Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games | Lex Fridman Podcast #475
发布时间 2025-07-23 18:39:15 来源
It's 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. Yes. Exactly. I mean, fluid dynamics and aviostokes equations, these are traditionally thought of very, very difficult, intractable problems to do on classical systems. They take enormous amounts of compute, you know, where the prediction systems, you know, these kind of things all involve fluid dynamics calculations.
我们人类很难对高度非线性动态系统做出准确的预测。不过，正如你所提到的，我们可能会惊讶于传统的学习系统在处理这些系统（例如流体）时的能力。是的，确实如此。流体动力学和纳维-斯托克斯方程通常被认为是非常复杂且难以在传统系统上解决的问题。它们需要巨大的计算量，而预测系统等也都涉及流体动力学计算。
But again, if you look at something like Vio, our video generation model, it can model liquids quite well, surprisingly well, and materials, specular lighting. I love the ones where, you know, there's people who generate videos where there's like clear liquids going through hydraulic presses and then 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.
再说一遍，如果你看看像Vio这样的模型——我们的一个视频生成模型，它在模拟液体方面表现得非常出色，令人惊讶地好，甚至还能处理材料和镜面光效。我特别喜欢那些视频，就是有人生成的视频中有清澈的液体经过液压机并被压出的场景。早年在游戏行业时，我曾经编写物理引擎和图形引擎，所以我知道创建能够实现这种效果的程序是多么困难。
And yet somehow these systems are, you know, 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, you know, maybe true of most of reality.
这些系统不知怎地通过观看 YouTube 视频来进行逆向工程。据推测，它们提取了这些材料行为的某种基本结构。因此，如果我们真正理解其内部运作，可能会发现某种低维流形是可以被学习的。这可能在很大程度上反映了现实世界的情况。
The following is a conversation with Demis Hussabas, his second time on the podcast. He is the leader of Google Deep Mind and is now a Nobel Prize winner. Demis is one of the most brilliant and fascinating minds in the world today working on understanding and building intelligence and exploring the big mysteries of our universe. This was truly an honor and a pleasure for me.
以下是与德米斯·哈萨比斯的对话，这是他第二次参加这个播客。他是谷歌DeepMind的负责人，目前已获得诺贝尔奖。德米斯是当今世界上最聪明、最令人着迷的头脑之一，他致力于理解和构建智能，并探索宇宙中的重大奥秘。对我来说，这次对话真的很荣幸和愉快。
This is the Lex Friedman podcast to support it. Please check out our sponsors in the description and consider subscribing to this channel. And now, dear friends, here's Demis Hussabas. 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?
这是Lex Friedman的播客来支持它。请查看描述中的赞助商，并考虑订阅这个频道。现在，亲爱的朋友们，这位是Demis Hussabas。在您的诺贝尔奖演讲中，您提出了一个我认为非常有趣的猜想，即“任何能在自然界中生成或发现的模式，都可以通过经典学习算法高效地被发现和建模。”这可能包含哪些系统的模式？生物学、化学、物理学、也许是宇宙学、神经科学？我们在讨论什么呢？
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 X projects, some thinking Alpha Go, of course Alpha Fold. What they really are is we're building models of very combinatorially high dimensional spaces that, you know, if you try to brute force a solution, find the best moving go or find the exact shape of a protein.
好的。我觉得在诺贝尔奖演讲中，稍微具有挑衅性是一种传统。我想遵循这个传统。我在讲的是，如果你退一步来看我们所做的所有工作，尤其是在 Alpha X 项目上，比如 Alpha Go 和 Alpha Fold。实际上，我们是在构建非常高维组合空间的模型。直接用蛮力去寻找解，比如在围棋中找到最佳走法或是找到蛋白质的确切形状，是非常困难的。
And if you numerated 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, you know, why should that be possible?
如果你列出所有的可能性，即便在宇宙的时间范围内也不够用。因此，你需要采取更聪明的方法。在这两种情况下，我们所做的是为那些环境建立模型。这指导了我们以聪明的方式进行探索，使问题变得可解。因此，如果你考虑蛋白质折叠这个自然系统，你就会明白，为什么这是可能的。
How does physics do that? You know, 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 shaped them. And if that's true, then you can maybe learn what that structure is. So this perspective, I think, is really interesting one.
物理是怎么做到的呢？你知道，在我们体内，蛋白质在毫秒间就能折叠完成。所以，物理学以某种方式解决了这个问题，而我们现在也通过计算解决了这个问题。我认为这之所以可能，是因为自然界中的自然系统有其结构，它们是经过进化过程塑造而成的。如果这是事实，那么我们或许可以学习到这种结构究竟是什么。我觉得这种观点非常有趣。
You've hinted at it, which is almost like crudely stated, anything that can be evolved can be officially modeled. I think there's some truth to that. Yeah, I sometimes call it survival of the stabilist or something like that because, you know, it's of course there's evolution for life, living things. But there's also, you know, if you think about geological time, so the shape of mountains, that's been shaped by weathering processes.
你已经暗示过了，这就像是粗略地说：任何可以进化的东西都可以被正式建模。我觉得这话有一定道理。是的，我有时把它称为“稳定者的生存”或类似的东西，因为，当然，生命和生物有进化。但如果你考虑地质时间的话，比如山的形状，也是由风化等过程形成的。
Right over thousands of years, but then you can even take it cosmological, the orbits of planets, the shapes of asteroids. These have all been survived kind of processes that have acted on them many, many times. So if that's true, then there should be some sort of pattern that you can kind of reverse learn and a kind of manifold really that helps you search to the right solution to the right shape.
在数千年的时间里，这些确实是正确的，但你甚至可以从宇宙学的角度来看待，比如行星的轨道、小行星的形状。这些都是在经历了许多次的作用后幸存下来的结果。所以，如果这是真的，那么就应该存在某种模式，你可以通过逆向学习来理解，从而帮助你寻找正确的解决方案，找到合适的形状。
And actually allow you to predict things about it in an efficient way because it's not a random pattern. Right, so it may not be possible for man-made things or abstract things like factorizing large numbers, because unless there's 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. There's no model to learn that will help you search, so you have to do brute force. So in that case, you know, you maybe need a quantum computer, something like this. But in most things in nature that we're interested in 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 your network. It's like nature is doing a search process, and it's so fascinating that it's in that search process, it's creating systems that could be efficiently modeled. Yes, right.
实际上，这可以让你以一种有效的方式预测事物，因为它不是一个随机的模式。对于一些人为的或抽象的事物，比如因式分解大数，这可能行不通。因为除非在数域中存在某种模式（也许有，但如果没有），如果它是均匀的，那就没有可以学习的模式。没有可以帮助你搜索的模型，你就必须用穷举法。在这种情况下，你可能需要一个量子计算机来处理。但在大多数我们感兴趣的自然现象中，它们并不是这样的。它们有一种结构，这种结构是为了某种原因而进化，并在时间中存活下来。如果真是这样，我认为这种结构是可以通过你的网络学习的。这就像大自然在进行一个搜索过程，并且令人着迷的是，在这个搜索过程中，它创造了可以高效建模的系统。没错，就是这样。
Yeah. So interesting. So they can be efficiently rediscovered or recovered because nature's not random, right? These are everything that we see around us, including like the elements that are more stable, all of those things. They're subject to some kind of selection process, pressure. 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. Yeah. This is a demo stop this 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，很有趣。因此，它们可以有效地被重新发现或恢复，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，都是经受某种选择过程和压力的结果。你作为理论计算机科学和复杂性方面的爱好者，你认为我们能否提出一种复杂性分类，比如类似复杂性"动物园"的分类，也许是可学习系统的集合，可学习的自然系统（LNS）的集合。这是一个新的系统类别的演示，它可以被经典系统这样地学习，是一种能够被有效建模的自然系统。
Yeah. I mean, I've always been fascinated by the peak or the peak question, and what is modulable by classical systems are non-quantum systems, you know, cheering machines in effect. And that's exactly what I'm working on actually in kind of my few moments of spare time with a few colleagues about it should there be, you know, maybe a new class of problem that is solvable by this type of neural network process. And kind of mapped on to these natural systems. So, you know, the things that exist in physics and have structure. So I think that could be a very interesting new way of thinking about it.
好的。我一直对“峰值”或“峰值问题”感兴趣，以及经典系统或非量子系统（比如图灵机）可以做到什么。这正是我在有限的业余时间里，与一些同事一起探索的课题。也许有一种全新的问题类型可以通过这种神经网络过程解决，并且可以映射到自然系统中——这些系统在物理学中存在并具有结构。我认为这可能是一个非常有趣的新思路。
And it sort of fits with the way I think about physics in general, which is that, you know, I think information is primary. Information is the most sort of fundamental unit of the universe, more fundamental than energy matter. I think they can all be converted into each other, but I think of the universe as a kind of informational system. So when you think of the universe as an informational system, then the P equals and P question is a is a physics question. That's right. And it's a question that can help us actually solve the entirety of this whole thing going on.
这与我对物理的总体看法有些契合，我认为信息是最根本的概念。信息是宇宙中最基本的单位，比能量和物质更为基础。我认为它们之间可以相互转化，但我把宇宙视为一种信息系统。因此，当你把宇宙看作一个信息系统时，那么“P等于NP”这个问题就变成了一个物理学问题。没错，这个问题或许能帮助我们解决所有正在发生的事。
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 it's going to be, you know, very enlightening. More specific to the P and P question. This again, some of the stuff we're saying is kind of crazy right now. Just like the Christian entrance and Nobel Prize speech controversial thing that he said sounded crazy. And then you went and got a Nobel Prize for this with John Dumper, solved the problem. So let me just stick to the P equals and P.
是的，我认为如果你把物理学视为信息学，那么这个问题实际上是最根本的问题之一。而对这个问题的答案，我觉得将会非常有启发性。更具体地说到 P 和 NP 问题。我们现在讨论的一些内容听起来有点疯狂。这就像当初克里斯蒂安在诺贝尔奖的演讲中说的一些有争议的话，听起来很疯狂。但后来他和约翰·邓普尔一起解决了问题，还因此获得了诺贝尔奖。所以，我就紧扣 P = NP 话题继续讨论。
Do you think there's something in this thing we're talking about that could be shown if you can do something like a 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. Yeah, I think that there are actually a huge cluster problems that could be couched in this way. The way we did AlphaGo and the way we did AlphaFold where you know, 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 in your network is it runs on normal computers, right classical computers, cheering machines in effect.
