and we can control the plasma fast enough and so on. And I think both of those things will actually get solved, so we'll probably have at least those will probably be the two primary sources of renewable, clean, almost free or perhaps free energy.

活在这个时代真棒。如果我和你一起穿越到 100 年后，如果我们已经超越了 I 型卡尔达肖夫文明，你会感到多惊讶？

What a time to be alive. If I traveled into the future with you 100 years from now, how much would you be surprised if we've passed a type one Kardashev scale civilization?

如果从此刻算起有 100 年的时间尺度，我不会那么惊讶。我的意思是，我认为很明显，如果我们用刚才讨论的某种方式解决了能源问题——核聚变或高效的太阳能——那么如果能源变得免费、可再生且清洁，就会解决一大堆其他问题。例如，水资源获取问题就消失了，因为你可以直接使用海水淡化。我们有这项技术，只是太贵了。所以只有像新加坡和以色列这样相当富裕的国家才实际使用它。但如果它变得便宜，那么所有沿海国家都可以使用。而且你还会有无限的火箭燃料。你可以直接用能量将海水分离成氢和氧，那就是火箭燃料。所以结合埃隆·马斯克令人惊叹的自动着陆火箭，那么它就像去太空的巴士服务。这开启了不可思议的新资源和领域。我认为小行星采矿将成为现实，人类将最大程度地繁荣到星辰大海。这也是我梦想的，就像卡尔·萨根的想法，将意识带到宇宙，唤醒宇宙。我认为如果我们把 AI 做好，并用它解决其中一些问题，人类文明将在时间的完整意义上实现这一点。

I would not be that surprised if there was a 100-year time scale from here. I mean, I think it's pretty clear if we crack the energy problems in one of the ways we've just discussed, fusion or very efficient solar, then if energy is kind of free and renewable and clean, then that solves a whole bunch of other problems. So, for example, the water access problem goes away because you can just use desalination. We have the technology, it's just too expensive. So only fairly wealthy countries like Singapore and Israel actually use it. But if it was cheap, then all countries that have a coast could use it. But also you'd have unlimited rocket fuel. You could just separate sea water out into hydrogen and oxygen using energy, and that's rocket fuel. So combined with Elon's amazing self-landing rockets, then it could be like a bus service to space. So that opens up incredible new resources and domains. Asteroid mining I think will become a thing, and maximum human flourishing to the stars. That's what I dream about as well, like Carl Sagan's sort of idea of bringing consciousness to the universe, waking up the universe. And I think human civilization will do that in the full sense of time if we get AI right and crack some of these problems with it.

是的。我想知道如果你只是一个在太空中飞行的游客，会看到什么。你可能会注意到地球，因为如果你解决了能源问题，可能会看到很多太空火箭。所以就像伦敦这里的交通一样。

Yeah. I wonder what it would look like if you just a tourist flying through space. You would probably notice Earth because if you solve the energy problem, you would see a lot of space rockets probably. So it would be like traffic here in London.

但在太空中。

But in space.

很多火箭。

Just a lot of rockets.

然后你可能会看到太空中漂浮着某种能源，比如太阳能。

And then you would probably see floating in space some kind of source of energy like solar.

有可能。所以地球表面看起来会更科技化，然后你会利用那种能源的力量来保护自然，比如雨林之类的东西。

Potentially. So Earth would just look more on the surface more technological, and then you would use the power of that energy then to preserve the natural, like the rainforest and all that kind of stuff.

是的，因为人类历史上第一次我们不再受资源限制，我认为这可能是人类一个了不起的新时代，不再是零和博弈，对吧？我有这块地，你就没有。或者如果我们考虑，如果老虎拥有它们的森林，那么当地村民就不能用了，他们用什么？我认为这会大有帮助。

Yes, because for the first time in human history we wouldn't be resource constrained, and I think that could be an amazing new era for humanity where it's not zero sum, right? I have this land, you don't have it. Or if we take, if the tigers have their forest, then the local villagers can't, what are they going to use? I think that this will help a lot.

不，它不会解决所有问题，因为仍然存在其他人类弱点，但它至少会消除一个主要因素，那就是资源稀缺，包括土地、材料和能源。我们应该——我有时称之为，其他人也称之为——这种资源极大丰富的时代，有充足的资源可供分配。但当然，下一个大问题是确保公平分享，让社会中的每个人都受益。所以关于人性，你知道，就像《波拉特》里说的：我的邻居，我喜欢，你开始惹麻烦，我们确实会引发冲突。这就是为什么游戏——我实际上越来越多地了解到，甚至在古代历史中——起到了将人们从战争，特别是热战中推开的作用。所以也许我们可以设计出越来越复杂的电子游戏，来满足我们对冲突的渴望，无论那是什么关于我们人性的东西，然后避免随着技术日益先进而可能发生的实际热战，因为我们现在早已过了能够制造出足以摧毁整个人类文明的武器的阶段。所以这不再是和邻居开战的好方式。最好是下一盘棋或踢一场足球。

No, it won't solve all problems because there's still other human foibles that will still exist, but it will at least remove one of the big vectors which is scarcity of resources, you know, including land and materials and energy. And you know, we should be—I sometimes call it, and others call it, this kind of radical abundance era where there's plenty of resources to go around. But of course the next big question is making sure that that's fairly shared and everyone in society benefits from that. So there is something about human nature where, you know, it's like Borat: my neighbor, I like, you start trouble, we do start conflicts. And that's why games throughout—I'm learning actually more and more even in ancient history—serve the purpose of pushing people away from war, actually hot war. So maybe we can figure out increasingly sophisticated video games that give us that scratch the itch of conflict, whatever that is about us, human nature, and then avoid the actual hot wars that would come with increasingly sophisticated technologies, because we're now long past the stage where the weapons we're able to create can actually destroy all of human civilization. So it's no longer a great way to start with your neighbor. It's better to play a game of chess or football.

或者足球。

Or football.

是的。我认为这就是现代体育的意义。所以我喜欢足球，看足球，而且我以前也经常踢——它非常直观，有部落感，我认为它确实将很多能量引导到一种人类归属感的需求上，但以一种有趣、健康的方式，不是破坏性的，而是建设性的。回到游戏，我认为它们之所以对孩子们玩象棋之类的东西如此有益，是因为它们是世界的绝佳微观模拟。它们也是世界的模拟。它们是现实世界某些情境的简化版本，无论是扑克、围棋还是象棋，都反映了现实世界的不同方面。它们还让你能够练习。因为，你知道，你一生中有多少次机会练习一个重大决策时刻？选择什么工作，上什么大学？你可能只有十来个关键决策要做，你必须尽可能做出最好的选择。而游戏是一种安全、可重复的环境，你可以在其中提高决策能力。而且它可能还有一个额外的好处，就是将一些能量引导到更有创造性和建设性的事业上。

Yeah. And I think that's what modern sport is. So I love football, watching it, and I just feel like—and I used to play it a lot as well—and it's very visceral and it's tribal, and I think it does channel a lot of those energies into a kind of human need to belong to some group, but into a fun way, a healthy way, and a not destructive but constructive thing. And I think going back to games again, I think they're originally why they're so great as well for kids to play things like chess: they're great little microcosm simulations of the world. They are simulations of the world too. They're simplified versions of some real-world situation, whether it's poker or Go or chess, different aspects of the real world. And they allow you to practice at them, too. Because, you know, how many times do you get to practice a massive decision moment in your life? What job to take, what university to go to? You get maybe a dozen or so key decisions one has to make, and you've got to make those as best as you can. And games are a kind of safe environment, repeatable environment where you can get better at your decision-making process. And it maybe has this additional benefit of channeling some energies into more creative and constructive pursuits.

嗯，我认为练习输赢也非常重要，对吧？就像输——这就是为什么我喜欢游戏。这也是为什么我喜欢像巴西柔术这样的东西。

Well, I think it's also really important to practice losing and winning, right? Like losing is a really—that's why I love games. That's why I love even things like Brazilian jiu-jitsu.

是的。

Yeah.

在那里你可以在一个安全的环境中反复被揍。它提醒你物理规律，世界运作的方式，有时你输，有时你赢。你仍然可以和每个人做朋友。但那种失败的感觉，对我们人类来说，真的很难理解，就像那只是生活的一部分。失败是生活的基本组成部分。

Where you can get your ass kicked in a safe environment over and over. It reminds you about the way about physics, about the way the world works, about sometimes you lose, sometimes you win. You can still be friends with everybody. But that feeling of losing, I mean, it's a weird one for us humans to really make sense of, like that's just part of life. That is a fundamental part of life is losing.

是的。我认为在武术中，据我所知，但在像国际象棋这样的东西中，至少我理解的方式，它与自我提升、自我认知有很大关系。你知道，好吧，我做了这件事。这并不真的是关于打败别人。而是关于最大化你自己的潜力。如果你以健康的方式去做，你会学会利用胜利和失败。不要被胜利冲昏头脑，认为自己是世界上最好的。而失败让你保持谦逊，始终知道总有更多东西要学。总有更厉害的专家可以指导你。我认为你在武术中学到了这一点，而且我认为这也是我接受国际象棋训练的方式。同样，它可以非常硬核和重要，当然你想赢，但你也需要学会如何以健康的方式应对挫折，并将你失去某物时的感觉转化为建设性的东西：下次我要改进这个，或者在这方面做得更好。有一种东西是幸福和意义的来源，那就是进步。它不在于输赢。

Yeah. And I think in martial arts as I understand it, but also in things like chess, at least the way I took it, it's a lot to do with self-improvement, self-knowledge. You know that, okay, so I did this thing. It's not about really beating the other person. It's about maximizing your own potential. If you do it in a healthy way, you learn to use victory and losses in a way. Don't get carried away with victory and think you're the best in the world. And the losses keep you humble and always knowing there's always something more to learn. There's always a bigger expert that can mentor you. I think you learn that in martial arts, and I think that's also the way that at least I was trained in chess. And so in the same way, it can be very hardcore and very important, and of course you want to win, but you also need to learn how to deal with setbacks in a healthy way, and wire that feeling that you have when you lose something into a constructive thing of next time I'm going to improve this, or get better at this. There is something that's a source of happiness, a source of meaning, that improvement step. It's not about the winning or losing.

是的。精通。没有什么比这更令人满足的了，就像‘哦，哇。我以前做不到的事，现在我能做到了。’同样，游戏、体育和智力运动，它们是衡量的方式。它们很美，因为你可以衡量那种进步。

Yes. The mastery. There's nothing more satisfying in a way is like, 'Oh, wow. This thing I couldn't do before, now I can.' And again, games and physical sports and mental sports, they're ways of measuring. They're beautiful because you can measure that progress.

是的。我的意思是，这就是为什么我喜欢角色扮演游戏，比如技能树上的数字上升。这实际上是我们人类意义的一个来源。无论我们——是的，我们相当沉迷于这种数字上升。也许这就是为什么我们制作了这样的游戏，因为显然我们自己就是爬山系统，对吧？

Yeah. I mean there's something about this is why I love role-playing games like the number go up of my on the skill tree. Literally that is a source of meaning for us humans. Whatever our—yeah, we're quite addicted to this sort of numbers going up. And maybe that's why we made games like that, because obviously that is something we're hill-climbing systems ourselves, right?

是的，如果我们没有任何机制——色带，所有这些。我们到处都这样做，对吧？我们就有这个东西——

Yeah, it would be quite sad if we didn't have any mechanism by—color belts, all of that. We do this everywhere, right? We just have this thing that—

这是——我不想否认这对我们人类来说是一个深刻意义的来源。

It's—and I don't want to dismiss that there is a source of deep meaning for us as humans.

那么，在商业和领导力方面，一个令人难以置信的故事是谷歌在过去一年所做的事情。所以我认为可以公平地说，一年前谷歌在 LLM 产品方面是落后的，Gemini 1.5 表现不佳，而现在它凭借 Gemini 2.5 领先了，而你掌舵并领导了这项工作。从所谓的“落后”到所谓的“领先”，在一年内需要做些什么？

So one of the incredible stories on the business on the leadership side is what Google has done over the past year. So I think it's fair to say that Google was losing on the LLM product side a year ago with Gemini 1.5, and now it's winning with Gemini 2.5, and you took the helm and led this effort. What did it take to go from, let's say, quote unquote losing to quote unquote winning in the span of a year?

是的。首先，我们拥有绝对不可思议的团队，由 Cory、Jeff Dean、Oral 以及 Gemini 的出色团队领导。绝对是世界级的。所以，没有最优秀的人才，你做不到这一点。当然，我们也有大量出色的算力。

Yeah. Well, firstly, it's absolutely incredible team that we have, led by Cory and Jeff Dean and Oral and the amazing team we have on Gemini. Absolutely world class. So, you can't do it without the best talent. And of course, we have a lot of great compute as well.