你认为我们正在讨论的这个东西中是否有某种可以展示的内容？如果你能够事先用多项式时间或常数时间进行计算并构建这个巨大的模型，那么你就可以以理论计算机科学的方式解决一些极其困难的问题。是的，我认为实际上有很多问题可以用这种方式来处理。就像我们做AlphaGo和AlphaFold的方式一样，你需要建模系统的动态特性以及你试图理解的环境的性质。这样一来，寻找解决方案或预测下一步就变得高效，基本上是多项式时间的，因此可以通过经典系统处理，也就是说，它可以在普通计算机上运行，实际上就是图灵机。
And I think it's one of the most interesting questions there is is how far can that paradigm go? You know, I think we've proven and the AI community in general that classical systems, cheering machines can go a lot further than we previously thought. You know, they can do things like model the structures of proteins and play go to better than world champion level. And you know, 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 to quantum systems to be able to do things like protein folding. And so I think we haven't really even sort of scratched the surface yet of what classical systems so called could do. And of course, AGI being built on a on a neural network system on top of a neural network system on top of a classical computer would be the ultimate expression of that.
我认为这是个非常有趣的问题：这种模式究竟能发展到什么程度？我们和整个人工智能界已经证明，经典系统（图灵机）能够走得比我们之前想象的更远。它们能做的事情包括模拟蛋白质结构以及下围棋，甚至超过了世界冠军水平。而很多人可能在10或20年前会认为这些事情还需要几十年的时间才能实现，或者需要某种量子计算机才能做到，比如蛋白质折叠之类的任务。因此，我认为我们对于经典系统的潜力仍然只触及了表面。当然，基于神经网络系统，再建立在经典计算机上的通用人工智能（AGI），将是这种潜力的终极体现。
And I think the limit that you know, the what what the bounds of that kind of system, what it can do. It's a very interesting question and and directly speaks to the P equals MP question. What do you think again hypothetical might be outside of this maybe emergent phenomena? Like if you look at cellular automata, some of that you have 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 classical.
我认为，了解这种系统的界限、即它能做到什么的极限，是一个非常有趣的问题，并且直接关系到P=NP问题。你认为在这个假设的情况下，可能会出现什么超出这个范围的现象呢？比如当你观察细胞自动机时，一些非常简单的系统会产生复杂的现象。是的，也许那会在这个范围之外，或者你会猜测即使那样的现象也可以通过经典方法进行有效建模吗？
Yeah, I think those systems would be right on the boundary, right? So I think most emergent systems, cellular automata, things like that could be modellable 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, you know, uncorrelated end state. Now those could be difficult to model. So I think these are kind of the open questions.
是的，我认为那些系统正处于边界上，对吧？所以我认为大多数突现系统，比如元胞自动机之类的，用经典系统模型化是可行的。你只需对其进行前向模拟，这可能就足够高效。当然，对于某些系统，比如混沌系统，初始条件非常重要。这样你可能会得到一些无关联的最终状态。这些系统可能会难以建模。所以我认为这些都是目前尚未解决的问题。
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 VO3 on like video generation, sort of rendering, physics and lighting and things like that. You know, really in 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.
我觉得，当你退一步来看我们在这些系统上所做的工作以及我们解决的问题时，再看看像VO3这样的技术在视频生成、渲染、物理和光照等方面的应用，你会发现我们在物理学核心基础方面所取得的成就真的很有趣。我认为，这从某种程度上揭示了宇宙结构的一些根本性特点。
So, you know, in a way, that's what I want to build AGI4 is to help us as scientists answer these questions like P-Calls MP. Yeah, I think we might be continuously surprised about what is modellable by classical computers. I mean, Alpha4 3 on the interaction side is surprising that you can make any kind of progress on that direction. Alpha G-Nome 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 that and then here you go. You can find the kernel that is efficiently modeled.
所以，你知道，从某种意义上说，我想要构建AGI4，是为了帮助我们这些科学家解答一些问题，比如说P与NP的问题。我觉得我们可能会不断对经典计算机能够建模的东西感到惊讶。比如，在交互方面，Alpha4 3的进展令人惊讶，它在这个方向上取得了任何进展。还有Alpha G-Nome，它能将遗传密码映射到功能上，这也是令人惊讶的。这有点像是对新兴现象的一种探索。你认为有那么多的组合选项，而你却能够找到一个能够高效建模的核心。
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 you can follow and of course what neural networks are very good at is following gradients. And so if there's one to follow and object and you can specify the objective function correctly, you know, 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's many, many problems like that and then you think, well, it's like 10 to the 300 possible protein structures, 10 to the 170 possible go positions.
是的，因为在能量景观或其他类似的景观中，存在一定的结构，有某种梯度是可以跟随的。当然，神经网络特别擅长的就是跟随梯度。所以，如果有可以跟随的梯度，并且你能正确地指定目标函数，那么就不用应对所有的复杂性。这可能就是我们过去几十年来可能有些天真地看待这些问题的方式。如果你仅仅枚举所有的可能性，就会显得完全无解。而且有很多类似的问题，比如可能有10的300次方种可能的蛋白质结构，10的170次方种可能的围棋局面。
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? But it turns out that it is possible and of course reality nature does do it, right? Protein to do fold. So that gives you confidence that there must be if we understood how physics was doing that in a sense. Then and we could mimic that process, a model that process, it should be possible on our classical systems is basically what the conjecture is about. And of course there's nonlinear dynamical systems, highly nonlinear dynamical systems. Everything involving fluid. Yes, right. You know, that recent conversation with Terence Thal, 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.
所有这些远远超过宇宙中的原子数量。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？然而，事实证明这是可能的，现实中的自然确实能够做到，不是吗？就像蛋白质如何折叠一样。这让我们相信，如果我们理解了物理学在这个过程中的运作方式，我们就可以模拟这个过程、建模这个过程，那么在我们的经典系统中实现这一点就应该是可能的，这基本上就是这个猜想的内容。当然，还有非线性动力系统，高度非线性的动力系统。一切涉及流体的事物，是的。你知道，最近与数学家Terence Thal的谈话，他在数学上处理具有一些奇异性的系统的非常困难的方面，这些奇异性使得数学失效。对于我们人类来说，关于高度非线性动力系统做出任何清晰的预测都很困难。
But again, to your point, we may be very surprised what classical learning systems might be able to do about even fluid. Yes, exactly. I mean, fluid dynamics, Navier Stokes equations, these are traditionally thought of very, very difficult, intractable kind of problems to do on classical systems. They take enormous amounts of compute, you know, where the prediction systems, you know, these kind of things all involve fluid dynamics calculations. And but again, if you look at something like Vio, our video generation model, it can model liquids quite well, surprisingly well. And materials, specular lighting, I love the ones where, you know, there's people who generate videos where there's like clear liquids going through hydraulic presses and then being squeezed out.
但再次回应你的观点，我们可能会对传统学习系统在处理流体问题上的能力感到非常惊讶。是的，没错。我的意思是，流体力学、Navier-Stokes方程这些通常被认为是在传统系统上非常困难、难以处理的问题。它们需要大量的计算能力，比如预测系统，涉及流体力学计算的所有这些事。但是，再看看像Vio这样的，我们的视频生成模型，它对液体的模拟效果相当不错，令人惊讶。还有材料、镜面光照，我特别喜欢那些生成的视频，其中有透明液体经过液压机被挤压的场景。
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, you know, 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, you know, maybe true of most of reality.
我早年从事游戏工作时曾经编写过物理引擎和图形引擎，我深知构建能够实现这些功能的程序有多么艰难。然而，不知怎么的，这些系统现在仅凭观看YouTube视频就能进行逆向工程。大概这些系统是在提取关于这些材料如何表现的某种底层结构。所以，或许如果我们完全理解背后的运作机制，就能学习到某种低维的流形结构。这或许也适用于大多数现实现象。
Yeah, I've been continuously precisely by this aspect of Vio3. I think a lot of people highlight different aspects, including the comedic and the meat and all that kind of stuff. And then the ultra realistic ability to capture humans in a really nice way that's compelling and it feels close to reality and then combine that with native audio. All of those are marvelous things about Vio3, but the exactly the thing you're mentioning, which is the physics. Yeah, 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?
是的，我一直对Vio3的这个方面感到惊讶。我想很多人会强调它不同的特点，包括它的喜剧元素、内容丰富的部分等等。还有，它能够超真实地呈现出人类，这种方式非常吸引人，让人感觉很接近现实，再加上本地音频的结合。这些都是Vio3了不起的地方。但正如你提到的，物理方面尤为显著。虽然不完美，但已经相当出色。真正有趣的科学问题是，它到底理解了我们世界的哪些东西，才能做到这样的表现呢？
Because the cynical take with the fusion of there's no way to understand 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 when it means to understand, then is like brought to the surface. To what degree do you think Vio3 understands our world? I think to the extent that it can predict the next frames, you know, in a coherent way, that some of that is a form, you know, of understanding, right? Not in the anthropomorphic version of, you know, it's not some kind of deep philosophical understanding of what's going on.
由于在融合过程中，持愤世嫉俗态度的人认为无法理解任何事情。但我觉得，生成那种视频是需要一定程度的理解的。然后我们自身对于"理解"的哲学概念就出现了。你认为Vio3在多大程度上理解我们的世界？我认为，只要它能够以连贯的方式预测下一帧画面，这在某种程度上就是一种理解，对吧？当然这不是人类那种深刻的哲学式的理解。
I don't think these systems have that, but they certainly have modeled enough of the dynamics, you know, put it that way that they can pretty accurately generate whatever it is. Eight seconds of consistent video that by I, at least, you know, 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 and how incredible that will, they will look given where we've come from, you know, the early versions of that one or two years ago. And so the rate of progress is incredible.
我觉得这些系统目前还没有具备那种能力，但它们确实已经对动态进行了足够的建模，可以这么说它们能够相当准确地生成大约八秒的连贯视频。至少在我看来，一眼看去很难辨别出问题所在。想象一下再过两三年后这种技术的发展。我正在考虑的就是这一点，以及它们到时候会多么不可思议，考虑到我们从一两年前的早期版本发展到现在的进步速度实在是惊人。
And I think I'm like you is like a lot of people love all of the, the, the, the standup comedians and the, the actually captures a lot of human dynamics very well and, and 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 it 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, right? As opposed to a, you know, a PhD student really being able to unpack all the equations is more of an intuitive physics understanding.
我觉得我像你，也像很多人一样，喜欢所有的单口喜剧演员，因为他们非常好地捕捉到了人类行为的动态和肢体语言。但实际上，我最为印象深刻和着迷的是物理行为、光影、材料和液体的表现。它能做到这些，真的让人惊讶。我认为这表明它至少有一种直觉上的物理理解，对吧？就像人类小孩能直观地理解物理，而不是像博士生那样去详细解析所有的方程式，这种理解更偏向于直观的物理感知。
Well, that intuitive physics understanding, that's the base layer. That's the thing people sometimes call a common sense. Again, 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. 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 V03 is directly challenging that. Right. It feels like. Yes. And it's very interesting, you know, even if we, if you were to ask me five, ten years ago, I would have said, even though I was a must in all of this, I would have said, well, yeah, you probably need to understand intuitive physics. Like if I push this off the table, this glass, it will maybe shatter, and the liquid will spill out.