但这是我们创造的研究文化，对吧？基本上，谷歌内部的不同团队走到一起，你知道，有世界级的 Google Brain 团队，还有原来的 DeepMind，汇集了所有最优秀的人才和最好的想法，围绕在一起打造我们所能做到的最伟大的系统。这很难，但我们都非常有竞争力，而且我们热爱研究。这太有趣了。我们对自己所处的位置非常满意。进步的速度是最重要的。所以，如果你看看我们从两年前到一年前再到现在的进展，我认为我们称之为‘不懈的进步’加上‘不懈地交付这种进步’，是非常成功的。整个 AI 领域竞争异常激烈，世界上一些最伟大的企业家、领导者和公司现在都在竞争，因为每个人都意识到了 AI 的重要性。看到这种进步，我们感到很高兴。

But then it's the research culture we've created, right? And basically coming together both different groups in Google, you know, there was Google Brain, world-class team, and then the old DeepMind, and pulling together all the best people and the best ideas and gathering around to make the absolute greatest system we could. Hard, but we're all very competitive and we love research. This is so fun to do. And we're very pleased with where we are. The rate of progress is the most important thing. So if you look at where we've come from 2 years ago to 1 year ago to now, I think our, we call it relentless progress along with relentless shipping of that progress, is being very successful. And it's unbelievably competitive, the whole AI space, with some of the greatest entrepreneurs and leaders and companies in the world all competing now because everyone's realized how important AI is. And it's been pleasing for us to see that progress.

谷歌是一家巨大的公司。你能谈谈在这种情况下自然会发生的事情吗，比如出现的官僚主义？就像你要小心，那种自然的会议和经理。从领导力的角度来看，突破这些挑战以便交付过去一年里那么多 Gemini 相关产品，有哪些挑战？这真是疯狂。

Google's a gigantic company. Can you speak to the natural things that happen in that case, the bureaucracy that emerges? Like you want to be careful, like the natural kind of there's meetings and there's managers. What are some of the challenges from a leadership perspective breaking through that in order to ship the number of Gemini related products that's been shipped over the past year? It's just insane.

没错，就是这样。这就是不懈的样子。我认为问题在于，任何大公司最终都会有很多管理层级之类的东西。这基本上是运作方式。但我仍然像以前一样运作，我一直把原来的 DeepMind 当作一家初创公司来运营，虽然规模很大，但仍然是初创公司的心态。今天我们在 Google DeepMind 仍然如此，以果断和最佳小型组织所拥有的能量来行动。我们试图两全其美：一方面拥有数十亿用户的巨大平台，可以用我们的 AI 和研究来赋能这些令人难以置信的产品。这太棒了。世界上很少有地方能做到这一点：一方面做世界级的研究，另一方面第二天就能将其融入产品，改善数十亿人的生活。这是一个非常惊人的组合。我们不断与官僚主义作斗争，削减它，以让研究文化和不懈交付文化蓬勃发展。我认为我们取得了很好的平衡，同时保持负责任的态度，这是作为大公司必须做到的，而且我们拥有众多巨大的产品平台。

Right, it is. Yeah, exactly. That's what relentlessness looks like. I think it's a question of like any big company ends up having a lot of layers of management and things like that. It's sort of the nature of how it works. But I still operate, and I was always operating with old DeepMind as a startup, still large one but still as a startup, and that's what we still act like today with Google DeepMind, acting with decisiveness and the energy that you get from the best smaller organizations. And we try to get the best of both worlds, where we have this incredible billions of users surfaces, incredible products that we can power up with our AI and our research. And that's amazing. There are very few places in the world you can get that: do incredible world-class research on the one hand and then plug it in and improve billions of people's lives the next day. That's a pretty amazing combination. And we're continually fighting and cutting away bureaucracy to allow the research culture and the relentless shipping culture to flourish. And I think we've got a pretty good balance, whilst being responsible with it, as you have to be as a large company and also with a number of huge product surfaces that we have.

你提到数十亿用户平台，这让我想起一件事。我和一位名叫 Irvin Fininkle 的人聊过，他是楔形文字（古代泥板上的文字）的世界专家。他不知道 ChatGPT 或 Gemini，甚至对 AI 一无所知。但他第一次接触 AI 就是谷歌上的 AI 模式。他说：‘这就是你说的东西吗？这个 AI 模式？’这提醒我们，世界上有很大一部分人还不知道 AI 这回事。

A funny thing you mentioned about the surface of the billion. I had a conversation with a guy named Irvin Fininkle, a world expert at cuneiforms, ancient writing on tablets. He doesn't know about ChatGPT or Gemini. He doesn't even know anything about AI. But his first encounter with this AI is AI mode on Google. He's like, 'Is that what you're talking about? This AI mode?' It's just a reminder that there's a large part of the world that doesn't know about this AI thing.

是啊，我知道。这很有趣，因为如果你活在 X 和 Twitter 上，至少我的信息流里全是 AI。在硅谷这样的地方和一些小圈子里，每个人都在想 AI。但很多普通世界的人还没接触到它。

Yeah, I know. It's funny because if you live on X and Twitter, at least my feed, it's all AI. And in certain places like the valley and certain pockets, everyone's just thinking about AI. But a lot of the normal world hasn't come across it yet.

这是一个巨大的责任，他们的第一次互动，在印度农村或世界任何地方的大规模场景中。你得以……

That's a great responsibility, their first interaction on the grand scale of rural India or anywhere across the world. You get to...

没错，我们希望它尽可能好。在很多情况下，它只是在幕后驱动 Maps 或搜索等产品更好地工作。理想情况下，对很多人来说，它应该是无缝的。这只是让他们的生活更高效、更有帮助的新技术。

Right, and we want it to be as good as possible. In a lot of cases, it's just under the hood powering something like Maps or Search to work better. And ideally, for a lot of those people, it should just be seamless. It's just new technology that makes their lives more productive and helps them.

Gemini 产品和工程团队的好多人对你评价极高，在另一个我几乎没想到的维度上，因为我通常认为你是一位关注重大科学问题的深度科学家。但他们也说你是很棒的产品人，知道如何创造很多人会使用并喜欢使用的东西。那么，你能谈谈创造一款很多人会喜欢使用的 AI 产品需要什么吗？

A bunch of folks on the Gemini product and engineering teams spoke extremely highly of you on another dimension that I almost didn't expect, because I kind of think of you as the deep scientist caring about big research scientific questions. But they also said you're a great product guy, how to create a thing that a lot of people would use and enjoy using. So can you speak to what it takes to create an AI-based product that a lot of people would enjoy using?

是的。嗯，这又回到了我设计游戏的时期，那时我为数百万玩家设计游戏。人们忘了这一点。我有过将尖端技术应用于产品的经验。90 年代的游戏就是这样。所以我热爱将前沿研究应用于产品、驱动新体验的结合。我认为这其实是同一种技能：想象使用它时的直观感受，并拥有良好的品味。回到之前，我认为在科学中有用的东西，在产品设计中也有用。我一直是一个多学科的人，所以我看不到艺术与科学、产品与研究之间的界限。对我来说，这是一个连续体。我只喜欢做前沿的产品。如果只是普通产品，我不会兴奋。所以这需要发明创造的能力。

Yeah. Well, again, that comes back from my game design days where I used to design games for millions of gamers. People forget about that. I've had experience with cutting-edge technology in product. That's how games were in the '90s. And so I love the combination of cutting-edge research and then being applied in a product to power a new experience. I think it's the same skill really, of imagining what it would be like to use it viscerally and having good taste. Coming back to earlier, the same thing that's useful in science, I think, can also be useful in product design. I've always been a sort of multi-disciplinary person, so I don't see the boundaries between arts and sciences or product and research. It's a continuum for me. I only like working on products that are cutting-edge. I wouldn't be excited about them if they were just run-of-the-mill products. So it requires this invention creativity capability.

你在与 Gemini 互动时学到了一些具体的东西吗？比如布局、界面感觉不对，或者延迟的权衡，如何向用户展示等待时间，以及等待的显示方式，或者推理能力？有一些有趣的事情，因为这是非常前沿的。我们不知道如何正确呈现它。那么你学到了什么具体的东西吗？

What are some specific things you learned about when you're interacting with Gemini, like this doesn't feel like the layout, the interface, maybe the trade-off between latency, how to present to the user how long to wait, and how that waiting is shown, or the reasoning capabilities? There's some interesting things because it's the very cutting edge. We don't know how to present it correctly. So is there some specific things you've learned?

我的意思是，这是一个快速发展的领域。我们一直在评估，但今天我们的结论是，你想要不断简化事物。无论是界面还是你在模型之上构建的所有东西，你都想为模型让路。模型这列火车正在轨道上飞驰，它的改进速度快得令人难以置信。

I mean, it's such a fast-evolving space. We're evaluating this all the time, but where we are today is that you want to continually simplify things. Whether that's the interface or all the stuff you build on top of the model, you kind of want to get out of the way of the model. The model train is coming down the track and it's improving unbelievably fast.

我们之前谈到这种持续的进步。你看，从 1.5 到 2.5 就是巨大的飞跃，未来版本也会如此。模型正变得越来越强大。所以在当今这些 AI 优先产品的设计空间中，有趣的是，你不能只根据今天技术能做什么来设计，而要考虑一年后它能做什么。所以你实际上必须是一个非常懂技术的产品人员，因为你需要有很好的直觉和感觉：我现在梦想的东西今天还做不到，但研究路线图是否能在 6 个月或一年内实现它？所以你得预判这个快速变化的技术走向，同时新能力不断涌现，你之前没意识到，比如 D research 能用了，或者现在我们有了视频生成，我们用它做什么？还有多模态的东西。我的一个问题是：我们现在的 UI 真的还会是文本框聊天吗？考虑到这些超级多模态系统，这似乎不太可能。难道不应该更像《少数派报告》那样，你以一种协作的方式与它互动吗？对吧？今天的方式太受限了。我认为在短短几年内，我们就会觉得现在的界面、产品和系统相当原始。所以我认为在产品端和研究端都有很大的创新空间。

This relentless progress we talked about earlier. You know, you look at 2.5 versus 1.5 and it's just a gigantic improvement. And we expect that again for the future versions. And so the models are becoming more capable. So you've got the interesting thing about the design space in today's world these AI first products is you got to design not for what the thing can do today the technology can do today but in a year's time. So you actually have to be a very technical product person because you got to kind of have a good intuition for and feel for okay that thing that I'm dreaming about now can't be done today but is the research track on schedule to basically intercept that in 6 months or a year's time. So you kind of got to intercept where this highly changing technology is going as well as that new capabilities are coming online all the time that you didn't realize before that can allow like D research to work or now we got video generation what do we do with that this multimodal stuff you know is it one question I have is is it really going to be the current UI that we have today these textbox chats seems very unlikely given once you think about these super multimodal systems Shouldn't it be something more like Minority Report where you're sort of vibing with it in a collaborative way? Right? It seems very restricted today. I think we'll look back on today's interfaces and products and systems as quite archaic in maybe just a couple of years. So I think there's a lot of space actually for innovation to happen on the product side as well as the research side.

我们私下讨论过这个键盘的问题：我们何时、以何种程度会转向以音频作为与周围机器交互的主要方式，而不是打字？是的，打字是一种非常低带宽的方式，即使你打字很快。我认为我们将不得不开始使用其他设备，比如智能眼镜、音频、耳机，最终可能是一些神经设备，将输入输出带宽提升到今天的 100 倍。

And then we're offline talking about this keyboard is the open question is how when and how much will we move to audio as the primary way of interacting with the machines around us versus typing stuff. Yeah, I mean typing is a very low bandwidth way of doing even if you're very fast, you know, typer and I think we're going to have to start utilizing other devices whether that's smart glasses, you know, audio, earbuds, and eventually maybe some sorts of neural devices where we can increase the input and the output bandwidth to something maybe 100x of what is today.

我认为被低估的艺术形式是界面设计。但如果没有合适的界面，你就无法释放系统的智能力量。界面确实是释放其力量的方式。如何做到这一点是一个非常有趣的问题。

I think that you know underappreciated art form is the interface design. But I think you can not unlock the power of the intelligence of a system if you don't have the right interface. The interface is really the way you unlock its power. It's such an interesting question of how to do that.

你会觉得，不挡道本身就是一门真正的艺术。

You would think like getting out of the way isn't real art form.

是的。你知道，这大概就是史蒂夫·乔布斯一直谈论的东西，对吧？我们想要的是简单、美丽和优雅，对吧？我们还没达到。在我看来，还没有人达到。这就是我希望我们达到的。这又让我想到围棋，作为一项游戏，它是最优雅、最美丽的。你能做出像那样美丽的界面吗？实际上，我认为我们将进入一个 AI 生成界面的时代，这些界面可能是为你个人定制的，符合你的审美、感觉和大脑运作方式。AI 会根据任务生成界面。我觉得这大概就是我们最终会走向的方向。

Yes. You know, it's the sort of thing that I guess Steve Jobs always talked about, right? It's simplicity, beauty, and elegance that we want, right? And we're not there. Nobody's there yet in my opinion. And that's what I would like us to get to. Again, it sort of speaks to like Go again, right? As a game, the most elegant, beautiful game. Can you make an interface as beautiful as that? And actually, I think we're going to enter an era of AI generated interfaces that are probably personalized to you so it fits the way that you your aesthetic, your feel, the way that your brain works. And the AI kind of generates that depending on the task. You know, that feels like that's probably the direction we'll end up in.

是的。因为有些人是高级用户，他们希望屏幕上显示每一个参数，所有东西，比如我可能喜欢基于键盘的导航，我喜欢所有东西都有快捷键。而有些人喜欢极简主义，把所有复杂性都隐藏起来。

Yeah. Because some people are power users and they want every single parameter on screen, everything, everything based like perhaps me with a key keyboard based navigation. I like to have shortcuts for everything. And some people like the minimalism just hide all of that complexity.

正是如此。

Exactly.

好吧，我很高兴你也有史蒂夫·乔布斯模式。太好了。爱因斯坦模式，史蒂夫·乔布斯模式。好了，让我试着骗你回答一个问题。Gemini 3 什么时候发布？是在 GTA 6 之前还是之后？全世界都在等这两个。从 2.5 到 3.0 需要什么？因为看起来 2.5 已经发布了很多版本，性能已经飞跃了。那么，推出新版本到底意味着什么？是关于性能吗？还是关于完全不同的体验？

Well, I'm glad you have a Steve Jobs mode in you as well. This is great. Einstein mode, Steve Jobs mode. All right, let me try to trick you into answering a question. When will Gemini 3 come out? Is it before or after GTA 6? The world waits for both. And what does it take to go from 2.5 to 3.0? Because it seems like there's been a lot of releases of 2.5 which are already leaps in performance. So what does it even mean to go to a new version? Is it about performance? Is this about a completely different flavor of an experience?

是的。我们不同版本号的工作方式是，我们尝试收集——大概需要 6 个月左右的时间来完成一次全新的完整运行和新版本的全面产品化。在此期间，会出现许多有趣的研究迭代和想法，我们将其收集起来。你可以想象，过去 6 个月在架构或数据方面有很多有趣的想法。我们把这些想法打包，测试哪些可能对下一次迭代有用，然后捆绑在一起，开始新的巨大英雄训练运行。当然，这会被监控，然后预训练结束后，还有后训练，有很多不同的方法和修补方式，所以有一个完整的实验阶段，你也可以从中获得很多收益。版本号通常指的是基础模型，即预训练模型。而像 2.5 这样的中间版本，以及不同尺寸和小的附加功能，通常是在相同基础架构上后续进行的修补或后训练想法。当然，除此之外，我们还有不同的尺寸，比如 Pro、Flash 和 FlashLite，它们通常是从最大的模型中蒸馏出来的，比如 Flash 模型来自 Pro 模型。这意味着如果你是一名开发者，你有一系列不同的选择：你是想优先考虑性能，还是速度或成本？我们喜欢将其视为帕累托前沿，y 轴是性能，x 轴是成本或延迟和速度，我们的模型完全定义了这条前沿。所以无论你作为个人用户或开发者想要什么样的权衡，你都能找到我们的一款模型满足你的约束。

Yeah. Well, so the way it works with our different version numbers is we try to collect maybe it takes roughly 6 months or something to do a new kind of full run and the full productization of a new version and during that time lots of new interesting research iterations and ideas come up and we sort of collect them all together that you could imagine the last 6 months worth of interesting ideas on the architecture front maybe it's on the data front. It's like many different possible things and we collect package that all up, test which ones are likely to be useful for the next iteration and then bundle that all together and then we start the new giant hero training run right and then of course that gets monitored and then at the end then there's the of the pre-training then there's all the post-training there's many different ways of doing that different ways of patching it so there's a whole experimental phase there which you can also get a lot of gains out and that's where you see the version numbers usually referring to the base model, the pre-trained model. And then the interim versions of 2.5, you know, and the different sizes and the different little additions, they're often patches or post-training ideas that can be done afterwards off the same basic architecture. And then of course on top of that, we also have different sizes, pro and flash and flashlight that are often distilled from the biggest ones, you know, the flash model from the Pro model. And that means we have a range of different choices if you are the developer of do you want to prioritize performance or speed right and cost. And we like to think of this Pareto frontier of on the one hand the y-axis is like performance and then the x-axis is cost or latency and speed basically and we have models that completely define the frontier. So whatever your trade-off is that you want as an individual user or as a developer, you should find one of our models satisfies that constraint.

所以版本变化的背后是一次大的英雄运行。

So behind the version changes, there is a big hero run.

是的。

Yes.

然后还有极其复杂的产品化过程。接着是沿着帕累托前沿对不同尺寸进行蒸馏。每走一步，你都会发现可能有一个很酷的产品。还有支线任务。

And then there's just an insane complexity of productization. Then there's the distillation of the different sizes along that Pareto front. And then as with each step you take, you realize there might be a cool product. There's side quests.

是的。

Yes.

正是如此。

Exactly.

但你也不想做太多支线任务，因为那样你就会有无数个版本和产品。这很不明确，但你也会非常兴奋，因为它超级酷。

But then you also don't want to take too many side quests because then you have a million versions of million products. It's very unclear, but you also get super excited because it's super cool.

比如你怎么看 VO？它如何融入更大的图景？

Like how do you even look at VO? How does it fit into the bigger thing?

没错。然后你不断进行我们所说的“上游汇聚”过程，从产品界面或后训练甚至更下游的地方获取想法，再将这些想法上游到下一轮的核心模型训练中。这样，主模型，也就是 Gemini 主线，变得越来越通用，最终达到 AGI。

Exactly. Exactly. And then you're constantly this process of converging upstream we call it, you know, ideas from the product surfaces or from the post-training, and even further downstream than that, you kind of upstream that into the core model training for the next run. Right. So then the main model, the main Gemini track, becomes more and more general and eventually, you know, AGI.

一次一个英雄运行。

One hero run at a time.

是的，没错。几次英雄运行之后。

Yes. Exactly. A few hero runs later.

嗯，是的。所以有时候当你发布新版本，或者说每个版本，基准测试对于展示模型性能既有用也可能适得其反。你需要它们，但重要的是不能过拟合，对吧？所以它们不应该是终极标准。比如 LMArena（以前叫这个名字），它自然成为了人们测试这些系统的主要方式之一，至少对于聊天机器人来说是这样。显然还有很多学术基准测试，测试数学和编程能力、通用语言能力、科学能力等等。然后我们还有自己关心的内部基准测试。这是一个多目标优化问题，对吧？你不想只擅长一件事。我们试图构建全面优秀的通用系统，并做出无悔的改进，比如提升编程能力，但不能降低其他领域的性能，对吧？这就是难点，因为你可以加入更多编程数据，或者更多游戏数据，但这样会不会让你的语言系统或翻译系统等其他你关心的方面变差？所以你必须持续监控这个越来越大的基准测试集。另外，当这些模型被应用到产品中时，你也会关心直接使用情况、直接统计数据和来自最终用户的信号，无论他们是程序员还是普通用户使用聊天界面。

Uh yeah. So sometimes when you release these new versions, or every version really, benchmarks are productive or counterproductive for showing the performance of a model. You need them, but it's important that you don't overfit to them, right? So they shouldn't be the be-all and end-all. So there's LMArena, or it used to be called that, which turned out organically to be one of the main ways people like to test these systems, at least the chatbots. Obviously there are loads of academic benchmarks from that test mathematics and coding ability, general language ability, science ability, and so on. And then we have our own internal benchmarks that we care about. It's a kind of multi-objective optimization problem, right? You don't want to be good at just one thing. We're trying to build general systems that are good across the board, and you try and make no-regret improvements, though where you're improving like coding, but it doesn't reduce your performance in other areas, right? So that's the hard part, because you can of course put more coding data in, or you could put more, I don't know, gaming data in, but then does it make worse your language system or your translation systems and other things that you care about? So you've got to kind of continually monitor this increasingly larger and larger suite of benchmarks. And also, when you stick them into products, these models, you also care about the direct usage and the direct stats and the signals that you're getting from the end users, whether they're coders or the average person using the chat interfaces.

是的。因为最终你想衡量的是有用性，但很难把它转化为一个数字，对吧？这实际上是基于大量用户的感觉基准测试，很难知道。对我来说，如果有一个更聪明的模型，但感觉上不太对劲，那会很可怕。这很吓人，因为正如你所说，它必须在很多领域都既聪明又有用。所以你会非常兴奋，因为它突然能解决以前解决不了的编程问题。但现在它写的诗很烂，或者别的什么，这……我不知道，这很有压力，很难平衡，因为你不能真正相信基准测试，你必须相信最终用户。

Yeah. Because ultimately you want to measure the usefulness, but it's so hard to convert that into a number, right? It's really vibe-based benchmarks across a large number of users, and it's hard to know. And it would be just terrifying to me to have a much smarter model but it's just something vibe-based, not quite working. That's such a scary thing because everything you just said, it has to be smart and useful across so many domains. So you get super excited because it's all of a sudden solving programming problems you've never been able to solve before. But now it's crappy poetry or something, and it's just... I don't know, that's stressful, that's so difficult to balance, and because you can't really trust the benchmarks, you really have to trust the end users.

是的。然后还有一些更玄妙的东西也会起作用，比如系统的角色风格，它是啰嗦还是简洁，还是幽默？不同的人喜欢不同的东西。所以这很有趣，几乎像是心理学或人格研究的前沿。我读博时研究过五大人格。我们到底希望我们的助手是什么样子？不同的人也会喜欢不同的东西。所以这些都是产品领域的新问题，我认为以前从未真正解决过，但现在我们必须迅速应对。我认为这是一个非常迷人的领域，塑造事物的性格，同时它也是一面镜子，照出我们自己：我们喜欢什么样的东西？因为提示工程允许你控制很多这些元素，但产品能否让你更容易地控制这些体验的不同风味，与你互动的不同角色？

Yeah. And then other things that are even more esoteric come into play, like the style of the persona of the system, how it is, you know, is it verbose, is it succinct, is it humorous? Different people like different things. So it's very interesting, it's almost like cutting-edge part of psychology research or personality research. I used to do that in my PhD, five-factor personality. What do we actually want our assistants to be like? And different people will like different things as well. So these are all just sort of new problems in product space that I don't think have ever really been tackled before, but we're going to sort of rapidly have to deal with now. I think it's a super fascinating space, developing the character of the thing, and in so doing it puts a mirror to ourselves: what are the kind of things that we like? Because prompt engineering allows you to control a lot of those elements, but can the product make it easier for you to control the different flavors of those experiences, the different characters that you interact with?

是的，没错。所以……

Yeah, exactly. So...

那么 Google DeepMind 获胜的概率有多大？

So what's the probability of Google DeepMind winning?

嗯，我不认为这是“获胜”。我的意思是，考虑到我们正在构建的东西有多么重要和影响深远，我认为“获胜”是错误的理解方式。所以，有趣的是，我尽量不把它看作一场游戏或竞争，尽管这很大程度上是我的思维方式。在我看来，我们这些处于前沿的人都有责任管理好这项不可思议的技术，它既可以用于巨大的善举，也存在风险，要安全地将其引入世界，造福人类。这一直是我的梦想，也是我们一直努力的方向，我希望最终社区，也许是国际社会，会在我们越来越接近 AGI 时意识到这是必要的，并团结起来。

Well, I don't see it as sort of winning. I mean, I think we need to think winning is the wrong way to look at it, given how important and consequential what it is we're building. So, funnily enough, I try not to view it like a game or competition, even though that's a lot of my mindset. It's about, in my view, all of us at the leading edge have a responsibility to steward this unbelievable technology that could be used for incredible good, but also has risks, steward it safely into the world for the benefit of humanity. That's always what I've dreamed about and what we've always tried to do, and I hope that's what eventually the community, maybe the international community, will rally around when it becomes obvious that as we get closer and closer to AGI, that that's what's needed.

我同意你的观点。我觉得说得很好。你说过，随着竞争加剧，你与一些实验室的负责人保持联系并关系良好。维持这些关系有多难？

I agree with you. I think that's beautifully put. You've said that you talk to and are on good terms with the leads of some of these labs as the competition heats up. How hard is it to maintain sort of those relationships?

到目前为止还好。我努力以合作精神为傲。我是一个乐于合作的人。研究是合作的事业，科学也是合作的事业，对吧？最终，如果你治愈了可怕的疾病，带来了不可思议的疗法，这对人类是净收益，能源也是如此。所有我想用 AI 帮助解决的问题都是如此。所以我只希望这项技术存在于世界，并被用于正确的事情，其好处，生产力上的好处，能被所有人共享。所以我努力与所有领先实验室的人保持良好关系。他们很多人都有非常有趣的性格，正如你所料。但我和他们几乎所有人都关系良好，我希望如此。而且我认为当事情变得比现在更严重时，保持这些沟通渠道会很重要，这将促进必要的合作或协作，尤其是在安全等问题上。

It's been okay so far. I try to pride myself in being collaborative. I'm a collaborative person. Research is a collaborative endeavor. Science is a collaborative endeavor. Right? It's all good for humanity in the end if you cure incredible terrible diseases and you come with an incredible cure, this is a net win for humanity, and the same with energy. All of the things that I'm interested in helping solve with AI. So I just want that technology to exist in the world and be used for the right things, and the benefits of that, the productivity benefits, being shared for the benefit of everyone. So I try to maintain good relations with all the leading lab people. They have very interesting characters, many of them as you might expect. But yeah, I'm on good terms, I hope, with pretty much all of them. And I think that's going to be important when things get even more serious than they are now, that there are those communication channels, and that's what will facilitate cooperation or collaboration if that is required, especially on things like safety.

是的，我希望在一些不那么高风险的事情上也有合作，这样有助于维持友谊和关系。比如，我觉得如果你和埃隆能合作制作一款电子游戏，互联网会很高兴的。这种事情能促进友谊和良好关系，而且你们俩都是真正的游戏玩家。所以，这很有趣……是的。

Yeah, I hope there's some collaboration on stuff that's less high stakes, and in so doing serves as a mechanism for maintaining friendships and relationships. So, for example, I think the internet would love it if you and Elon somehow collaborated on creating a video game. That kind of thing, I think, enables camaraderie and good terms, and also you two are legit gamers. So, it's just fun to... Yeah.