好吧，那种直观的物理理解就是基础层。这就是人们有时称之为常识的东西。再次强调，它确实是在理解某些东西。我认为，这真的让很多人大吃一惊。让我震惊的是，我本来认为不可能在没有理解的情况下产生那种级别的逼真感。有一种观念认为，你只能通过拥有一个与世界互动的人工智能系统，比如机器人，来理解物理世界。这被认为是构建对世界理解的唯一途径。但V03正直接挑战这一点。对。是的，这感觉是这样。非常有趣的是，你知道，即使在五到十年前如果你问我，我可能也会说，即使我身处这一领域，我可能也会说，是的，你可能需要理解直观物理。就像如果我把这个玻璃杯从桌子上推下来，它可能会碎掉，液体会洒出来。
Right. So we know all of these things. But I thought that there's a lot of theories in neuroscience. It's called action in perception where you need to act in the world to really, truly perceive it in a deep way. And there was a lot of theories about you need embodied intelligence or robotics or something, or maybe at least simulated action so that you would understand things like intuitive physics. But it seems like you can understand it through passive observation, which is pretty surprising to me. And again, I think hints at something underlying about the nature of reality in my opinion, beyond just the, you know, the cool videos that it generates. And of course, there's next stages is maybe even making those videos interactive.
好的，所以我们知道这一切。不过，我认为神经科学中有很多理论，认为所谓的“行动中的知觉”是指你需要在现实世界中采取行动，才能深入地理解和感知它。有很多理论认为你需要具备体现化的智能、机器人技术，或者至少是模拟的行动，这样才能理解像直觉物理学这样的东西。但令人惊讶的是，似乎通过被动观察也可以理解这些。我认为这提示了某些关于现实本质的深层含义，而不仅仅是生成酷炫视频的能力。当然，下一步可能是让这些视频变得可以互动。
So one can actually step into them and move around them, which will be really mind-blowing, especially given my games background. So you can imagine. And then I think you know, 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. I have to talk to you about video games. So you're being a bit trolley. I think you're having more and more fun on Twitter on X, which is great to see. So guy named Jimmy Apple's tweeted, let me play a video game of my VO3 videos already. Google cooked so good playable world models when spelled W-E-N question mark. And then you quoted that with, not wouldn't that be something.
所以，人们实际上可以进入其中并在其中四处走动，这将是非常令人震惊的，尤其是考虑到我在游戏方面的背景。你可以想象一下。我想，你也知道，我们正在开始接近我所称的“世界模型”，一个关于世界如何运作的模型，包括世界的机制、物理定律和这个世界中的事物。当然，这正是一个真正的人工通用智能（AGI）系统所需要的。我必须跟你聊聊电子游戏。你有点像个“小捣蛋鬼”，我觉得你在 Twitter 上（现在叫 X）越来越开心，真是让人欣慰。有个人叫 Jimmy Apple 发推说，让我玩我 VO3 视频的电子游戏，谷歌已经做得很好了，可玩世界模型什么时候实现呢？然后你引用了那句话，说“那岂不是太棒了”。
So how hard is it to build game worlds with AI? Maybe can you look out into the future of video games five, 10 years out? What do you think that looks like? 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 have, I want to scratch that each one day and come back to that. So, you know, and I will do I think. And I think I'd sort of dream about, you know, what would I have done back in the 90s if I'd had access to the kind of AI systems we have today? And I think you could build absolutely mind-blowing games.
那么，用人工智能来构建游戏世界到底有多难呢？也许你可以展望一下未来五到十年的电子游戏会是什么样子？其实，游戏一直是我的初恋。而在我十几岁的时候，做游戏的人工智能是我第一份专业的工作，也是我开发的第一个大型人工智能系统。我总想有一天能重新回到这一领域，并将这个愿望实现。我常常幻想，如果在90年代我就能使用今天这样的人工智能系统，我会做出怎样的游戏。我想那将是绝对令人惊叹的。
And I think the next stage is I always used to love making all the games I've made are open world games. So there are games where there's a simulation and then there's AI characters. And then the player interacts with that simulation and the simulation adapts to the way the player plays. And I always thought they were the coolest games because, so games like Theme Park that I worked on, where everybody's game experience would be unique to them. Right, because you're kind of co-creating the game. Right, we set up the parameters, we set up initial conditions and then use the player immersed in it and then you are co-creating it with the simulation.
我认为下一阶段是，我一直喜欢制作开放世界游戏。我制作的所有游戏都是开放世界的。在这些游戏中，有一个模拟系统和人工智能角色，玩家可以与这个模拟系统互动，而模拟系统会根据玩家的玩法进行调整。我一直觉得这类游戏非常酷，比如我参与制作的《主题公园》（Theme Park），每个人的游戏体验都是独特的。因为你是在与游戏共同创作：我们设定参数和初始条件，然后玩家沉浸其中，与模拟系统一起共同创造游戏体验。
But of course, it's very hard to program open world games. You know, you've got to be able to create a content which every direction the player goes in and you want it to be compelling, no matter what the player chooses. And so it was always quite difficult to build things like cellular automata, actually, type of those kind of classical systems which created some emergent behavior. But they're always a little bit fragile, a little bit limited. Now we're maybe on the cusp in the next few years, five, ten years of having AI systems that can truly create around your imagination, can dynamically change the story and story tell the narrative around and make it dramatic, no matter what you end up choosing.
当然，制作开放世界游戏是非常困难的。您需要能够在玩家前往的每个方向上创建内容，并且无论玩家选择什么，都希望这些内容有吸引力。因此，构建一些像元胞自动机这样的经典系统总是相当困难的，因为它们可以产生一些意外的行为，但总是有点脆弱，受限。然而，也许在未来的五到十年内，我们正处于一个转折点，那时的AI系统可以真正围绕着你的想象进行创作，能够动态地改变故事情节和叙述，不管你选择了什么，都可以让它变得引人入胜。
So it's like the ultimate choose your own adventure sort of game. And I think maybe we're within reach if you think of a kind of interactive version of VO and then wind that forward five to ten years and imagine how good it's going to be. Yeah, so you said a lot of super interesting stuff there. So 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, but you can open any door and there'll be something there. It's that the choice of which door you're open in an unconstrained way defines the world you see.
这就像一个终极版的“自己选择路线”冒险游戏。我想也许我们已经接近实现这一点了，如果你把它想象成一个互动版的虚拟现实（VR），再向前发展五到十年，想象一下它会多么出色。嗯，你刚才说了很多非常有趣的东西。其中一个是，你所描述的那种开放世界中包含了深度个性化。所以这不仅仅是一个开放世界，而是你可以打开任何一扇门，都会有东西等待着你。而你选择打开哪扇门的方式，是完全不受限制的，而这正是定义了你所看到的世界。
So some games try to do that to give you choice. Yes, but it's really just an illusion of choice because the only like Stanley Parable. Yeah, it's really there's a couple of doors and it really just takes it down to narrative. Stanley Parable is a great video game. I recommend you play that kind of in a matter of way mocks the illusion of choice and there's philosophical notions of free will and so on. But I do like one of my favorite games, Felder Scrolls, is a dagger fall, I believe, that they really played with a random generation of the dungeons.
有些游戏试图通过给予玩家选择权来提升体验。是的，但实际上这只是选择的幻觉，就像《史丹利的寓言》一样。实际上，它只给你几个门，并引导你进入叙述。《史丹利的寓言》是一款非常棒的电子游戏，我推荐你去玩，它用一种调侃的方式揭示了选择的幻觉，并探讨了自由意志等哲学概念。不过，我确实喜欢《上古卷轴》系列中的一款游戏，我认为是《匕首雨》，它在地下城的随机生成方面做得很出色。
Yeah. Of if you can step in and it gives you this feeling of an open world and there you're much in interactivity. You don't need to interact that's a first step because you don't need to interact that much. You just when you open the door, whatever you see is randomly generated for you. And that's already an incredible experience because you might be the only person to ever see that.
好的。这种感觉就像踏入一个开放的世界，那里有很多互动性，但你并不需要进行太多互动。最初，你只需要开门，然后眼前看到的一切都是为你随机生成的。这已经是一次很棒的体验，因为你可能是唯一能见到这些的人的。
Yeah, exactly. But what you'd like is a little bit better than sort of a random generation. And also better than a simple A, B hard code of choice. That's not really open world. As you say, it's just giving you the illusion of choice. What you want to better do is potentially anything in that game environment. And I think the only way you can do that is to have a generated system systems that will generate that on the fly. Of course, you can't create infinite amounts of game assets.
是的，没错。你想要的东西应该比随机生成要好一些，也比简单的A、B硬编码选择更好。那种方式并不是开放世界，它只是给了你一种选择的假象。你真正想要的是能够在游戏环境中实现各种可能性的能力。我认为，要做到这一点，唯一的办法就是拥有一个能够即时生成内容的系统。当然，你不可能创造出无限量的游戏资产。
Right? It's expensive enough already how AAA games are made today. And that was obvious to us back in the 90s when I was working on all these games. I think maybe black and white was the game that I worked on early stages of that that had still probably the best AI, learning AI in it. It was an early reinforcement learning system that you were looking after this mythical creature and growing it and nurturing it.
对吧？如今的3A游戏制作成本已经够高了。早在90年代，我在从事这些游戏的开发时就很清楚这一点。我觉得可能《黑与白》是我在那些早期阶段参与的一款游戏，它的人工智能可能是最好的一个。它是一个早期的强化学习系统，你要照顾一个神话中的生物，培养和呵护它。
And depending how you treated it, it would treat the villagers in that world in the same way. So if you're mean to it, it would be mean. If you're good, it would be protective. And so it was really a reflection of the way you played it. So actually, all of the... I've been working on simulations in AI through the medium of 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 hard coded ways of doing the AI to now fully general learning systems that are trying to achieve the same thing.
根据你对待它的方式，它也会以同样的方式对待那个世界里的村民。所以，如果你对它不好，它也会对你不友好；如果你对它好，它就会保护你。这实际上反映了你是如何进行游戏的。实际上，我在职业生涯的初期就通过游戏来研究人工智能的模拟。而且我今天所做的事情，实际上是那些早期较为硬编码的人工智能方法的延续，发展到现在的全面通用学习系统，依然在尝试实现相同的目标。
Yeah, it's been interesting, hilarious and fun to watch you and Elon obviously itching to create games because you're both gamers. And one of the sad aspects of your incredible success and so many domains of science, like serious adult stuff that you might not have time to really create a game. You might end up creating the tooling that others will create the game. You have to watch. Others create the thing you've always dreamed of.
是的，看你和埃隆明显渴望创造游戏真是既有趣又搞笑，因为你们都是游戏爱好者。而你们在科学各个领域取得了巨大的成功，这反而让人觉得遗憾，因为这些严肃的工作可能让你们没有时间真正去开发一款游戏。最终你们可能只是创造了让其他人用来开发游戏的工具，而你们只能看着别人实现你们一直以来的梦想。
Do you think it's possible you can somehow, in your extremely busy schedule, actually find time to create something like Black and White? Some actual video game where you could make the childhood dream come big reality. Well, there's two things where you think about that is maybe with vibe coding as it gets better and there's possibility that one could do that actually in your spare time.
你认为在极其繁忙的日程中，你是否可能腾出时间来创造出类似《黑与白》的东西？一种能让童年梦想成为现实的视频游戏。关于这点，有两个方面可以考虑：一是当编码技术不断进步时，这种可能性会增加；二是或许你其实可以在空闲时间里实现这一目标。
So I'm quite excited about that as that would be my project if I got the time to do some vibe coding. I'm actually itching to do that. And then the other thing is maybe it's a sabbatical after AGI is being safely stewarded into the world and delivered into the world. You know that and then working on my physics theory as we talked about at the beginning, those would be the two my two post AGI projects.
因此，我对此感到非常兴奋，因为如果我有时间进行一些有趣的编程，那将是我的项目。我实际上迫不及待想要开始。而另外一件事可能是在人工智能安全地被引入和应用到世界后，可以休一个长假。你知道的，然后就是继续研究我之前提到的物理理论，这将是我在人工智能之后的两个项目。
Let's call it that way. I would love to see what the ultimate game post AGI would you choose? Solving the problem that some of the smartest people in human history can tend to with is a P and E equals MP or creating a cool video. Yeah, well, but in my world they'd be related because it would be an open world simulated game as realistic as possible.
我们就这样称呼它吧。我很想知道你在后AGI时代会选择什么样的终极游戏？是解决一些人类历史上最聪明的人们曾处理过的问题，比如P和E等于MP，还是制作一个很酷的视频？嗯，在我看来，这两者是相关的，因为那将是一个尽可能真实的开放世界模拟游戏。
So you know, what is the universe? That's speaking to the same question, right? And P equals MP. I think all these things are related at least in my mind. I mean, in a really serious way, like video games sometimes are a little down upon. That's just this fun side activity. But especially as AGI does more and more of the difficult boring tasks, something we in a modern world called work, you know, video games is the thing. in which we may find meaning in which we may find like 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.
你知道，宇宙是什么？这其实是同一个问题，对吧？还有P等于NP。我觉得这些事情在我脑海中至少是相关的。我的意思是，真的很严肃，就像电子游戏有时被认为是无关紧要的，只是个有趣的休闲活动。然而，特别是随着人工智能在现代社会中承担越来越多繁琐枯燥的工作，这些被我们称为“工作”的任务，电子游戏可能会成为我们寻找意义和安排时间的方式。你可以创造出极其丰富而有意义的体验，这就是人类生活的真谛。而在电子游戏中，你可以设计出更复杂、更丰富多样的生活方式。
Yeah, I think so. I mean, those of us who love games and I still do is is, you know, 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 gold and era, maybe the 80s of the game's 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 were co-create, 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.
是啊，我也是这么想的。我是说，那些热爱游戏的人，包括我自己，都觉得玩游戏就像让你的想象力自由驰骋，对吧？我过去特别喜欢游戏，特别是制作游戏，因为这是融合，尤其是在90年代和21世纪初，也许还有80年代，那是游戏产业的黄金时代，一切都在被探索。新的游戏类型不断涌现。我们不仅仅是在制作游戏，我们觉得自己在创造一种前所未有的娱乐方式，特别是在这些开放世界游戏和模拟游戏中，玩家能够共同创造故事。在其他娱乐媒体中，没有哪一种能让你作为观众共同创造故事。
And of course, now with multiplayer games as well, it can be a very social activity and can explore all kinds of interesting worlds and that. But on the other hand, you know, it's very important to also enjoy and experience the physical world. But the question is then, you know, I think we're going to have to co-create or confront the question again of what is the fundamental nature of reality? What is the going to be the difference between these increasingly realistic simulations and multiplayer ones and emergent and what we do in the real world?
当然，现在有了多人游戏，它们可以成为一种非常社交的活动，让人们探索各种有趣的世界。但是另一方面，我们也必须重视体验和享受物理世界。问题是，我认为我们需要再次共同探讨或面对一个问题：现实的本质究竟是什么？这些越来越逼真的模拟游戏和多人游戏，以及我们在现实世界中的活动之间，会有什么区别呢？
Yeah, there's clearly a huge amount of value to experiencing the real world nature. There's also huge amount of value in experiencing other humans directly in person, the way we're sitting here today. But we need to really scientifically rigorously answer the question, why? Yeah. Exactly. Which aspect of that can be mapped into the virtual world? Exactly. And it's not enough to say, yeah, you should go touch grass and hang out in nature. It's like, why exactly? It's that valuable. Yes. And I guess that's maybe the thing that's been haunting me, obsessing me from the beginning of my career. You can think about all the different things I've done.
是的，亲身体验真实的自然世界有着显而易见的巨大价值。直接与他人面对面交流也有着巨大的价值，就像我们今天这样坐在一起。但是我们需要科学而严谨地回答这个问题，为什么会有这样的价值？究竟哪些方面可以被转化到虚拟世界中？这可不是简单一句“你应该去接触大自然、享受自然”的建议能够解答的。为什么这些体验这么有价值？这可能就是从我职业生涯开始就一直萦绕并让我着迷的问题之一。你可以回想一下我所做过的各种事情。
That's they're all related in that way. This simulation, nature of reality, and what is the bounds of what can be modeled? Sorry for the ridiculous question, but so far, what is the greatest video game of all time? What's up there? My favorite one of all time is civilization. I have to say. That was the civilization one and civilization two, my favorite games of all time. I can only assume you've avoided the most recent one because it would probably... That would be your sabbatical. That would disappear. Yes. Exactly. They take a lot of time to civilization games. So I've got to be careful with them.
它们在某种程度上都是相关的。这个模拟、现实的本质，以及可以被建模的界限是什么？很抱歉这个问题有点奇怪，但到目前为止，有史以来最伟大的电子游戏是哪一个？有哪些可以算得上呢？我最喜欢的游戏是《文明》。我得说，文明一和文明二是我有史以来最喜欢的游戏。我可以想象你可能避开了最近的一作，因为那可能会... 那可能会占用你的全部休假时间，完全沉迷其中。是的，完全正确。《文明》系列游戏真的很花时间，所以我不得不小心对待它们。
Fun question. You and Elon seem to be somehow solid gamers. Is there a connection between being great gaming and being great leaders of AI companies? I don't know. It's 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 were just became a thing in the late 80s and 90s, especially in the UK. I had a spectrum and then a Commodore Miga 500, which is my favorite computer ever. That's why I learned all my programming.
有趣的问题。你和Elon似乎都是出色的游戏玩家。成为优秀的游戏玩家和成为优秀的AI公司领导者之间有联系吗？我不知道，但这很值得思考。我想说我们都热爱游戏，而且有趣的是，Elon一开始也是通过编写游戏入门的。在我成长的时代，家庭电脑刚刚在80年代末到90年代之间变得流行，尤其是在英国。我先用过Spectrum，然后是Commodore Amiga 500，这是我最喜欢的电脑。那时候我就是在这上面学会了编程。
Of course, it's a very fun thing to program is to program games. I think it's a great way to learn programming, probably still is. Then of course, I immediately took it in directions of AI and simulations. I was able to express my interest in games and my wider scientific interests all together. Then the final thing that's great about games is it fuses artistic design, art, with the most cutting edge programming. 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.
当然，编程游戏是一件非常有趣的事情。我认为这是学习编程的一个好方法，可能到现在仍然如此。然后，我很快就把它应用到人工智能和模拟方向。我能够同时表达我对游戏的兴趣和更广泛的科学兴趣。游戏的另一个优点是，它将艺术设计与最前沿的编程技术结合在一起。同样在90年代，所有最有趣的技术进步都发生在游戏领域，无论是人工智能、图形、物理引擎，还是硬件，甚至GPU，最初都是为游戏设计的。
Everything that was pushing computing forward in the 90s was due to gaming. Interestingly, that was where the forefront of research was going on. It was this incredible fusion with art, graphics, but also music, and just the whole new media of storytelling. I love that. For me, it's this multi-disciplinary kind of effort is again something I've enjoyed my whole life.
在90年代，推动计算机技术进步的所有因素都源于游戏。 有趣的是，那时的前沿研究主要集中在这一领域。这是艺术、图形以及音乐等多种元素的惊人融合，也是一种全新的叙事媒体。我非常喜欢这一点。对我来说，这种多学科的合作是我一生中特别欣赏的东西。
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. As Alpha evolved, we talked about evolution a little bit, but it's the Google DeepMind system that evolves algorithms. Are these kinds of evolution techniques promising as a component of future superintelligent systems? So people don't know. It's kind of, I don't know if it's fair to say, LLM guided evolution search. Evolution algorithms are doing the search, and LLM's are telling you where.
我必须问你。我差点忘记了最近发生的一件事情，这件事情可以说是其中最让人惊奇的之一，但却没有得到足够的关注。随着Alpha的进化，我们曾稍微谈过进化，而这其中包括Google DeepMind系统，它负责演化算法。这样的进化技术在未来的超级智能系统中是否有前途？人们对此还不太清楚。我不知道这样说是否准确，但可以说是“大型语言模型（LLM）指导的进化搜索”。进化算法在进行搜索，而LLM告诉你搜索的方向。
Yes, exactly. LLMs are proposing some possible solutions, and then you do, you use an evolutionary computing on top to find some novel part of the search base. 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 research. Basically, many types of search algorithms or reasoning algorithms sort of 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 hybrid systems. Let's call them.
是的，完全正确。大型语言模型（LLM）可以提出一些可能的解决方案，然后你可以在其基础上应用进化计算来探索搜索空间中的新部分。实际上，我认为这是一个非常有前途的方向的例子，就是将LLMs或基础模型与其他计算技术结合起来。进化方法是一种可能，但你也可以想象使用蒙特卡罗研究。基本上，有很多种搜索算法或推理算法可以建立在基础模型之上或以其为基础。因此，我认为在这些混合系统中可能会发现很多有趣的东西。让我们称之为混合系统。
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 where there's a lot of low hanging fruit of us understanding being able to model, being able to simulate evolution, and using that whatever we understand about that nature is by mechanism to then do search better and better and better? Yes. So if you think about, again, breaking down the solar systems we've built to their really fundamental core, you've got like the model of the underlying dynamics of the system.