创造很有趣。是的，那太棒了。我们过去也讨论过，这可能是我们可以做的一件很酷的事情。我同意你的看法。拥有一些副项目会很好，这样人们可以专注于合作方面，这对双方都是双赢，也能锻炼合作能力。

Fun to create. Yeah, that would be awesome. And we've talked about that in the past and it may be a cool thing that we can do. And I agree with you. It'd be nice to have kind of side projects in a way where one can just lean into the collaboration aspect of it and it's a sort of win-win for both sides and it kind of builds up that collaborative muscle.

我认为科学事业就是人类的这种副项目，而 DeepMind 一直在推动这一点。我希望其他实验室也能做更多科学工作并合作，因为在重大科学问题上合作似乎更容易。

I see the scientific endeavor as that kind of side project for humanity and I think DeepMind has been really pushing that. I would love it if other labs did more scientific stuff and then collaborated, because it just seems easier to collaborate on the big scientific questions.

我同意，我很希望看到很多人、很多其他实验室谈论科学，但我认为我们是唯一真正将 AI 用于科学并这样做的，这就是为什么像 AlphaFold 这样的项目对我如此重要，对我们的使命也是如此——展示 AI 如何以非常具体的方式造福人类。我们还从 AlphaFold 衍生出 Isomorphic 这样的公司进行药物发现，进展顺利，并构建了额外的 AlphaFold 类型系统进入化学领域，以加速药物设计。我认为我们需要展示这些例子，社会需要了解 AI 能带来这些巨大好处。

I agree and I would love to see a lot of people, a lot of the other labs talk about science, but I think we're really the only ones using it for science and doing that, and that's why projects like AlphaFold are so important to me and I think to our mission is to show how AI can be clearly used in a very concrete way for the benefit of humanity. And also we spun out companies like Isomorphic off the back of AlphaFold to do drug discovery and it's going really well, and build sort of additional AlphaFold-type systems to go into chemistry space to help accelerate drug design. I think the examples we need to show and society needs to understand what AI can bring these huge benefits.

衷心感谢你们以严谨、有趣、谦逊的态度推动科学努力。我很喜欢看到这些，而且还在讨论 P 是否等于 NP。这太不可思议了。我很喜欢。

Well, from the bottom of my heart, thank you for pushing the scientific efforts forward with rigor, with fun, with humility, all of it. I just love to see and still talking about P equals NP. I mean, it's just incredible. So, I love it.

似乎有一场人才争夺战。有些是梗，我不确定。你怎么看 Meta 用高薪挖人以及人才争夺战的白热化？我想说，很多人认为 DeepMind 是进行前沿工作的好地方，原因正如你所说，这里有充满活力的科学文化。

There's been seemingly a war for talent. Some of it is meme, I don't know. What do you think about Meta buying up talent with huge salaries and the heating up of this battle for talent? And I should say that I think a lot of people see DeepMind as a really great place to do cutting-edge work for the reasons that you've outlined, like there's this vibrant scientific culture.

是的。当然，Meta 现在采取了一种策略。我认为，至少从我的角度看，那些真正相信 AGI 使命及其能力、理解其好坏后果以及随之而来的责任的人，他们这样做主要是为了像我一样处于研究前沿，从而能够影响发展方向，并安全地将这项技术引入世界。而 Meta 目前并不在前沿；也许他们会设法回到前沿。从他们的角度看，他们这样做可能是理性的，因为他们落后了，需要采取行动，但我认为有比金钱更重要的东西。当然，必须按市场价支付人们薪水，而且这些费用还在上涨。但我预料到了这一点，因为越来越多的人，尤其是公司领导者，终于意识到我 30 多年来一直知道的事情：AGI 很可能是人类有史以来最重要的技术。所以从某种意义上说，这样做是理性的。但我也认为有一个更大的问题。如今 AI 领域的人收入很高。我记得 2010 年我们刚开始时，我好几年都没给自己发工资，因为没钱。我们筹不到钱。而现在实习生的薪水相当于我们第一轮种子轮融资的总金额。这很有趣。我记得过去我不得不免费工作，甚至自费实习。现在完全反过来了，但现实就是这样。这是一个新世界。但我想我们一直在讨论 AGI 之后会发生什么，能源系统问题解决后，金钱还有什么意义？所以我认为我们将面临更大的问题，比如经济在那个世界如何运作，公司如何运作。所以我认为今天的薪水之类的问题只是次要的。

Yeah. Well, look, of course, there's a strategy that Meta is taking right now. I think that from my perspective at least, I think the people that are real believers in the mission of AGI and what it can do and understand the real consequences both good and bad from that and what that responsibility entails, I think they're mostly doing it to be like myself to be on the frontier of that research so they can help influence the way that goes and steward that technology safely into the world. And Meta right now are not at the frontier; maybe they'll manage to get back on there. And it's probably rational what they're doing from their perspective because they're behind and they need to do something, but I think there are more important things than just money. Of course one has to pay people their market rates and all of these things and that continues to go up. But I was expecting this because more and more people are finally realizing, leaders of companies, what I've always known for 30 plus years now, which is that AGI is the most important technology probably that's ever going to be invented. So in some senses it's rational to be doing that. But I also think there's a much bigger question. I mean people in AI these days are very well paid. I remember when we were starting out back in 2010, I didn't even pay myself for a couple of years because there wasn't enough money. We couldn't raise any money. And these days interns are being paid the amount that we raised as our first entire seed round. So it's pretty funny. And I remember the days where I used to have to work for free and almost pay my own way to do an internship. Right now it's all the other way around, but that's just how it is. It's the new world. But I think that we've been discussing like what happens post-AGI and energy systems are solved and so on, what is even money going to mean? So I think we're going to have much bigger issues to work through, like how does the economy function in that world and companies. So I think it's a little bit of a side issue about salaries and things like that today.

是的，当你面对如此巨大的后果和如此迷人的科学问题时，而这些可能就在几年后。

Yeah, when you're facing such gigantic consequences and gigantic fascinating scientific questions, which maybe only a few years away.

那么从实际、务实的角度来看，如果我们聚焦于工作，可以看看程序员，因为 AI 系统目前在编程方面做得非常好，而且越来越好。所以很多以编程为生、热爱编程的人担心会失业。你认为他们应该有多担心？以及如何正确适应新现实，确保作为人类在编程世界中生存并茁壮成长？

So on the practical, pragmatic sense, if we zoom in on jobs, we can look at programmers because it seems like AI systems are currently doing incredibly well in programming and increasingly so. So a lot of people that program for a living, love programming, are worried they will lose their jobs. How worried should they be, do you think? And what's the right way to adjust to the new reality and ensure that you survive and thrive as a human in the programming world.

有趣的是，编程——这又与我们多年前的想法相反——一些我们认为更难的技能，由于各种原因，结果可能更容易。但编码和数学，因为你可以创建大量合成数据并验证这些数据是否正确。所以由于这种性质，更容易制作合成数据来训练。当然，这也是我们都感兴趣的领域，因为作为程序员，它可以帮助我们，让我们更快、更高效。所以我认为在下一个时代，比如未来 5 到 10 年，我们会发现那些拥抱这些技术的人，无论是在创意产业还是技术产业，都会变得几乎与它们融为一体，从而变得超乎人类的生产力。

Well, it's interesting that programming, and it's again counterintuitive to what we thought years ago maybe, that some of the skills that we think of as harder skills turned out maybe to be the easier ones for various reasons. But coding and math, because you can create a lot of synthetic data and verify if that data is correct. So because of that nature, it's easier to make things like synthetic data to train from. It's also an area of course we're all interested in because as programmers, right, to help us and get faster at it and more productive. So I think for the next era, like the next 5 to 10 years, I think what we're going to find is people who kind of embrace these technologies become almost at one with them, whether that's in the creative industries or the technical industries, will become sort of superhumanly productive, I think.

所以伟大的程序员会变得更好，他们会比现在强 10 倍，因为他们能够利用自己的技能最大限度地使用这些工具，充分发挥它们。所以我认为这就是我们在下一个领域将要看到的。这将带来相当大的变化，对吧？很多人会从中受益。例如，如果编程变得更容易，更多的创意人员就能用它做更多事情。但我认为顶级程序员仍然会有巨大优势，比如指定架构应该是什么，如何以有用的方式指导这些编程助手，以及检查它们生成的代码是否良好。所以我认为在可预见的未来几年里，还有很大的发展空间。

So the great programmers will be even better, they'll be 10x what they are today, because they'll be able to use their skills to utilize the tools to the maximum, exploit them to the maximum. So I think that's what we're going to see in the next domain. That's going to cause quite a lot of change, right? A lot of people will benefit from that. For example, if coding becomes easier, it becomes available to many more creatives to do more. But I think the top programmers will still have huge advantages in terms of specifying what the architecture should be, how to guide these coding assistants in a useful way, and checking whether the code they produce is good. So I think there's plenty of headroom there for the foreseeable next few years.

所以我认为这里有几点有趣的事情。一是持续提高使用这些工具的能力非常必要。所以他们是在乘着模型改进的浪潮前进。

So I think there's several interesting things there. One is there's a lot of imperative to just get better and better consistently at using these tools. So they're riding the wave of the improving models.

是的。

Yes.

而不是与他们竞争。

Versus competing against them.

但遗憾的是，这就是地球上的生活本质。某些前沿编程可能会有巨大价值，而其他类型的价值则较低。例如，前端网页设计可能更容易由 AI 系统生成，而游戏引擎设计、后端设计或在高性能场景中指导系统、高性能编程类型的设计决策可能极具价值。但这将改变人类最需要的地方，这对人们来说调整起来很可怕。

But sadly, that's the nature of life on Earth. There could be a huge amount of value to certain kinds of programming at the cutting edge and less value to other kinds. For example, front-end web design might be more amenable to generation by AI systems, and maybe game engine design or back-end design or guiding systems in high-performance situations, high-performance programming type of design decisions might be extremely valuable. But it will shift where the humans are needed most, and that's scary for people to adjust.

我认为没错。每当出现大量颠覆和变化时——我们在人类历史上经历过很多次，比如互联网、移动设备，以及之前的工业革命。这将是其中一个充满变化的时代。我认为会出现我们今天无法想象的新工作，就像互联网创造的那样。那些拥有正确技能、能乘上这波浪潮的人会变得极其有价值。但也许人们需要重新学习或调整他们当前的技能。这次更难处理的是，我认为我们将看到的影响大约是工业革命的 10 倍，但速度也快 10 倍。所以不是 100 年，而是 10 年。这相当于影响和速度结合起来的 100 倍。所以我认为这会让社会更难应对。有很多需要思考的问题，我认为我们现在就需要开始讨论。我鼓励顶级经济学家和哲学家开始思考社会将如何受到影响，以及我们应该做什么，包括像全民基本保障这样的东西，其中很多增加的生产力被分享和分配给社会，可能以服务等形式。如果你想要更多，你仍然可以去获得一些极其稀有的技能，让自己变得独特。但会有一个基本保障提供。

I think that's right. Any time there's a lot of disruption and change, and we've had this many times in human history with the internet, mobile, and before that the industrial revolution. It's going to be one of those eras where there will be a lot of change. I think there'll be new jobs we can't even imagine today, just like the internet created. Those people with the right skill sets to ride that wave will become incredibly valuable. But maybe people will have to relearn or adapt their current skills. The thing that's going to be harder to deal with this time around is that I think we're going to see something like probably 10 times the impact the industrial revolution had, but 10 times faster as well. So instead of 100 years, it takes 10 years. That's like a 100x the impact and the speed combined. So that's what I think is going to make it more difficult for society to deal with. There's a lot to think through, and I think we need to be discussing that right now. I encourage top economists and philosophers to start thinking about how society is going to be affected and what we should do, including things like universal basic provision, where a lot of the increased productivity gets shared out and distributed to society, maybe in the form of services and other things. If you want more than that, you still go and get some incredibly rare skills and make yourself unique. But there's a basic provision that is provided.

如果你把政府看作一种技术，那么不仅在经济上，在政治上也有有趣的问题。你如何设计一个能够应对快速变化时代的系统，使得它能代表不同群体感受到的不同痛苦？你如何重新分配资源，以解决这些痛苦，并代表不同人的希望、痛苦和恐惧，而不导致分裂？因为政客们往往非常擅长煽动分裂，并利用这一点来当选，定义他者并说那是坏的。我认为这通常不利于利用快速变化的技术来帮助世界繁荣。所以如果我们把政治体系看作一种技术，我们几乎也需要快速改进它们。

And if you think of government as a technology, there are also interesting questions not just in economics but in politics. How do you design a system that's responding to the rapidly changing times such that you can represent the different pain people feel from different groups? And how do you reallocate resources in a way that addresses that pain and represents the hope and the pain and the fears of different people, without leading to division? Because politicians are often really good at fueling division and using that to get elected, defining the other and saying that's bad. I think that's often counterproductive to leveraging a rapidly changing technology to help the world flourish. So we almost need to improve our political systems as well rapidly, if you think of them as a technology.

当然。我认为我们可能需要新的治理结构和机构来帮助这一转型。所以政治哲学和政治科学将是关键。但我认为首要的事情是创造更多的资源丰裕。这是第一要务：提高生产力，获得更多资源，也许最终摆脱零和局面。然后第二个问题是如何使用和分配这些资源。但如果没有先实现丰裕，你就无法做到这一点。

Definitely. I think we'll need new governance structures and institutions probably to help with this transition. So political philosophy and political science is going to be key to that. But I think the number one thing first of all is to create more abundance of resources. That's the number one thing: increase productivity, get more resources, maybe eventually get out of the zero-sum situation. Then the second question is how to use and distribute those resources. But you can't do that without having that abundance first.

你向我提到过本杰明·拉巴图特的书《狂人》，这本书首先是关于你的传记，但不太清楚有多少是虚构，多少是现实。但我认为中心人物是约翰·冯·诺伊曼。我会说这是一次令人难忘且美丽的探索，关于疯狂、天才以及发现的双刃剑。对于不了解的人来说，约翰·冯·诺伊曼是一位传奇人物。他为量子力学做出了贡献，参与了曼哈顿计划，并被广泛认为是现代计算机和 AI 之父或先驱。所以很多人说，他是有史以来最聪明的人之一。这很迷人。同样迷人的是，作为一个目睹核科学和物理学变成原子弹的人，他看到了想法变成对世界有巨大影响的东西。他也预见到了计算领域的同样情况。这就是这本书美丽而令人难忘的一面。然后向前跳跃，看看 AlphaGo、AlphaZero 那个重大时刻，也许约翰·冯·诺伊曼的思想变成了现实。所以我想问的是：如果你现在能和约翰·冯·诺伊曼一起出去玩，他会对正在发生的事情说什么？

You mentioned to me the book 'The Maniac' by Benjamin Labatut, a book about... first of all, there's a bio about you, it's unclear how much is fiction and how much is reality. But I think the central figure is John von Neumann. I would say it's a haunting and beautiful exploration of madness and genius and the double-edged sword of discovery. For people who don't know, John von Neumann was a legendary mind. He contributed to quantum mechanics, was on the Manhattan Project, and is widely considered the father or pioneer of the modern computer and AI. So as many people say, he's one of the smartest humans ever. It's fascinating. And what's also fascinating is as a person who saw nuclear science and physics become the atomic bomb, he got to see ideas become a thing that has a huge impact on the world. He also foresaw the same thing for computing. That's the beautiful and haunting aspect of the book. Then taking a leap forward, looking at AlphaGo, AlphaZero, that big moment, maybe John von Neumann's thinking was brought to reality. So I guess the question is: what do you think if you got to hang out with John von Neumann now? What would he say about what's going on?

嗯，那将是一次奇妙的经历。

Well, that would be an amazing experience.

你知道，他是一位杰出的思想家。我还喜欢他曾在普林斯顿高等研究院度过大量时间，那是一个非常适于思考的地方。他是一位多么博学的人，涉猎之广令人惊叹，他帮助发明了许多东西，当然包括所有现代计算机所基于的冯·诺依曼架构。他有着非凡的远见。我想他会喜欢我们今天所处的时代。他会非常欣赏 AlphaGo，因为他也研究过博弈论。我认为他预见到了许多关于学习机器的发展——那些更像是被“培育”而非编程的系统。我甚至不确定他是否会对此感到惊讶，因为他在 1950 年代就已经预见到了这一切的实现。

You know, he's a fantastic mind. I also love that he spent a lot of his time at Princeton at the Institute for Advanced Study, a very special place for thinking. It's amazing how much of a polymath he was, the spread of things he helped invent, including of course the von Neumann architecture that all modern computers are based on. He had amazing foresight. I think he would have loved where we are today. He would have really enjoyed AlphaGo, since he also did game theory. I think he foresaw a lot of what would happen with learning machines, systems that are kind of grown rather than programmed. I'm not sure he would even be that surprised by the fruition of what he already foresaw in the 1950s.

我想知道他会给出什么建议。你见证了曼哈顿计划中原子弹的建造。我相信肯定有一些有趣的事情没有被充分讨论。也许是一些官僚层面的东西，也许是政治家的影响，也许是不够主动拿起电话与那些被政治家称为敌人的人交谈。实际上，那个时代可能有一些我们已经遗失的深刻智慧。

I wonder what advice he would give. You got to see the building of the atomic bomb with the Manhattan Project. I'm sure there's interesting stuff that maybe is not talked about enough. Maybe some bureaucratic aspect, maybe the influence of politicians, maybe not enough of picking up the phone and talking to people that are called enemies by the said politicians. There might be some deep wisdom that we just may have lost from that time actually.

是的，我确信。我读过很多那个时代的书，记录了那个时代和一些参与其中的杰出人物。我同意你的看法。我认为可能需要更多的对话和理解。我希望我们能从那些时代中学习。我认为这里的区别在于，AI 有如此多的用途；它是一种多用途技术。显然，我们正在努力做一些事情，比如解决所有疾病，帮助解决能源和稀缺问题，这些了不起的事情。这就是为什么我们所有人，包括我自己，在 30 多年前就开始了这段旅程。但当然也有风险。我猜冯·诺依曼可能预见到了这两方面。我记得他好像对他妻子说过，计算机对世界的影响会更大。正如我们刚才讨论的，我认为这是对的。我认为它的影响至少是工业革命的 10 倍。所以我认为他是对的。我想他会对我们现在所处的位置着迷。

Yeah, I'm sure. I'm sure there is. I mean, I've studied, I read a lot of books from that time as well, chronicling the time and some brilliant people involved. I agree with you. I think maybe there needs to be more dialogue and understanding. I hope we can learn from those times. I think the difference here is that AI has so many uses; it's a multi-use technology. Obviously we're trying to do things like solve all diseases, help with energy and scarcity, these incredible things. This is why all of us, and myself, I started on this journey 30 plus years ago. But of course there are risks too. Probably von Neumann, my guess is he foresaw both. I think he sort of said to his wife that computers would be even more impactful in the world. As we just discussed, I think that's right. I think it's going to be 10 times at least of the industrial revolution. So I think he's right. I imagine he would be fascinated by where we are now.

我认为这本书的一个启示是，理性——正如书中《理性的疯狂梦》所说——不足以引导人类建造这些超级强大的技术。还有别的东西。还有一个宗教成分。无论是什么神，什么宗教，它触动了我们人类精神中某种冰冷的理性所无法给予的东西。

I think one of the takeaways from the book is that reason, as said in the book 'Mad Dreams of Reason', is not enough for guiding humanity as we build these super powerful technologies. There's something else. There's also a religious component. Whatever God, whatever religion gives it, it pulls at us something in the human spirit that raw cold reason doesn't give us.

我同意这一点。我认为我们需要以某种方式对待它，无论你称之为精神维度还是人文维度。它不一定与宗教有关，对吧？但关于灵魂、是什么让我们成为人类、我们拥有的这种火花，也许与意识有关——当我们最终理解它时。我认为这必须成为这项事业的核心。技术，我一直将技术视为赋能者，是使我们能够繁荣并更多地了解世界的工具。在这一点上我有点赞同费曼；他过去常说科学和艺术是伴侣。你可以从两个方面理解它：一朵花的美丽，它有多美，同时也理解为什么花的颜色会那样进化。这只会让它更美，花的内在美。我一直是这么看的。也许在文艺复兴时期，像达芬奇这样的伟大发现者，我不认为他看到了科学与艺术之间的区别，也许还有宗教。一切都是作为人类的一部分，受到周围世界的启发。这就是我试图采取的哲学。我最喜欢的哲学家之一是斯宾诺莎，我认为他很好地结合了这一切，这种试图理解宇宙和理解我们在其中位置的想法，这是他理解宗教的方式。我认为这很美。对我来说，所有这些事情都是相关的、相互关联的：技术和作为人的意义。我认为当我们沉浸在技术和研究中时，记住这一点非常重要。我认为我们领域中的许多研究人员有点过于狭隘，只理解技术。这也是为什么这件事需要被整个社会讨论的原因。我非常支持像即将举行的 AI 峰会这样的事情，以及政府理解它。我认为聊天机器人时代和 AI 产品时代的一个好处是，普通人实际上可以感受并与前沿 AI 互动，亲自感受它。

I agree with that. I think we need to approach it with whatever you want to call it, a spiritual dimension or humanist dimension. It doesn't have to be to do with religion, right? But this idea of a soul, what makes us human, this spark that we have perhaps is to do with consciousness when we finally understand that. I think that has to be at the heart of the endeavor. Technology, I've always seen technology as the enabler, the tools that enable us to flourish and to understand more about the world. I'm sort of with Feynman on this; he used to always talk about science and art being companions. You can understand it from both sides, the beauty of a flower, how beautiful it is, and also understand why the colors of the flower evolved like that. That just makes it more beautiful, the intrinsic beauty of the flower. I've always sort of seen it like that. Maybe in the Renaissance times, the great discoverers like Da Vinci, I don't think he saw any difference between science and art, and perhaps religion. Everything was just part of being human and being inspired about the world around us. That's the philosophy I try to take. One of my favorite philosophers is Spinoza, and I think he combined that all very well, this idea of trying to understand the universe and understanding our place in it, and that was his kind of way of understanding religion. I think that's quite beautiful. For me, all of these things are related, interrelated: the technology and what it means to be human. I think it's very important that we remember that when we're immersed in the technology and the research. I think a lot of researchers in our field are a little bit too narrow and only understand the technology. That's also why it's important for this to be debated by society at large. I'm very supportive of things like the AI summits that will happen and governments understanding it. I think one good thing about the chatbot era and the product era of AI is that everyday people can actually feel and interact with cutting-edge AI and feel it for themselves.

是的。因为这迫使技术专家进行人性化的对话。这是它充满希望的一面。就像你说的，这是一种双重用途的技术，我们正在强行将整个人类纳入关于 AI 的讨论中，因为最终 AI/AGI 将被用于国家使用技术的目的，即冲突等等。我们通过与他们聊天越多地将人类融入这幅图景，就越能引导社会适应。社会将能够适应这些技术，就像我们过去总是适应我们发明的不可思议的技术一样。

Yeah. Because they force the technologist to have the human conversation. That's the hopeful aspect of it. Like you said, it's a dual-use technology that we're forcefully integrating the entire of humanity into the discussion about AI, because ultimately AI AGI will be used for the things that states use technologies for, which is conflict and so on. The more we integrate humans into this picture by having chats with them, the more it will guide society to be able to adapt. Society will be able to adapt to these technologies like we've always done in the past with the incredible technologies we've invented.

你认为会出现像曼哈顿计划那样的事情吗？这项技术的力量会升级，而国家以它们旧的思维方式会试图将其用作武器技术，从而出现这种升级？

Do you think there will be something like a Manhattan Project where there will be an escalation of the power of this technology and states in their old way of thinking will try to use it as weapons technologies and there will be this kind of escalation?

我希望不会。我认为这样做非常危险，也不是对技术的正确使用。我希望我们最终能达成更合作的方式，如果需要的话，更像一个 CERN 项目，以研究为重点，让世界上最优秀的人才聚集在一起，仔细完成最后步骤，并确保在向世界部署之前负责任地完成。我们拭目以待。我的意思是，在目前的地缘政治气候下，合作很难，但情况可能会改变。我认为至少在科学层面上，研究人员保持联系并在至少这些话题上保持密切是很重要的。

I hope not. I think that would be very dangerous to do and also not the right use of the technology. I hope we'll end up with something more collaborative if needed, more like a CERN project, where it's research focused and the best minds in the world come together to carefully complete the final steps and make sure it's responsibly done before deploying it to the world. We'll see. I mean, it's difficult with the current geopolitical climate to see cooperation, but things can change. I think at least on the scientific level, it's important for the researchers to keep in touch and keep close to each other on at least those kinds of topics.

我个人认为，在教育方面和移民方面，如果双向流动——西方人移民到中国，中国人移民到西方——那就太好了。我的意思是，人们相互融合，这其中有人性的一面。

And I personally believe on the education side and immigration side, it would be great if both directions—people from the West immigrated to China and vice versa. I mean there is some human aspect of people just intermixing.

是的。

Yeah.

这样，这些纽带就会变得牢固。所以你不能用那种老式的思维方式来分裂彼此。因此，多文化、多学科的研究团队共同攻克科学问题——这就是希望所在。不要让那些好战分子分裂我们。我认为科学最终是一个真正美好的连接器。

And thereby those ties grow strong. So you can't sort of divide against each other with this old-school way of thinking. So multicultural, multidisciplinary research teams working on scientific questions—that's like the hope. Don't let the warmongers divide us. I think science is ultimately a really beautiful connector.

是的。我认为科学一直是一项相当协作的事业，科学家们也知道这是一项集体努力，我们可以互相学习。所以也许它可以成为促进合作的一个载体。你那荒谬的问题是什么？你的 p(doom) 是多少？人类文明自我毁灭的概率。

Yeah. Science has always been, I think, quite a collaborative endeavor, and you know scientists know that it's a collective endeavor as well, and we can all learn from each other. So perhaps it could be a vector to get a bit of cooperation. What's your ridiculous question? What's your p(doom)? Probability that human civilization destroys itself.