但也不应该过于浪漫化描绘进化。是啊，我只是个人类，不过你觉得无论那机制是什么都有其价值，因为我们已经谈论了自然系统。你认为我们在理解、能够模拟进化以及利用对自然机制的理解来进行更好、更优的探索方面，有很多浅显易懂的机会吗？是的。所以如果你再想一下，将我们建造的太阳能系统分解到其真正的核心本质，你就会得到这个系统基本动力学的模型。
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. And you can do that in a number of ways. Evolutionary computing is one. With AlphaGo, we just use Monte Carlo research. And that's what found Move37, then you kind of never seen before strategy in Go. And so that's how you can go beyond potentially what is already known.
如果你想要探索一些新的、之前未见过的事物，那么你需要一个搜索过程来引导你进入搜索空间中的新区域。你可以通过多种方式来实现这一点。进化计算是一种方法。在AlphaGo中，我们使用了蒙特卡罗搜索，这就是发现围棋中前所未见的“第37步”的方法。因此，这就是如何超越现有已知事物的途径。
So the model can model everything that you currently know about, all the data that you currently have. But then how do you go beyond that? So that starts to speak about the ideas of creativity. How can these systems create something new, fight, discover something new? Obviously this is super relevant for scientific discovery or pushing metascience and medicine forward, which we want to do with these systems. And you can actually bolt on some fairly simple search systems on top of these models and get you into a new region of space.
因此，这个模型能够对你目前所知道的一切、以及你目前拥有的所有数据进行建模。但是，你该如何超越这些已知的信息呢？这就涉及到创造力的概念。如何让这些系统创造一些新的东西、或者发现一些新的事物呢？显然，这对于科学发现或促进元科学和医学的发展非常重要，因为我们希望利用这些系统来实现这些目标。实际上，你可以在这些模型上加一些相对简单的搜索系统，从而进入一个新的领域。
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 heal climb towards and that guides that search. But there's some mechanism of evolution that are interesting. Maybe in the space of programs, but then the space of programs that is an extremely important space because you can probably generalize to everything.
当然，你也必须确保你不是完全随机地搜索这个空间。这样的话空间会太大。所以你需要有一个目标函数来优化，并朝着这个目标努力，这样才能指导你的搜索。但是，进化机制中有些是非常有趣的。也许在程序的空间中就是如此，而程序的空间是一个非常重要的空间，因为你可能可以从中推广到所有事物。
But you know, for example, mutation. It's not just Monte Carlo tree search where it's like a search. You get every once in a while combined things. Yeah. Combined things out there like sub components of a thing. So then, you know, what evolution is really good at is not just the natural selection. It's combining things and building increasingly complex hierarchical systems. So that component is super interesting, especially like with alpha, evolving the space of programs.
你知道，比如说突变。这不仅仅是像蒙特卡洛树搜索那样的搜索。有时候，你可以把事情组合在一起。是的，把它们的子组件组合在一起。因此，进化真正擅长的不仅仅是自然选择。它还擅长于组合事物，构建越来越复杂的层级系统。这个方面非常有趣，特别是在开发“Alpha”程序空间时。
Yeah. Exactly. So there's a you can get a bit of an extra property out of evolutionary systems, which is some new emergent capability may come about. Yes. But of course, like happen with life. Interestingly, with naive sort of traditional evolution computing methods without LLMs and the modern AI, the problem with them, they were very well studied in the 90s and early 2000s and some promising results.
是的，没错。在进化系统中，你可以获得一些额外特性，也就是说可能会出现某种新的涌现能力。是的。但当然，就像生命中发生的那样。有趣的是，传统的进化计算方法没有使用大型语言模型（LLM）和现代人工智能，它们的问题在于它们在90年代和21世纪初得到了非常深入的研究，并取得了一些有希望的成果。
But the problem was 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. So bacteria to where we are now. So clearly that 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 cost of discovering how to do that.
问题在于，他们一直无法弄清楚如何发展新的特性，即新的涌现特性。你总是只能获得系统中已有特性的一部分。然而，也许如果我们将这些与基础模型结合起来，我们可能可以克服这一限制。显然，自然进化是能做到的，因为它确实发展出了新的能力。例如，从细菌进化到我们现在的样子。因此，显然进化系统能产生新的模式、能力和涌现特性。也许我们正处于发现如何做到这一点的关键时刻。
Yeah. Listen, F.O.Va was one of the coolest things I've ever seen. I've ever on my desk at home, you know, most of my time spent on that computer is just programming. And next to the three screens is a scolva tiktolic, 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 competition mechanism of evolution is, it's quite incredible. Yes, truly, truly incredible. Now, whether that's exactly the thing we need to do to do our search, but never, never dismiss the power of nature. What, what it did here.
好的，听着，F.O.Va是我见过的最酷的东西之一。我在家里的桌子上有它，你知道，我大部分时间都是用那台电脑编程。在三个屏幕旁边有一个叫做scolva tiktolic的小模型，它是最早从水中爬到陆地上的生物之一。我就那样看着那个小家伙，觉得无论进化的竞争机制是什么，都是相当惊人的。是，非常非常惊人。现在，我们是否应该用它来进行我们的研究还不确定，但绝不能小看大自然的力量，它的造化真是不可思议。
Yeah. And it's amazing. Which is a relatively simple algorithm, right? Effectively. And it can generate all of this immense complexity emerges. Obviously running over, you know, four billion years of time, but it's, it's, you know, you can think about that as again, a process, a search process that round over the physics substrate of the universe for a long amount of computational time. But then it generated all this incredible rich diversity.
是的，这非常神奇。这其实是一个相对简单的算法，对吧？说起来简单，但它能够产生如此巨大的复杂性。显然，这个过程是在大约四十亿年的时间里进行的。你可以将其视作一个在宇宙的物理基础上进行的长期计算搜索过程。结果是，它创造了所有这些令人难以置信的丰富多样性。
So, so many questions I want to ask you. So one, you do have a dream. One of the natural systems you want to try to model is a, 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, just broadly. So there's a essay from Daniel Cucutayo, Scott Alexander and others that outline steps along the way to get to ASI. And as a lot of interesting ideas in it, one of which is including a superhuman coder and a superhuman AI researcher.
有太多问题我想问你。首先，你确实有一个梦想。你想尝试模拟的自然系统之一是细胞。这是一个美丽的梦想，我可以就此向你提问。我也想广泛地了解一下在人工智能科学家方面的情况。有一篇由Daniel Cucutayo、Scott Alexander等人撰写的文章，描述了实现超人工智能（ASI) 的步骤，其中有很多有趣的想法，其中包括超级人类程序员和超级人工智能研究员。
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-scientists does to help steer human, 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 like a really important component of how to do great science.
在这方面，有一个"研究品味"的术语。这非常有趣。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，就像AI合作科学家一样，帮助引导杰出的人类科学家，并可能自主确定哪些方向可以产生真正新颖的想法？因为这似乎是如何进行伟大科学研究的一个非常重要的组成部分。
Yeah, I think that's going to be one of the hardest things to to mimic or model is this idea of taste or judgment. I think that's what separates the, you know, the great scientist from the good scientist. Like all professional scientists are good technically, right? Otherwise, it wouldn't have been made it that far in academia and things like that. But then do you have the taste to sort of sniff out what the right direction is, what the right experiment is, what the right question is.
是的，我认为很难复制或模仿的一点就是品味或判断力的概念。我认为这就是伟大科学家和优秀科学家的区别所在。所有职业科学家的技术水平都很高，否则他们无法在学术界走到如此远的地步。但关键在于你是否有那种品味，能够发现正确的方向、设计正确的实验、提出正确的问题。
So picking the right question is the hardest part of science and making the right hypothesis. And that's what, you know, today's systems definitely they can't do. So, you know, 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, I am a maths Olympiad problems where we, you know, our for proof last year, our system got, so you know, silver medal in that, really hard problems.
因此，选择正确的问题是科学中最困难的一部分，同时也是提出正确假设的一部分。而这正是当今的系统绝对无法做到的。我常常说，提出一个猜想，一个真正好的猜想，比解决它更难。所以，我们可能很快会拥有能够解决非常困难猜想的系统。我曾参加数学奥林匹克比赛，在我们去年的一个证明中，我们的系统获得了银牌，解决了非常难的问题。
Maybe eventually we'll better solve a millennium prize kind of problem, but could a system come up with a conjecture worthy of study? There's someone like Terence Tower who had 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. And that is far harder type of creativity. And we don't really know, today's systems clearly can't do that. And we're not quite sure what that mechanism would be.
也许最终我们能够更好地解决千禧年大奖式的问题，但系统能否提出一个值得研究的猜想呢？像特伦斯·陶这样的人可能会说，你知道吗？这个问题真的很深入，涉及数学的本质、数字的本质或者物理的本质。这是一种更难的创造力形式。目前的系统显然无法做到这一点，我们也不太确定实现这种能力的机制是什么。
This kind of leap of imagination, like Einstein had when he came up with, you know, special relativity and then general relativity with the knowledge he had at the time. And for conjecture, the, you want to come up with a thing that's interesting, it's amenable to prove. Yes. So like, it's easier to come up with a thing that's extremely difficult. Yeah. It's easier to come up with a thing that's extremely easy, but at that very edge, that sweet spot, right? Of basically advancing the science and splitting the hypothesis space into two, ideally, right?
这种想象力的飞跃，就像爱因斯坦提出特殊相对论和广义相对论时所具备的那样——也是基于他当时的知识。对于猜想，你希望能提出一个有趣并且可以证明的观点。是的，比起提出一个极其困难的东西，或者一个非常简单的东西，找到那个恰到好处的平衡点要更有意义。这个平衡点能够推进科学进步，并将假设空间一分为二。这是理想的状态。
Whether you fit true or not true, you've learned something really useful. And, and that's hard. And, and, and making something that's also, you know, falsifiable and within sort of the technologies that you have, you can't really 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. Okay. The idea of splitting the hypothesis space into super interesting. So I've heard you say that there's basically no failure in, or failure is extremely valuable. If it's done, if you construct the questions right, if you construct experiments, right? If you design them, right? That failure or success are both useful. So, perhaps because it splits the hypothesis basically to like a binary search. That's right. So when you do like, you know, real blue sky research, there's no such thing as failure, really, as long as you're picking experiments and hypotheses that, that, that, that meaningfully spit the hypothesis space.