嗯，我没有一个 p(doom) 数字。我没有的原因是因为我认为这会暗示一种不存在的精确度。所以，我不知道人们是怎么得出他们的 p(doom) 数字的。我认为这有点荒谬，因为我要说的是它肯定非零，而且可能不可忽略。这本身就相当令人警醒，我的观点是它非常不确定，对吧？这些技术能做什么，它们发展得多快，它们有多可控。有些事情可能会证明，并且希望如此，比我们想象的要容易得多。但也可能有一些非常困难的问题比我们今天猜测的更难，而且我认为我们并不确定。所以在这些高度不确定但风险巨大的条件下——一方面，我们可以解决所有疾病、能源问题、稀缺问题，然后前往星辰大海，实现人类最大繁荣；另一方面，是这种 p(doom) 情景。因此，考虑到不确定性和重要性，对我来说，唯一理性、明智的方法就是带着谨慎的乐观前进。所以我们想要结果。我们当然想要好处，以及人工智能能带来的所有惊人事物。实际上，如果考虑到我们面临的其他挑战——气候、疾病、衰老、资源等等——如果我不知道像人工智能这样的东西即将到来，我会非常担心人类，对吧？我们如何解决所有其他问题？我认为很难。所以我认为它可以为善带来惊人的变革。但另一方面，这些风险我们知道存在，但无法完全量化。所以最好的办法是用科学方法做更多研究，试图更精确地定义这些风险，当然也要解决它们。我认为这就是我们正在做的。我认为随着我们越来越接近 AGI 的界限，现在需要付出比现在多十倍的努力。

Well, look, I don't have a p(doom) number. The reason I don't is because I think it would imply a level of precision that is not there. So, like I don't know how people are getting their p(doom) numbers. I think it's a kind of a little bit of a ridiculous notion because what I would say is it's definitely nonzero and it's probably non-negligible. So that in itself is pretty sobering, and my view is it's just hugely uncertain, right? What these technologies are going to be able to do, how fast they are going to take off, how controllable they are going to be. Some things may turn out to be, and hopefully, way easier than we thought. But it may be there are some really hard problems that are harder than we guess today, and I think we don't know that for sure. So under those conditions of a lot of uncertainty but huge stakes both ways—on the one hand, we could solve all diseases, energy problems, the scarcity problem, and then travel to the stars and maximum human flourishing; on the other hand, is this sort of p(doom) scenarios. So given the uncertainty around it and the importance of it, it's clear to me the only rational, sensible approach is to proceed with cautious optimism. So we want the outcome. We want the benefits, of course, and all the amazing things that AI can bring. And actually, I would be really worried for humanity if, given the other challenges that we have—climate, disease, aging, resources, all of that—if I didn't know something like AI was coming down the line, right? How would we solve all those other problems? I think it's hard. So I think it could be amazingly transformative for good. But on the other hand, there are these risks that we know are there but we can't quite quantify. So the best thing to do is to use the scientific method to do more research to try and more precisely define those risks and of course address them. And I think that's what we're doing. I think there probably needs to be 10 times more effort on that than there is now as we're getting closer and closer to the AGI line.

你更担心的是什么？是人类造成的，还是人工智能/AGI 造成的？

What would be the source of worry for you more? Would it be human-caused or AI/AGI-caused?

人类滥用这项技术，对比 AGI 本身通过你提到的那些迷人机制——欺骗之类的东西——秘密地变得越来越好，然后……

Humans abusing that technology versus AGI itself through mechanisms that you've spoken about which is fascinating—deception or this kind of stuff—getting better and better and better secretly and then...

我认为它们在不同的时间尺度上运作，并且同样重要，需要解决。所以有常见的坏人利用新技术，在这种情况下是通用技术，将其用于有害目的，这是一个巨大的风险。我认为这有很多复杂性，因为通常我非常支持开放科学和开源，事实上我们所有的科学项目如 AlphaFold 等都是这样做的，为了科学界的利益。但如何限制坏人——无论是个人还是流氓国家——接触这些强大系统，同时允许好人接触以最大限度地在此基础上发展？这是一个非常棘手的问题，我还没有听到明确的解决方案。所以有坏人使用案例的问题，然后显然随着系统变得更具智能体性、更接近 AGI 和更自主，我们如何确保护栏，并让它们坚持做我们想做的事，并处于我们的控制之下。

I think they operate over different time scales and they're equally important to address. So there's just the common garden variety of bad actors using new technology, in this case general-purpose technology, and repurposing it for harmful ends, and that's a huge risk. And I think that has a lot of complications because generally I'm in huge favor of open science and open source, and in fact we did it with all our science projects like AlphaFold and all of those things for the benefit of the scientific community. But how does one restrict bad actors' access to these powerful systems, whether they're individuals or even rogue states, but enable access at the same time to good actors to maximally build on top? It's a pretty tricky problem that I've not heard a clear solution to. So there's the bad actor use case problem, and then there's obviously as the systems become more agentic and closer to AGI and more autonomous, how do we ensure the guardrails and they stick to what we want them to do and under our control.

是的，我倾向于——也许我的想法有限——更担心人类，那些坏人。

Yeah, I tend to—maybe on my mind is limited—worry more about the humans, the bad actors.

这可能是，部分地，如何不把破坏性技术交到坏人手中，但另一方面，从地缘政治技术角度来看，如何减少世界上的坏人数量？这也是一个有趣的人类问题。

And there it could be, in part, how do you not put destructive technology in the hands of bad actors, but in another part from a geopolitical technology perspective, how do you reduce the number of bad actors in the world? That's also an interesting human problem.

是的，这是一个难题。我的意思是，我们也许也可以利用技术本身来帮助对某些坏人使用案例进行早期预警，对吧？无论是生物、核还是其他什么，人工智能可能在那里有帮助，只要你所使用的人工智能本身是可靠的。所以这是一个相互关联的问题，这就是为什么它非常棘手。而且，这可能需要国际上的某种协议，至少在中国和美国之间，达成一些基本标准。

Yeah, it's a hard problem. I mean, look, we can maybe also use the technology itself to help with early warning on some of the bad actor use cases, right? Whether that's bio or nuclear or whatever it is, AI could be potentially helpful there as long as the AI that you're using is itself reliable. So it's a sort of interlocking problem, and that's what makes it very tricky. And again, it may require some agreement internationally, at least between China and the US, of some basic standards.

我必须问你关于《The Maniac》这本书。有一个“上帝之手”的时刻——李世石的 78 手——也许是最后一次人类做出纯粹人类天才的一手，击败了 AlphaGo，或者说打破了它的“大脑”，如果抱歉拟人化的话。但这是一个有趣的时刻，因为我认为在很多领域，这种情况会不断发生。

I have to ask you about the book 'The Maniac'. There's this 'Hand of God' moment—Lee Sedol's move 78—that perhaps the last time a human did a move of sort of pure human genius and beat AlphaGo, or like broke its brain, if sorry to anthropomorphize. But it's an interesting moment because I think in so many domains it will keep happening.

是的，这是一个特殊的时刻，你知道这对李世石来说很棒，我认为在某种程度上他们互相激励。我们作为团队被李世石的才华和高尚所激励，然后也许他被 AlphaGo 的表现所启发，从而创造了这个令人难以置信的鼓舞人心的时刻。这一切都在关于它的纪录片中很好地捕捉到了，我认为这将在许多领域继续下去，至少在可预见的未来，人类发挥他们的聪明才智，提出正确的问题，然后利用这些工具来解决问题。

Yeah, it's a special moment, and you know it was great for Lee Sedol, and I think in a way they were kind of inspiring each other. We as a team were inspired by Lee Sedol's brilliance and nobleness, and then maybe he got inspired by what AlphaGo was doing to then conjure this incredible inspirational moment. It's all captured very well in the documentary about it, and I think that'll continue in many domains where there's this, at least for the foreseeable future, of humans bringing in their ingenuity and asking the right question, let's say, and then utilizing these tools in a way that then cracks a problem.

是的。随着人工智能变得越来越聪明，我们可以问自己的一个有趣问题是，是什么让人类与众不同？我确实觉得——也许我有偏见——我们人类非常特别。我不知道是不是我们的智力。

Yeah. As AI becomes smarter and smarter, one of the interesting questions we can ask ourselves is what makes humans special? It does feel—perhaps I'm biased—that we humans are deeply special. I don't know if it's our intelligence.

它可能是理性疯狂梦境之外的其他东西。我想这就是我小时候开始这段旅程时一直想象的。我对意识之类的东西着迷，读了神经科学博士，研究大脑如何工作，特别是想象力和记忆，专注于海马体。这很有趣。我一直认为，最好的方式是进行哲学思考和思想实验，甚至像神经科学那样在真实大脑上做实际实验，但最终，我设想构建 AI——一种智能人造物——然后与人类心智比较，看看差异在哪里，这将是揭示人类心智独特之处的最佳方式，如果确实有独特之处的话。我怀疑很可能有，但很难定义。我认为我们正在进行的这段旅程将帮助我们理解这一点。碳基基质（我们）和硅基基质在处理信息时可能存在差异。我喜欢的一个关于意识的最佳定义是：意识是信息被处理时的感受。这不是一个很有帮助的科学解释，但这是一个有趣的直觉。这段科学旅程将有助于揭开那个谜团。

It could be something else that's outside the mad dreams of reason. I think that's what I've always imagined when I was a kid starting on this journey. I was fascinated by things like consciousness, did a neuroscience PhD to look at how the brain works, especially imagination and memory, focusing on the hippocampus. It's interesting. I always thought the best way to philosophize about it and have thought experiments, or even do actual experiments like in neuroscience on real brains, but in the end, I imagined that building AI, a kind of intelligent artifact, and comparing that to the human mind, seeing what the differences were, would be the best way to uncover what's special about the human mind, if indeed there is anything special. I suspect there probably is, but it's going to be hard to define. I think this journey we're on will help us understand that. There may be a difference between carbon-based substrates that we are and silicon ones when they process information. One of the best definitions I like of consciousness is that it's the information feels when we process it. It's not a very helpful scientific explanation, but it's an interesting intuitive one. This scientific journey will help uncover that mystery.

“我无法创造的东西，我就无法理解。”这是你深深敬佩的理查德·费曼说的，就像你提到的。你还提到了维格纳对普遍性的梦想，他在受限领域以及更广泛的数学等领域看到了这一点。你在很多方面都在推动。

What I cannot create I do not understand. That's somebody you deeply admire, Richard Feynman, like you mentioned. You also reach for the Wigner's dreams of universality that he saw in constrained domains but also broadly in mathematics and so on. So many aspects on which you're pushing towards.

最后不想惹麻烦，但罗杰·彭罗斯。

Not to start trouble at the end, but Roger Penrose.

是的。那么，你认为意识，这个意识的难题，信息如何有感受。首先，你认为意识是一种计算吗？如果是，如果像你说的那样是信息处理，一切都是。它是否可以用经典计算机建模？还是本质上是量子力学的？

Yes. So, do you think consciousness, there's this hard problem of consciousness, how information feels. Do you think consciousness, first of all, is a computation? And if it is, if it's information processing like you said, everything is. Is it something that could be modeled by a classical computer? Or is it quantum mechanical in nature?

嗯，你看，彭罗斯是一位了不起的思想家，现代最伟大的人物之一，我们对此有过很多讨论。当然，我们友好地持不同意见。我觉得他与许多优秀的神经科学家合作，试图在大脑中找到量子力学行为的机制。据我所知，他们还没有找到任何令人信服的证据。所以我打赌，大脑中主要只是经典计算在运行，这意味着所有现象都可以用经典计算机建模或模仿。但我们会看到的。可能还有最后的神秘之处，即意识的感受、感受质这类哲学家争论的东西，它们对基质是独特的。当我们做像 Neuralink 这样的事情，并与 AI 系统建立神经接口时，我们甚至可能开始理解这一点，我认为我们最终可能会这样做，也许是为了跟上 AI 系统。我们可能真的能自己感受到在硅上计算是什么感觉。也许那会告诉我们答案。所以我认为这会很有趣。我曾与已故的丹尼尔·丹尼特辩论过，为什么我们认为彼此有意识。有两个原因。一是你表现出和我一样的行为。所以从行为上看，如果我有意识，你似乎也有意识。但第二个经常被忽视的原因是，我们运行在相同的基质上。所以如果你行为相同，且运行在相同基质上，最简洁的假设是你和我有相同的感受。但对于运行在硅上的 AI，我们无法依赖第二部分。即使它表现出第一部分，行为看起来像有意识的存在，它甚至可能声称自己有意识。但我们不知道它实际感受如何。而且它可能也不知道我们的感受，至少在最初阶段。也许当我们达到超级智能及其构建的技术时，我们能够弥合这一差距。

Well, look, Penrose is an amazing thinker, one of the greatest of the modern era, and we've had a lot of discussions about this. Of course, we cordially disagree. I feel like he collaborated with a lot of good neuroscientists to see if he could find mechanisms for quantum mechanical behavior in the brain. And they, to my knowledge, haven't found anything convincing yet. So my betting is that it is mostly just classical computing going on in the brain, which suggests that all the phenomena are modelable or mimickable by a classical computer. But we'll see. There may be this final mysterious thing of the feeling of consciousness, the qualia, these kinds of things that philosophers debate, where it's unique to the substrate. We may even come towards understanding that when we do things like Neuralink and have neural interfaces to AI systems, which I think we probably will eventually, maybe to keep up with the AI systems. We might actually be able to feel for ourselves what it's like to compute on silicon. And maybe that will tell us. So I think it's going to be interesting. I had a debate once with the late Daniel Dennett about why we think each other are conscious. It's for two reasons. One is you're exhibiting the same behavior that I am. So behaviorally you seem like a conscious being if I am. But the second thing, which is often overlooked, is that we're running on the same substrate. So if you're behaving in the same way and we're running on the same substrate, it's most parsimonious to assume you're feeling the same experience that I'm feeling. But with an AI that's on silicon, we won't be able to rely on the second part. Even if it exhibits the first part, the behavior looks like that of a conscious being. It might even claim it is. But we wouldn't know how it actually felt. And it probably couldn't know what we felt, at least in the first stages. Maybe when we get to superintelligence and the technologies it builds, perhaps we'll be able to bridge that.

不，我的意思是，这对激进同理心是一个巨大的考验，即与不同基质共情。

No, I mean that's a huge test for radical empathy is to empathize with a different substrate.

对。正是。我们以前从未需要面对这一点。

Right. Exactly. We never had to confront that before.

是的。所以也许通过脑机接口，我们能够真正共情成为一台计算机是什么感觉，信息不是在碳基系统上计算。我的意思是，这很深奥……有些人会想到植物，其他生命形式，它们不同，基质相似但在进化树上足够远，需要激进同理心。但和计算机这样做……

Yeah. So maybe through brain-computer interfaces we'll be able to truly empathize what it feels like to be a computer, for information to be computed not on a carbon system. I mean that's deeply... I mean some people kind of think about that with plants, with other life forms which are different, similar substrate but sufficiently far enough on the evolutionary tree that it requires a radical empathy. But to do that with a computer...

我的意思是，对此有动物研究。当然，高等动物如虎鲸、海豚、狗、猴子、大象，它们有意识的某些方面，尽管在智商意义上可能不那么聪明。所以我们已经能够与之共情。也许有一天我们的一些系统，比如我们构建了一个叫 DolphinGemma 的东西，是我们系统的一个版本，用海豚和鲸鱼的声音训练，也许我们能在某个时候构建一个解释器或翻译器，那应该很酷。

I mean, there are animal studies on this. Of course, higher animals like killer whales, dolphins, dogs, monkeys, elephants, they have some aspects of consciousness, even though they might not be that smart in an IQ sense. So we can already empathize with that. And maybe even some of our systems one day, like we built this thing called DolphinGemma, a version of our system trained on dolphin and whale sounds, and maybe we'll be able to build an interpreter or translator at some point, which should be pretty cool.

什么让你对人类文明的未来充满希望？

What gives you hope for the future of human civilization?

嗯，让我充满希望的是，首先我认为我们几乎无限的创造力。我认为我们中最优秀的人，最优秀的人类头脑是不可思议的。我喜欢见到和观察任何在其领域顶尖的人，无论是体育、科学还是艺术。没有什么比看到他们处于最佳状态、心流状态更美妙了。我认为这几乎是无限的。我们的大脑是通用系统，智能系统。所以我认为我们能用它们做的事情几乎是无限的。另一件事是我们极端的适应性。我认为会没事的，尽管会有很多变化。但看看我们现在，带着我们实际上还是狩猎采集者的大脑。我们如何能应对现代世界？坐飞机、做播客、玩电脑游戏和虚拟模拟。它已经适应了我们今天拥有的令人惊叹的 AI 技术。

Well, what gives me hope is I think our almost limitless ingenuity, first of all. I think the best of us and the best human minds are incredible. I love meeting and watching any human that's at the top of their game, whether that's sport or science or art. There's nothing more wonderful than seeing them in their element, in flow. I think it's almost limitless. Our brains are general systems, intelligent systems. So I think it's almost limitless what we can potentially do with them. And then the other thing is our extreme adaptability. I think it's going to be okay in terms of there's going to be a lot of change. But look where we are now with our effectively hunter-gatherer brains. How is it we can cope with the modern world? Flying on planes, doing podcasts, playing computer games and virtual simulations. It's already adapted to this mind-blowing AI technology we have today.

就像，哦，我和聊天机器人说话，完全没问题。

It's like, oh, I talk to chat bots. It's totally fine.

而且很有可能，我现在参与的这档播客活动，会被 AI 完全取代。我非常容易被替代，我正等着呢……

And it's very possible that this very podcast activity, which I'm here for, will be completely replaced by AI. I'm very replaceable and I'm waiting for...

还没到你能做到的程度，Lex。所以别这么想……

Not to the level that you can do it, Lex. So don't think...

谢谢。这就是我们人类互相做的事。我们互相赞美。

Thank you. That's what we humans do to each other. We compliment.

好吧。我深深感激我们人类拥有这种无限的好奇心、适应力，就像你说的，还有同情心和爱的能力。

All right. And I'm deeply grateful for us humans to have this infinite capacity for curiosity, adaptability, like you said, and also compassion and ability to love.

正是。

Exactly.

所有这些人类的……所有深深属于人类的东西。

All of those human... all the things that are deeply human.

好吧，这是莫大的荣幸，Demis。你是世界上真正特别的人类之一。非常感谢你所做的一切，以及今天的对话。

Well, this is a huge honor, Demis. You're one of the truly special humans in the world. Thank you so much for doing what you do and for talking today.

好吧，非常感谢你，Lex。感谢收听本期与 Demis 的对话。要支持本播客，请查看描述中的赞助商，并考虑订阅本频道。现在，让我回答一些问题，并试着阐述一些我一直在思考的事情。如果你想提交问题，包括音频和视频形式，请访问 lexfridman.com/am。我收到了很多很棒的问题、想法和请求。我会继续尝试随机挑选一些，并在每期节目末尾进行评论。

Well, thank you very much, Lex. Thanks for listening to this conversation with Demis. To support this podcast, please check out our sponsors in the description and consider subscribing to this channel. And now, let me answer some questions and try to articulate some things I've been thinking about. If you would like to submit questions, including in audio and video form, go to lexfridman.com/am. I got a lot of amazing questions, thoughts, and requests from folks. I'll keep trying to pick some randomly and comment on it at the end of every episode.

今年 5 月 21 日，我收到一条留言说：“嗨，Lex。20 年前的今天，大卫·福斯特·华莱士在肯扬学院发表了著名的《这就是水》演讲。你怎么看这个演讲？”首先，我认为这可能是史上最伟大、最独特的毕业典礼演讲之一。当然，我还有很多其他最喜欢的，比如史蒂夫·乔布斯的。大卫·福斯特·华莱士是我最喜欢的作家之一，也是我最喜欢的人之一。他的作品有一种悲剧性的诚实。总感觉他一直在与自己的思想进行持续的战斗。而他的写作就像是那场战斗前线的笔记。现在，来说说这个演讲。让我引用一些部分。当然，有那个鱼和水的寓言：“有两条小鱼在游泳，碰巧遇到一条老鱼从对面游来，老鱼向他们点头说：‘早上好，孩子们。水怎么样？’两条小鱼继续游了一会儿，然后其中一条看着另一条说：‘水到底是什么？’”在演讲中，大卫·福斯特·华莱士接着说：“鱼故事的重点仅仅是，最明显、最重要的现实往往是最难看见和谈论的。”作为一个英文句子说出来。当然，这只是一个陈腐的套话。但事实是，在成年生活的日常战壕里，陈腐的套话可能具有生死攸关的重要性。至少我想在这个干燥而可爱的早晨向你们提出这一点。我从这个寓言和随后的演讲中得到了几个启示。首先，我认为我们必须质疑一切，尤其是关于我们现实、生活和存在本质的最基本假设，而且这个工程在某种根本意义上是高度个人化的。在这个发现过程中，没有人能真正帮助你。我认为大卫·福斯特·华莱士在这里的行动号召，用他的话来说，就是“少一点傲慢，多一点对自身和自身确定性的批判意识”。因为我自动确信的很大一部分东西，结果证明是完全错误和自欺欺人的。好了，回到我，Lex 在说话。第二个启示是，我们生活中核心的精神战斗不是在某个山顶的冥想静修处进行的，而是在日常生活的平凡时刻中进行的。第三个启示是，我们太容易把时间和注意力交给世界塞给我们的无数分心事物——那些永不满足的注意力黑洞。大卫·福斯特·华莱士在这种情况下呼吁我们要深刻意识到每一刻的美，并在平凡中寻找意义。我经常引用大卫·福斯特·华莱士的建议：生活的关键是变得不无聊。我认为这完全正确。每一刻、每一个物体、每一次经历，只要足够仔细地观察，都蕴含着无限的丰富性等待探索。

I got a note on May 21st this year that said, "Hi, Lex. 20 years ago today, David Foster Wallace delivered his famous 'This is Water' speech at Kenyon College. What do you think of this speech?" Well, first I think this is probably one of the greatest and most unique commencement speeches ever given. But of course, I have many favorites, including the one by Steve Jobs. And David Foster Wallace is one of my favorite writers and one of my favorite humans. There's a tragic honesty to his work. And it always felt as if he was engaging in a constant battle with his own mind. And his writing were kind of his notes from the front lines of that battle. Now, onto the speech. Let me quote some parts. There's of course the parable of the fish and the water that goes, "There are these two young fish swimming along, and they happen to meet an older fish swimming the other way who nods at them and says, 'Morning boys. How's the water?' And the two young fish swim on for a bit and then eventually one of them looks over at the other and goes, 'What the hell is water?'" In the speech, David Foster Wallace goes on to say, "The point of the fish story is merely that the most obvious important realities are often the ones that are hardest to see and talk about." Stated as an English sentence. Of course, this is just a banal platitude. But the fact is that in the day-to-day trenches of adult existence, banal platitudes can have a life or death importance. Or so I wish to suggest to you in this dry and lovely morning. I have several takeaways from this parable and the speech that follows. First, I think we must question everything and in particular the most basic assumptions about our reality, our life and the very nature of existence and that this project is a deeply personal one in some fundamental sense. Nobody can really help you in this process of discovery. The call to action here, I think, from David Foster Wallace, as he puts it, is to quote, to be just a little less arrogant, to have just a little more critical awareness about myself and my certainties. Because a huge percentage of the stuff that I tend to be automatically certain of is, it turns out, totally wrong and deluded. All right, back to me. Lex speaking. Second takeaway is that the central spiritual battles of our life are not fought on a mountain top somewhere at a meditation retreat but it is fought in the mundane moments of daily life. Third takeaway is that we too easily give away our time and attention to the multitude of distractions that the world feeds us. The insatiable black holes of attention. David Foster Wallace's call to action in this case is to be deeply aware of the beauty in each moment and to find meaning in the mundane. I often quote David Foster Wallace in his advice that the key to life is to be unborable. And I think this is exactly right. Every moment, every object, every experience when looked at closely enough contains within it infinite richness to explore.

既然本播客的 Demis 和我都是理查德·费曼的粉丝，请允许我也引用费曼先生关于这个话题的话。引用：“我有一个朋友是艺术家，他有时会持一种我不太同意的观点。他会举起一朵花说：‘看这多美。’我会同意。然后他说：‘我作为艺术家，能看到这有多美，但你作为科学家，把它拆解开来，它就变得无趣了。’我觉得这有点荒谬。首先，他看到的美丽，我相信其他人也能看到，我也能看到。虽然我的审美可能不如他那么精致，但我能欣赏一朵花的美。同时，我看到的关于花的东西比他多。我能想象里面的细胞，内部复杂的活动，这些也有美。我的意思是，美不仅仅存在于 1 厘米这个维度。在更小的维度上也有美。内部结构，还有过程。花的颜色进化是为了吸引昆虫授粉，这个事实很有趣。这意味着昆虫能看到颜色。这引出了一个问题：这种审美感在低等生物中也存在吗？为什么它是审美的？各种各样有趣的问题，科学知识只会增加对一朵花的兴奋、神秘和敬畏。它只会增加。”好了，回到大卫·福斯特·华莱士的演讲。他里面有一个我特别喜欢的故事。故事是这样的：“有两个人坐在阿拉斯加偏远荒野的一个酒吧里。其中一个是信徒，另一个是无神论者，两人在争论上帝的存在，那种特别的激烈程度大约是在第四杯啤酒之后。无神论者说：‘听着，并不是我没有不相信上帝的实际理由。也不是我没有尝试过整个上帝和祈祷的事。就在上个月，我离开营地时遇到了可怕的暴风雪，完全迷路了，什么也看不见，气温零下 50 度。于是我试了。我跪在雪地里大喊：“哦上帝，如果有上帝的话，我在这暴风雪中迷路了，如果你不帮我，我会死的。”’现在回到酒吧，信徒困惑地看着无神论者。‘那么你现在一定相信了，’他说。毕竟，你还活着。无神论者翻了个白眼。‘不，老兄。只是碰巧有几个爱斯基摩人路过，给我指了回营地的路。’”

And since Demis of this very podcast episode and I are such fans of Richard Feynman, allow me to also quote Mr. Feynman on this topic as well. Quote, "I have a friend who's an artist and has sometimes taken a view which I don't agree with very well. He'll hold up a flower and say, 'Look how beautiful it is.' And I'll agree. Then he says, 'I, as an artist, can see how beautiful this is, but you as a scientist take this all apart and it becomes a dull thing.' And I think that's kind of nutty. First of all, the beauty that he sees is available to other people and to me too, I believe. Although I may not be quite as refined aesthetically as he is, I can appreciate the beauty of a flower. At the same time, I see much more about the flower than he sees. I can imagine the cells in there, the complicated actions inside which also have beauty. I mean it's not just beauty at this dimension at 1 cm. There's also beauty at the smaller dimensions. The inner structure also the processes. The fact that the colors in the flower evolved in order to attract insects to pollinate it is interesting. It means that the insects can see the color. It adds a question. Does this aesthetic sense also exist in lower forms? Why is it aesthetic? All kinds of interesting questions which the science knowledge only adds to the excitement, the mystery and the awe of a flower. It only adds." All right back to David Foster Wallace's speech. He has a great story in there that I particularly enjoy. It goes, "There are these two guys sitting together in a bar in the remote Alaskan wilderness. One of the guys is religious. The other is an atheist and the two are arguing about the existence of God with that special intensity that comes after about the fourth beer. And the atheist says, 'Look, it's not like I don't have actual reasons for not believing in God. It's not like I haven't ever experimented with the whole God and prayer thing. Just last month, I got caught away from the camp in that terrible blizzard and I was totally lost and I couldn't see a thing and it was 50 below. And so I tried it. I fell to my knees in the snow and cried out, "Oh God, if there is a God, I'm lost in this blizzard and I'm going to die if you don't help me." And now back in the bar, the religious guy looks at the atheist all puzzled. 'Well, then you must believe now,' he says. After all, there you are alive. The atheist just rolls his eyes. 'No, man. All that happened was a couple of Eskimos happened to be wandering by and showed me the way back to the camp.'"