无论你的假设最终被证明为真或不真实，你都学到了非常有用的东西。这一点很难做到。而且，创造出可证伪的东西，并在现有技术范围内实施，这实际上是一个非常富有创造性的过程。我认为，仅仅在模型上进行一种简单的搜索是不够的。将假设空间拆分的这个想法非常有趣。我曾听你说过，在这个过程中基本上没有失败，或者说失败的价值是极高的。只要问题和实验设计得当，无论失败还是成功都是有意义的。或许是因为这就像在进行二分搜索，当你进行真正开创性的研究时，只要你选择的实验和假设能够有意义地拆分假设空间，就实际上不存在失败。
So, you know, and you learn something, you can learn something kind of equally valuable from an experiment that doesn't work. That should tell you if you've designed an experiment well, and your hypotheses are interesting, it should tell you a lot about where to go next. And, and then it's you're effectively doing a search process and using that information in, in, in, you know, very helpful ways. 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 alpha, I mean, there's just so many leaps. So alpha fold 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 alpha fold three is doing protein RNA DNA interactions, which is super complicated and fascinating, this amenable to modeling.
所以，你知道，当你学到一些东西时，即使是没有成功的实验，也能学到同样有价值的东西。这应该告诉你，如果你设计了一个好的实验，并且你的假设很有趣，它应该能告诉你下一步该往哪里走。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使用这些信息。回到你想要模拟一个细胞的梦想，要实现这个目标，我们面临的主要挑战是什么？我们可能应该强调一下，例如，阿尔法折叠（AlphaFold）已经取得了许多突破。如果说这解决了蛋白质折叠问题，那是公平的，还有很多令人难以置信的事情可以谈论，包括开源。你们发布的AlphaFold 3正在处理蛋白质与RNA和DNA的相互作用，这相当复杂又迷人，并适合进行建模。
Alpha genome predicts how small genetic changes, like if we think about single mutations, how they link to actual function. So those are, it seems like it's creeping along. Yes, they're sophisticated, it's a much more complicated things like a cell, but a cell has a lot of really complicated components. Yeah. So what I've tried to do throughout my career is I have these really grand dreams, and then I try to, as you've noticed, and then I try to break, but I try to break them down. It's, you know, it's easy to have a kind of a crazily 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? And so virtual cell, which is what I call the project of modeling a cell, I've had this idea, you know, of wanting to do that for maybe more like 25 years.
Alpha基因组预测小的基因变化，比如说我们考虑单个突变时，它们如何与实际功能关联。是的，这些变化看起来像是在慢慢潜入。虽然这些过程很复杂，与一个细胞这样更复杂的事物相比，单个突变的复杂性要小得多，但细胞本身也有很多复杂的组成部分。是的。在我的职业生涯中，我一直试图去做的是有一些宏大的梦想，然后尽量去分解这些梦想。你知道，拥有一个极具野心的梦想很容易，但诀窍在于如何将其分解成可管理的、可实现的中间步骤，这些步骤本身就有意义且有用。所以虚拟细胞——我对细胞建模的项目——这个想法我可能已经有25年了。
And I used to talk with Paul Nurse, who is a bit of a mentor of mine in biology. He runs the, you know, founded the quick institute and one of the nowhere prize in 2001. It is, is, we've been talking about it since, you know, before the, you know, in the 90s. And, and I come used to come back to every five years is like, what would you need to model of the full internals of a cell so that you could do experiments on the virtual cell and what those experiments, you know, in silico, and those predictions would be useful for you to save you a lot of time in the wet lab, right? 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. That would be, that's the, that's the dream.
我过去常常与保罗·纳斯交谈，他在生物学方面是我的一位导师。他创立了著名的研究所，并在2001年获得了诺贝尔奖。我们自上世纪90年代以来就一直在讨论这个话题。我每隔五年就会回到这个问题，那就是要如何建立一个细胞内部结构的完整模型，以便在虚拟细胞上进行实验。这些"计算机模拟"的实验和预测能够为你在实际实验室中节省大量时间。这是我们的梦想。也许通过在计算机中进行大部分实验，再将验证步骤放在实际实验室，可以将实验速度提高100倍。这就是我们的理想。
And so, but maybe now finally, so I was trying to build these components, alpha-follow being one, that that would allow you eventually to model the full interaction, a full simulation of a cell. And I'd probably start with a yeast cell, and partly that's what Paul Nurse studied, because the yeast cell is like a full organism, there's a single cell, right? So it's the kind of simplest single cell organism. And so it's not just a cell, it's a full organism. And yeast is very well understood. And so that would be a good candidate for a kind of full simulated model. Now, alpha-fold is the solution to the kind of static picture of what is a protein, look 3D structure, protein look like, a static picture of it, but we know that biology, all the interesting things happen with the dynamics, the interactions.
好的，现在我终于尝试解释一下我的想法。我正在努力构建一些组件，其中之一是alpha-fold，它可以最终帮助模拟细胞完整的相互作用和整体行为。我可能会从酵母细胞开始，因为保罗·纳斯（Paul Nurse）研究过这种细胞，酵母细胞本身就像一个完整的生物体，只是一个单细胞生物。这是最简单的单细胞生物之一，并且我们对酵母的了解非常深入。所以，说到要模拟一个完整模型，酵母是一个不错的候选对象。
现在，alpha-fold解决的是蛋白质结构静态图像问题，即蛋白质在三维结构中是什么样子，这是静态的。但是我们知道，生物学中所有有趣的事情都发生在动态过程中和相互作用中。
And that's what alpha-fold 3 is the first step towards is modeling those interactions. So first of all, pairwise, you know, proteins with proteins, proteins with RNA and DNA. But then the next step after that would be modeling maybe a whole pathway, maybe like the tour pathway that's involved in cancer or something like this. And then eventually, you might be able to model, you know, a whole cell. Also, there's another complexity here that stuff in a cell happens at different times, skills. Is that tricky? Like, they're, you know, protein folding is, you know, super fast. Yes. I don't know all the biological mechanisms, but some of them take a long time. Yeah. And so that's the level. So the levels of interaction has a different temporal scale that you have to be able to model.
AlphaFold 3的首要目标是模拟这些相互作用。首先是成对的，比如蛋白质与蛋白质、蛋白质与RNA和DNA的相互作用。接下来的步骤可能是模拟整个信号通路，比如与癌症相关的mTOR通路之类的。最终，或许可以模拟整个细胞。不过，这里还有一个复杂性，就是细胞内的活动发生在不同的时间尺度上。比如蛋白质折叠非常迅速，而其他生物机制需要很长时间。因此，不同层次的相互作用需要在不同的时间尺度上进行建模。
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 drop up and down the different temporal stages. So can you avoid me one of the challenges here is not avoid simulating, for example, the quantum mechanical aspects of any of this, right? You want to not overmodel, you can 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 you're modeling any natural system. What is the cutoff level of the granularity that you're going to model it to? Then it captures the dynamics that you're interested in.
这可能会很困难。你可能需要几个模拟系统，以便它们能够在不同的时间动态下互动，或者至少可能需要一个层级系统，这样你可以在不同的时间阶段之间上下切换。这样可以避免一个挑战就是不去模拟，比如量子力学方面的细节，对吧？你不想过度建模，可以直接关注高级层面的东西，以便很好地预测将会发生什么。因此，在对任何自然系统进行建模时，你需要决定建模的精细程度是什么，以便能够捕捉到你感兴趣的动态。
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. So, you know, and of course that's where alpha vault stock 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'd get the emergent behavior. Apologize for the part head questions ahead of time. But do you think we'll be able to simulate a model, the origin of life? So being able to simulate the first from non-living organisms, the birth of a living organism? I think that's one of the, of course, one of the deepest and most fascinating questions. I love that area of biology. You know, these people, like there's a great book by Nick Lane, one of the top top experts in this area called the 10 Great Inventions of evolution.
所以，对于一个细胞，我希望能够在蛋白质层面进行研究，而不必深入到原子层面。当然，这就是AlphaFold技术开始发挥作用的地方。这将成为基础，然后你可以以此为构建模块，进行更高级别的模拟，从而观察到出现的行为特征。提前为这些深入的问题道歉。但你是否认为我们能够模拟生命的起源？也就是说，能够从非活体有机物模拟出一个活体有机物的诞生？我认为这是其中一个最深刻和最令人着迷的问题之一。我非常喜欢这个生物学领域。比如，有本很棒的书，是由这方面的顶级专家之一尼克·莱恩撰写的，书名为《进化的十大发明》。
I think it's fantastic. And it also speaks to what the great filters might be. But, you know, prior or they head of us, I think they're most likely in the past if you read that book of how unlikely to go, you know, have any life at all. And then single cell to multi-cell seems an unbelievably big jump that took like a billion years, I think, on Earth to do. So it shows you how hard it was. Right. So you were super happy for a very long time. Well, very long time before they captured mitochondria somehow, right? I don't see why not, why AI couldn't help with that. So I'm kind of simulation again. It's again, it's a bit of a search process through a combinatorial space.
我认为这非常神奇。这也揭示了「伟大过滤器」可能是什么。你知道的，无论它们是在我们之前，还是在我们前进的路上，我认为它们更可能是在过去。如果你读过那本书，就会知道生命的形成是多么不容易。从单细胞到多细胞的进化似乎是一个令人难以置信的大飞跃，据我所知，在地球上这个过程花费了大约十亿年。这显示了这个过程的艰难程度，对吧？所以，在它们捕获线粒体之前，单细胞生命的状态持续了非常长的时间。而我认为，人工智能完全可能在这个过程中提供帮助。这有点像在一个组合空间中进行搜索和模拟的过程。
Here's like all the, you know, the chemical soup that you start with, the primordial soup that, you know, maybe was on Earth, near these hot vents. Here's some initial conditions. Can you generate something that looks like a cell? So perhaps that would be a next stage off to the virtual cell project is, well, how, how could you actually something like that emerge from the chemical soup? Well, I would love it if there was a move 37 for the origin of life. Yeah. I think that's one of the sort of great mysteries. I think ultimately what we will figure out is their continuum. There's no such thing as a line between non-living and living.
这里有一种化学“原汤”，你知道的，就是你开始时的那些物质，那种原始汤，可能在地球上、这些热裂缝附近存在过。这里是一些初始条件。你能生成看起来像细胞的东西吗？因此，也许这是虚拟细胞项目的下一个阶段，那么，如何能从这种化学汤中产生类似这样的东西呢？我真希望在生命起源方面也能有类似于第37步那样的突破。我认为这是一个巨大的谜团之一。我想最终我们会发现，它们是一个连续体，生与非生之间并不存在明确的界限。
But if we can make that rigorous, yes, that the very thing from the big bang to today is been the same process. If you can break down that wall that we've constructed in our minds of the actual origin of, from non-living to living, and it's not a line that it's a continuum that connects physics and chemistry and biology. Yes. Because there's no line. I mean, this is my whole reason why I worked on AI and AI my whole life because I think it can be the ultimate tool to help us answer these kind of questions. And I don't really understand why the average person doesn't think, like, worry about this stuff more. Like, how can we not have a good definition of life and not living and non-living and the nature of time and let alone consciousness and gravity and all these things?