这一切，我认为，告诉我们万事万物都关乎视角，而智慧可能会到来，如果我们有谦卑之心，不断转换和扩展我们对世界的视角。谢谢你让我谈谈大卫·福斯特·华莱士。他是我最喜欢的作家之一，他有一个美丽的灵魂。

All this, I think, teaches us that everything is a matter of perspective and that wisdom may arrive if we have the humility to keep shifting and expanding our perspective on the world. Thank you for allowing me to talk a bit about David Foster Wallace. He's one of my favorite writers and he's a beautiful soul.

如果可以的话，我还想简短地评论一件事。我发现自己处于一种奇怪的境地，经常在网上受到来自各方的攻击，有时是通过选择性歪曲来撒谎，但更多时候是彻头彻尾的谎言。我不知道还能怎么说。坦白说，这让我心碎，但我已经明白这是互联网的常态，也是我选择这条道路的代价。有些日子，我在精神上很艰难。被谎言中伤并不好玩，尤其是当它涉及那些长期以来一直是我快乐和喜悦源泉的事情时。但话说回来，这就是生活。我会继续以同理心和严谨的态度探索人与思想的世界，尽可能坦率地表达自己。对我来说，这是唯一的生活方式。

If I may, one more thing I wanted to briefly comment on. I found myself to be in this strange position of getting attacked online often from all sides, including being lied about sometimes through selective misrepresentation, but often through downright lies. I don't know how else to put it. This all breaks my heart, frankly, but I've come to understand that it's the way of the internet and the cost of the path I've chosen. There's been days when it's been rough on me mentally. It's not fun being lied about, especially when it's about things that are usually for a long time have been a source of happiness and joy for me. But again, that's life. I'll continue exploring the world of people and ideas with empathy and rigor, wearing my heart on my sleeve as much as I can. For me, that's the only way to live.

总之，一个常见的攻击是关于我在麻省理工学院和德雷塞尔大学的经历，这两所我深爱并极其尊重的优秀大学。由于网上关于这些话题的谎言已经积累到令人悲哀甚至有时可笑的程度，我想再次为少数可能关心的人陈述我简历中的明显事实。长话短说，两件事。第一，正如我经常说的，包括在最近一期被数百万人收听的播客中，我自豪地在德雷塞尔大学获得了学士、硕士和博士学位。第二，我是麻省理工学院的研究科学家，过去 10 年一直担任带薪研究职位。现在请允许我详细说明这两点，但如果这不感兴趣，请跳过。

Anyway, a common attack on me is about my time at MIT and Drexel, two great universities I love and have tremendous respect for. Since a bunch of lies have accumulated online about me on these topics to a sad and at times hilarious degree, I thought I would once more state the obvious facts about my bio for the small number of you who may care. TLDDR, two things. First, as I say often, including in a recent podcast episode that somehow was listened to by many millions of people, I proudly went to Drexel University for my bachelor's, master's, and doctoral degrees. Second, I am a research scientist at MIT and have been there in a paid research position for the last 10 years. Allow me to elaborate a bit more on these two things now, but please skip if this is not at all interesting.

所以，就像我说的，一个常见的攻击是我与麻省理工学院没有真正的关联。我猜，指控是我虚假声称与麻省理工学院有关联，因为我曾在那里讲过一堂课。不。那个指控完全是谎言。我从 2015 年至今在麻省理工学院担任带薪研究职位超过 10 年。更清楚地说，我是麻省理工学院计算学院信息与决策系统实验室（LIDS）的研究科学家。目前，因为我仍在麻省理工学院，你可以在目录和各个实验室页面上看到我。这些年来我确实在麻省理工学院做过很多讲座，其中一小部分我上传到了网上。教学对我来说一直只是乐趣，不是研究工作的一部分。我个人认为自己教得很差，但我总是从经历中学习和成长。就像费曼说的，如果你想深入理解某件事，试着去教它是个好方法。但就像我说的，我的主要焦点一直是研究。我发表了许多同行评审的论文，你可以在我的谷歌学术资料中看到。在麻省理工学院的头四年，我工作极其投入。大多数周都是 80 到 100 小时的工作周。之后，在 2019 年，我仍然保留了研究科学家的职位，但分出一部分时间，迈出一步，在麻省理工学院之外追求人工智能和机器人项目，并将大量精力投入到播客中。正如我所说，我一直惊讶于准备一集节目需要多少小时。播客的许多集，我需要在数周或数月内阅读、写作和思考 100、200 甚至更多小时。自 2020 年以来，我没有积极发表研究论文。就像播客一样，我认为这是一项严肃的全职工作。但不发表和不做全职研究一直让我耿耿于怀，因为我热爱研究，热爱编程和构建系统来测试有趣的技术想法，尤其是在人机交互或人机协作的背景下。我希望在未来几个月和几年里改变这一点。我对自己意识到的是，如果我不发表论文或不推出人们使用的系统，我确实感觉缺少了一部分。这确实是我幸福的一个来源。总之，我为在麻省理工学院的时光感到自豪。我过去和现在都一直被比我聪明得多的人包围，其中许多人已成为终生的同事和朋友。麻省理工学院是我逃离世界、专注于探索科学和工程前沿迷人问题的地方。这再次让我真正快乐。而当我在这一点上受到攻击时，心理上确实打击很大。也许我做错了什么。如果是这样，我会努力做得更好。在所有这些关于学术工作的讨论中，我希望你知道，我从未想声称自己是任何方面的专家。在播客和我的私人生活中，我不自称聪明。事实上，我经常称自己为白痴，而且是认真的。我尽量多自嘲，总体上更愿意赞美他人。

So, like I said, a common attack on me is that I have no real affiliation with MIT. The accusation, I guess, is that I'm falsely claiming an MIT affiliation because I taught a lecture there once. Nope. That accusation against me is a complete lie. I have been at MIT for over 10 years in a paid research position from 2015 to today. To be extra clear, I'm a research scientist at MIT working in LIDS, the Laboratory for Information and Decision Systems in the College of Computing. For now, since I'm still at MIT, you can see me in the directory and on the various lab pages. I have indeed given many lectures at MIT over the years, a small fraction of which I posted online. Teaching for me always has been just for fun and not part of my research work. I personally think I suck at it, but I have always learned and grown from the experience. It's like Feynman spoke about, if you want to understand something deeply, it's good to try to teach it. But like I said, my main focus has always been on research. I published many peer-reviewed papers that you can see in my Google Scholar profile. For my first four years at MIT, I worked extremely intensively. Most weeks were 80 to 100 hour work weeks. After that, in 2019, I still kept my research scientist position, but I split my time taking a leap to pursue projects in AI and robotics outside MIT and to dedicate a lot of focus to the podcast. As I've said, I've been continuously surprised just how many hours preparing for an episode takes. There are many episodes of the podcast for which I have to read, write, and think for 100, 200 or more hours across multiple weeks and months. Since 2020, I have not actively published research papers. Just like the podcast, I think it's something that's a serious full-time effort. But not publishing and doing full-time research has been eating at me because I love research and I love programming and building systems that test out interesting technical ideas, especially in the context of human-AI or human-robot interaction. I hope to change this in the coming months and years. What I've come to realize about myself is if I don't publish or if I don't launch systems that people use, I definitely feel like a piece of me is missing. It legitimately is a source of happiness for me. Anyway, I'm proud of my time at MIT. I was and am constantly surrounded by people much smarter than me, many of whom have become lifelong colleagues and friends. MIT is a place I go to escape the world, to focus on exploring fascinating questions at the cutting edge of science and engineering. This again makes me truly happy. And it does hit pretty hard on a psychological level when I'm getting attacked over this. Perhaps I'm doing something wrong. If I am, I will try to do better. In all this discussion of academic work, I hope you know that I don't ever mean to say that I'm an expert at anything. In the podcast and in my private life, I don't claim to be smart. In fact, I often call myself an idiot and mean it. I try to make fun of myself as much as possible and in general to celebrate others instead.

现在，谈谈德雷塞尔大学，我也热爱它，为它感到自豪，并深深感激在那里的时光。正如我所说，我在德雷塞尔大学获得了计算机科学和电气工程的学士、硕士和博士学位。我多次谈到德雷塞尔，包括，如我提到的，在最近一期播客的结尾，有趣的是，那期关于唐纳德·特朗普的节目被数百万人收听，我在那里回答了一个关于研究生院的问题，并解释了我自己在德雷塞尔的经历以及我多么感激它。如果你感兴趣，请去听那期节目的结尾或观看相关片段。在德雷塞尔，我遇到并与许多杰出的研究人员和导师合作，从他们那里学到了很多关于工程、科学和生活的知识。我从德雷塞尔时光中获得了许多宝贵的东西。首先，我上了大量非常难的数学和理论计算机科学课程。它们教会我如何深入和严谨地思考，也教会我如何努力工作，即使感觉自己太笨找不到技术问题的解决方案也不放弃。其次，那段时间我编程很多，主要是 C 和 C++。我编程了机器人、优化算法、计算机视觉系统、无线网络协议、多模态机器学习系统以及各种物理系统的模拟。正是在这里，我真正培养了对编程的热爱，包括是的，Emacs 和 Kinesis 键盘。那段时间我也读了很多书。我弹了很多吉他，写了很多糟糕的诗，还练了很多柔道和柔术，我对它们赞不绝口。柔术在我 20 多岁时每天让我谦卑，至今每当我有机会训练时仍然如此。总之，我希望那些偶尔被迷人的网络群体冲昏头脑、想要贬低他人的人，不要在其中迷失自己。

Now, to talk about Drexel University, which I also love, am proud of and am deeply grateful for my time there. As I said, I went to Drexel for my bachelor's, master's, and doctoral degrees in computer science and electrical engineering. I've talked about Drexel many times, including, as I mentioned, at the end of a recent podcast, the Donald Trump episode, funny enough, that was listened to by many millions of people, where I answered a question about graduate school and explained my own journey at Drexel and how grateful I am for it. If it's at all interesting to you, please go listen to the end of that episode or watch the related clip. At Drexel, I met and worked with many brilliant researchers and mentors from whom I've learned a lot about engineering, science, and life. There are many valuable things I gained from my time at Drexel. First, I took a large number of very difficult math and theoretical computer science courses. They taught me how to think deeply and rigorously, and also how to work hard and not give up even if it feels like I'm too dumb to find a solution to a technical problem. Second, I programmed a lot during that time, mostly C, C++. I programmed robots, optimization algorithms, computer vision systems, wireless network protocols, multimodal machine learning systems, and all kinds of simulations of physical systems. This is where I really developed a love for programming, including yes, Emacs and the Kinesis keyboard. I also during that time read a lot. I played a lot of guitar, wrote a lot of crappy poetry and trained a lot in judo and jiu-jitsu, which I cannot sing enough praises to. Jiu-jitsu humbled me on a daily basis throughout my 20s and it still does to this very day whenever I get a chance to train. Anyway, I hope that the folks who occasionally get swept up in enchanting online crowds that want to tear down others don't lose themselves in it too much.

归根结底，我仍然认为人性中善多于恶。但我们每个人都是好坏参半的。我知道自己有很多缺点。我说话笨拙，有时会说蠢话，会情绪失控，该友善的时候却太过刻薄。我会陷入偏见的兔子洞，然后才醒悟到更宏大、更真实的现实图景。我是人，你也是。无论好坏。我仍然相信我们共同身处这整个美丽的混乱之中。我爱你们所有人。

In the end, I still think there's more good than bad in people. But we're all, each of us, a mixed bag. I know I am very much flawed. I speak awkwardly. I sometimes say stupid things. I can get irrationally emotional. I can be too much of a jerk when I should be kind. I can lose myself in a biased rabbit hole before I wake up to the bigger, more accurate picture of reality. I'm human and so are you. For better or for worse. And I do still believe we're in this whole beautiful mess together. I love you all.