但是，如果我们能够严谨地理解这一点，那就是从宇宙大爆炸到今天的过程是相同的。如果你能打破我们在脑海中建构的从无生命到有生命起源的那堵墙，而这实际上并不是一条界限，而是一个连接物理、化学、生物的连续过程，是的，因为没有明确的界限。这也是我为什么一生都致力于研究人工智能的原因，因为我认为人工智能可以成为帮助我们解答这类问题的终极工具。我不明白为什么大多数人对此不太关心，比如，我们怎么能没有一个清晰的关于生命、非生命以及时间本质的定义，更不用说意识和引力这些东西。
It's just and quantum mechanics weirdness. It's just, to me, I've always had this, it's sort of screaming at me in my face. And it's getting louder. It's like, how what is going on here? I mean that in the deeper sense, like in the nature of reality, which has to be the ultimate question that would answer all of these things. It's sort of crazy, if you think about it, we can stare each other and all these living things all the time we can expect to at microscopes and take it apart, almost down to the atomic level. yet, we still can't answer that clearly in a simple way, that question of how do you define living? It's kind of amazing.
这就是量子力学的奇异之处。对我来说，这一直在我脑海中挥之不去，而且越来越明显。就像在问：“这里到底发生了什么？” 我指的是更深层次的东西，比如现实的本质，这必须是能够回答所有这些问题的终极问题。想想看，这真的很疯狂，我们可以一直注视彼此以及所有这些生命体，还可以用显微镜观察并分解它们，几乎可以追溯到原子层面。然而，我们仍然无法简单明了地回答那个问题：如何定义生命？这真是令人惊叹。
Yeah, living, you can kind of talk your way out of thinking about, but like consciousness, like we have this very obviously subjective conscious experience, like we're at the center of our own world and it feels like something and then how are you not screaming at the mystery of it all? I mean, but really humans have been contending with the mystery of the world around them for long, long. There's a lot of mysteries. Like what's up with the sun and the rain? Like what's that about? And then like last year we had a lot of rain and this year we don't have rain. Like what do we do wrong? Humans have been asking that question for a long time.
是的，生活中我们可以通过交谈来避免过多思考，但意识就不一样了。我们明显拥有一种主观的意识体验，就像我们是自己世界的中心，这种感觉非常真实。那么，面对这一切的神秘感，你怎么可能不感到震惊呢？实际上，人类已经面对周围世界的奥秘很久了。有许多神秘的事物，比如太阳和雨是怎么回事？为什么去年有很多雨，今年却没有？这是我们做错了什么吗？人类一直在问这些问题。
Exactly. So we're quite, I guess we've developed a lot of mechanisms to cope with this, these the mysteries that we can't fully, we can see, but we can't fully understand and we have to have to just get on with daily life and we keep ourselves busy, right? In a way, do we keep ourselves distracted? I mean, weather is one of the most important questions of human history. We still, that's the go-to small talk direction of the weather. Especially in England. And then it's, which is famously an extremely difficult system to model.
没错。所以我想我们已经发展出了很多应对机制，来处理这些我们能看到但无法完全理解的神秘事物。在日常生活中，我们必须继续前行，让自己忙碌起来，对吧？某种程度上，我们是在让自己分心吗？天气是人类历史上最重要的问题之一。即使现在，谈论天气仍然是人们聊天时最常用的话题，尤其在英国。而天气预报本身也是一个出了名的难以建模的系统。
And even that system, the Google DeepMine has made progress on. Yes, we've created the best weather prediction systems in the world and they're better than traditional fluid dynamics, solar systems that usually calculate on massive supercomputers, takes days to calculate it. We've managed to model a lot of the weather dynamics with neural network systems, with our weather next system. And again, it's interesting that those kinds of dynamics can be modeled, even though they're very complicated, almost bordering on chaotic systems, in some cases.
即使是在那个系统上，谷歌的 DeepMine 也取得了进展。是的，我们创建了世界上最好的天气预测系统，它们优于传统的流体动力学和通常需要大型超级计算机来进行计算的太阳系，这种计算通常需要几天的时间。我们通过一个名为 Weather Next 的系统，利用神经网络成功模拟了许多天气动态。同样有趣的是，即使这些动态非常复杂，有时几乎接近混沌系统，我们仍然能够对其进行建模。
A lot of the interesting aspects of that can be modeled by these neural network systems, including very recently we had cyclone prediction of where, you know, parts of hurricanes might go, of course, super useful, super important for the world. And it's super important to do that very timely and very quickly and as well as accurately. And I think it's very promising direction again of, you know, simulating and so they can run forward predictions and simulations of very complicated real world systems.
其中许多有趣的方面可以通过这些神经网络系统进行建模。最近，我们利用这些系统进行了飓风路径的预测，这对于全球来说非常有用且重要。及时、快速和准确地进行这样的预测非常关键。我认为模拟这些复杂的现实世界系统并进行预测和仿真是一个非常有前途的方向。
As you mentioned that I've got a chance in Texas, to me, a community of folks called the storm chasers. And what's really incredible about them, I need to talk to them more, is they're extremely tech savvy because what they have to do is they have to use models to predict where the storm is. So there it's just, it's this beautiful mix of like crazy enough to like go into the eye of the storm. And like, in order to protect your life and predict where the extreme events are going to be, they have to have increasingly sophisticated models of weather.
就像你提到的，我在德克萨斯有一个机会接触到一个叫做“追风者”的社区。他们最令人惊叹的地方在于，他们都非常精通技术，因为他们需要使用模型来预测风暴的位置。这个群体特立独行，敢进入风暴中心，同时为了保护自己的生命并预测极端天气事件，他们必须拥有越来越先进的气象模型。这个群体真的是疯狂与智慧的完美结合，我很想与他们深入交流。
Yeah. Yeah, it's a beautiful balance of like being in it as living organisms and the cutting edge of science. So they actually might be using deep mind systems. So that's yeah, they are, hopefully they are. And I love to join them in one of those cases. They look amazing, right? It's actually experience it one time. Exactly. And then also to experience the correct prediction, where something will come. And how is going to evolve? It's incredible.
是的，是的，这是一种美妙的平衡，既要像活的有机体一样参与其中，又要站在科学的前沿。所以他们实际上可能在使用DeepMind系统。是的，希望他们确实在用。而且我很想在这样的情况下加入他们。他们看起来很了不起，对吧？可以实际体验一次。没错，还能亲身体验正确的预测，知道事情会如何发展，真的很不可思议。
Yeah. You've estimated that we'll have a GI by 2030. So there's interesting questions around that how will we actually know that we got there? And what maybe the move, quote, move 37 of a GI? My estimate is sort of 50% chance by in the next five years. So, you know, by 2030, let's say. And so I think there's a good chance that that could happen. Part of it is what is your definition of a GI? Of course, people are arguing about that now.
好的。你估计我们会在2030年实现通用人工智能（GI）。所以，有一些有趣的问题，比如我们如何确定我们已经达到了这个目标？以及可能是什么事件标志着GI的关键时刻？我估计在接下来的五年内，我们有50%的可能性实现这一点，所以可以说到2030年，有很大机会能实现。其中一部分取决于你对GI的定义。当然，现在人们对此还在争论。
And mine's quite a high bar and always has been of like, can we match the cognitive functions that the brain has? Right. So we know our brains are pretty much general churing machines approximate. And of course, we created incredible modern civilization with our minds. So that also speaks to how general the brain is. And for us to know, we have a true AGI, we would have to like make sure that it has all those capabilities.
翻译成中文：我的标准一直以来都设得很高，比如说，我们能否匹配大脑的认知功能。我们知道，我们的大脑几乎就是通用的图灵机的近似版。当然，我们用大脑创建了令人惊叹的现代文明，这也说明了大脑的通用性。若我们想确认我们拥有真正的通用人工智能（AGI），我们必须确保它具备所有这些能力。
It isn't kind of a jagged intelligence where some things it's really good at like today's systems, but other things it's really flawed at. And 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, I think, capabilities, like sort of the true invention capabilities and creativity that we were talking about earlier. So you'd want to see those. How you test that? I think you just test it one way to do it would be kind of brute force test of tens of thousands of cognitive tasks that, you know, we know that humans can do and maybe also make the system available to a few hundred of the world's top experts, the Terence Towers of each subject area and see if they can find, you know, give them a month or two and see if they can find an obvious floor in the system.
这段文字的意思是：目前的系统不够稳定，有些地方表现很好，但在其他方面却有很大缺陷，我们现在的系统就是这样不一致。我们希望智能可以在各个方面保持一致。此外，我们还缺少某些能力，比如真正的发明能力和创造力。如何测试这些能力呢？一种方法是进行大量的认知任务测试，比如测试成千上万个我们知道人类能完成的任务。并且可以让世界上各个领域的几百名顶尖专家参与，让他们在一两个月内寻找系统中显而易见的缺陷。
And if they can't, then I think you're pretty, you know, pretty, you can be pretty confident. We have a fully general system. Maybe to push back a little bit, it seems like humans are really incredible as the intelligence improves across all domains to take it for granted. Like you mentioned, Terence Tau, these brilliant experts, they might quickly in a span of weeks take for granted all the incredible things you can do and then focus in, well, ha ha right there. You know, I consider myself a huge, a virtual human. Yeah. I identify as human. You know, some people listen to me talk and they're like, that guy is not good at talking, the stuttering, the, you know, so like even humans have obvious across domains, limits, even just outside of calc mathematics and physics and so on.
如果他们做不到，那我认为你可以很有信心地认为我们拥有一个完全通用的系统。或许反过来想一下，人类在各个领域的智慧提升后，往往会习以为常。正如你提到的特伦斯·陶这些杰出的专家，他们可能在几周内就习惯了所有令人难以置信的成就，然后专注于其他事情。我把自己看作虚拟人类的一分子。我认同自己是个人类。有些人听我说话时可能会觉得我不擅长交流，因为我会结巴，甚至在数学、物理等领域之外，人类也明显有其局限性。
It, I wonder if it will take something like a move 37. So on the positive side versus like a barrage of 10,000 cognitive tasks where it will be one or two where it's like, yes, holy shit. So I think there are exactly. So I think there's the sort of blanket testing to just make sure you got the consistency, but I think there are the sort of lighthouse moments like the move 37 that will I would be looking for. So one would be inventing a new conjecture or new hypothesis about physics like Einstein did. So maybe you could even run the back test of that very rigorously. Like have a cutoff of knowledge cutoff of 1900 and then give the system everything that was, you know, that was written up to 1900 and then and then see if it could come up with special relativity and generativity, right?
我想知道是否需要像第37步那样的突破。在积极方面，这就像进行一连串的认知任务，其中会有一两个时刻让人感到非常震惊。我认为需要进行全面测试以确保一致性，但我也在寻找像第37步那样的关键时刻。例如，有人能像爱因斯坦那样提出一个关于物理的新猜想或新假设。也许你可以非常严格地进行回测，比如设定一个1900年的知识截止点，然后给系统提供所有1900年前的信息，看它能否提出狭义和广义相对论，对吗？
Like Einstein did that would be an interesting test. Another one 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 as aesthetically beautiful as elegant as go? And those are the sorts of things I would be looking out for and probably a system being able to do several of those things, right? It's for it to be very general, not just one domain. And so I think that would be the signs, at least that I would be looking for that we've got a system that's a GI level. And then maybe to fill that out, you would also check the consistency, you know, make sure there's no holes in that system either.
像爱因斯坦那样进行测试会很有趣。另一个有趣的测试是，系统是否能发明一种像围棋一样的游戏，不仅仅是提出第37步这样的新策略，而是能创造出一个和围棋一样深刻、美观、优雅的游戏。这些是我会关注的事情，而且我可能希望一个系统能够做到其中的几件事，对吧？这意味着它要非常通用，而不仅仅局限于一个领域。所以，我认为这是我们拥有一个通用人工智能（AGI）系统的迹象，至少那是我会寻找的标志。为了进一步验证这一点，你还应该检查系统的一致性，确保没有漏洞。
Yeah, something like a new conjecture or a side-to-f discovery. That would be a cool feeling. Yeah, that would be amazing. So it's not not just helping us do that, but actually coming up with something brand new. And you would be in the room for that. And so it would be like probably two or three months before announcing it. And you would just be sitting there trying out to tweet something like that exactly. It's like, what is this amazing you, you know, physics idea, and then we would trouble you check it with world experts in that domain, right? And validate it and kind of go through its workings. And I guess it would be explaining its workings too.
是的，就像是一个新的猜想或者旁门发现。那种感觉会很酷。是的，那将会非常棒。所以这不仅仅是帮助我们实现这个目标，而是真正地提出一些全新的东西。而且你会在现场。可能会在宣布之前两三个月就发生这种情况。而你会坐在那里，努力不去发推文描述这个想法，就像这个特别棒的物理学创意是什么一样。然后我们会找这个领域的世界级专家来查证和验证，并详细研究它的运作过程。我想还得解释它是如何运作的。
Yeah, be an amazing moment. Do you worry that we as humans, even expert humans, like you might miss it? Might miss. Well, it may be pretty complicated. So it could be the analogy I give there is, I don't think it will be totally mysterious to the best human scientist, but it may be a bit like, for example, in chess, if I was to talk to Gary Casparo for Magnus Carlson 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 why afterwards that move made sense. And we would better understand it to some degree, not to the level they do, but you know, if they were good at explaining, which is actually part of intelligence too, is being able to explain in a simple way that what you're thinking about.
是啊，那将是一个惊人的时刻。你是否担心我们作为人类，即使是像你这样的专家，也可能会错过它呢？可能会错过。嗯，这可能会相当复杂。我给出的比喻是，我不认为这会对最优秀的人类科学家来说完全神秘，但可能有点像国际象棋。比如说，如果我和加里·卡斯帕罗夫或马格努斯·卡尔森下棋，他们做出一个精彩的棋步，我可能无法自己想到这个棋步，但他们可以事后解释为什么这个棋步有意义。我们或许可以在一定程度上理解，虽然达不到他们的水平，但如果他们擅长解释——而这实际上也是智慧的一部分，即能够用简单的方式解释你在想什么。
I think that that will be very possible for the best human scientist. But I wonder, maybe you can, you can educate me on the side of go. I wonder if there's moves from Magnus or Gary where they at first will dismiss it as a bad move. Yeah, sure. It could be, but then afterwards they'll figure out with their intuition that this why this works and then and then empirically the nice thing about games is one of the great things about games is you can it's a sort of scientific test. Does it do win the game or not win? And then that tells you, okay, that move in the end was good, that strategy was good. And then you can go back and analyze that and and and and explain even to yourself a little bit more why explore around it. And that's how chess analysis and things like that work. So perhaps that's why my brain works like that because I've been doing that since I was four and you're trained, you know, trade is sort of hardcore training in that way.
我认为，对于顶尖的人类科学家来说，这是非常有可能的。但我想知道，也许你可以在围棋方面给我一些指导。我想知道是否有某些由马格努斯或盖里做出的棋步，他们最初可能会认为这是一个不好的棋步。是的，当然有可能，不过后来他们通过直觉认识到这是有效的棋步。游戏的一个好处是它们提供了一种科学的测试方法——要么赢，要么输。然后这告诉你，这步棋最终是好棋，策略是好的。之后你可以回过头来分析，并进一步解释为什么这会有效。这就是国际象棋分析等工作的方式。也许这就是为什么我的思维方式是这样的，因为我从四岁起就一直这样做，并且接受了这种严格的训练。
But even even now, like when I generate code, there is this kind of nuanced fascinating contention that's happening where I might have first identified as a set of generated code is incorrect in some interesting nuanced ways. But then I'm always have to ask the question, is there a deeper insight here that I'm the one who's incorrect? And that's going to as the systems get more and more intelligent, you're going to have to tell that it's like, what what what he is this a bug or a future where you just came up with? Yeah, and they're going to be pretty complicated to do. But of course it will be you can imagine also AI systems that are producing that code or whatever that is and then human program is looking at but also not unadded 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 they're more that you know, not kind of monitoring tools are the ones that generated it.
即使是现在，当我生成代码时，会发生一种微妙而引人入胜的对抗。我可能最初认为一组生成的代码在一些有趣而微妙的方式上是错误的。但我总是不得不问自己一个问题：会不会是我自己错了，而这里有更深的见解？随着系统变得越来越智能，你将不得不判断这是一个bug还是一个你刚刚想到的"未来"。这将会变得相当复杂。当然，你可以想象AI系统在生成代码或其他内容时，人类程序员也在借助AI工具进行审查和修改。所以这将是一种有趣的互动，可能会用到不同的AI工具，而不仅仅是生成它们的那些工具。
So if we look at a AI system, sorry to bring it back up, but I fully evolve. Super cool. So I've evolved enables on the programming side, something like recursive self improvement, potentially. Like what if we can imagine what that AI system, maybe not the first version, but a few versions beyond that. What does that actually look like? Do you think it will be simple? You think it will be something like a self improving program and a simple one? 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 take off scenario. But but you these current systems like alpha evolve. They have, you know, human in the loop deciding on various things, their separate hybrid systems that interact. One could imagine eventually doing that end to end. I don't see why that wouldn't be possible.
如果我们看一个AI系统，很抱歉又提到这个，但我已经完全进化。超级酷。因此，我进化出的能力在编程方面可能类似于递归自我改进。那么，如果我们能想象一下那个AI系统，也许不是第一个版本，而是几个版本之后的样子。你觉得它会简单吗？你认为它会像一个自我改进的程序且很简单吗？我想说，有潜在可能，但不确定这是否是我们希望的，因为这有点像一种快速崛起的情景。不过，目前的系统，比如Alpha进化，它们有人类参与来决定各种事情，有不同的混合系统交互。可以想象，最终可以完全自主运行。我看不出为什么这会不可能。
But right now, you know, 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 reconnected to this idea of coming up with a new conjecture hypothesis. How 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. And I think that's related to this idea of like invent a game as good as go. 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, you know, and as you know, without revolve, like things like faster matrix multiplication.
但是现在，我认为这些系统还不够完善，无法独立构建代码架构。同时，这与提出新的假设或猜想的想法略有关联。当你给它们非常具体的指令时，它们表现得很好。但如果你给的是非常笼统的高层次指令，目前的系统就无法处理。我想这与类似“设计一个和围棋一样好的游戏”这样的指令相关，这种指令非常不明确。所以当前的系统可能不知道如何处理这些问题，将其细化到可行的程度。我认为有类似的情况，比如说，“做一个更好的自己”也是一个过于宽泛的问题。但我们在某些领域已经有了进展，比如更快的矩阵乘法。
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. Yeah, but it could also be sort of the pushback against hard take-offs scenario. It could be just 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, you'll slow down. So there would be like a, like the path to AGI won't be like a, it would be a gradual improvement over time. Yes. If it was just incremental improvements, that's how it would look.
当你将目标细化到非常具体的事情时，它非常擅长逐步改进。但目前，这些改进更像是逐渐的提升，一种小的迭代。相较之下，如果你想要在理解上有一个大的飞跃，则需要更大的进步。不过，这种情况也可能是催化快速突破情境的阻力，也可能只是一系列渐进的改进，比如矩阵乘法。它可能需要耗费几天时间来思考如何逐步改进一个事物，并且以递归的方式进行。当你进行越来越多的改进时，速度会慢下来。因此，通向通用人工智能的道路不会是迅速跳跃，而是随着时间逐渐改进。是的，如果只是渐进式的改进，就会是这样的情形。
So the question is, could it come up with a new leap, like the Transformers architecture? Like could it have done that back in 2017 when we did it and brain did it? And it's not clear that these systems, something our alpha vol wouldn't be able to do, make such a big leap. So for sure, these systems are good. We have systems, I think, that can do incremental hail 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? 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, leap.
所以问题是，这些系统能否提出新的突破，比如说Transformers架构这样的突破？就像我们在2017年做的那样，它能否做到呢？目前还不清楚这些系统是否能够做出如此重大的飞跃。所以可以肯定的是，这些系统非常强大。我认为我们有一些系统能够做逐步的改进。这就引发了一个更大的问题：这是否就是我们所需的一切，还是我们实际上需要一两次更大的突破？这些相同类型的系统能否也提供这样的突破？所以这可能会是多个S型曲线的过程，既有逐步改进的部分，但偶尔也会有大的飞跃。
Yeah. I don't think anyone has systems that can have shown unequivocally those big leaps. We have a lot of systems that do the hail climbing of the S curve that you're currently on. Yeah. And that would be the move 37. Yeah. I think it would be a leap. Something like that. Do you think the scaling laws are holding strong and the pre-training, post-training, test line compute? Do you on the flip side of that anticipate AI progress hitting a wall? We certainly feel there's a lot more room just in the scaling. So actually all steps, pre-training, post-training, and inference time.
是的。我认为没有任何系统可以明确展示出那种巨大的飞跃。我们有很多系统是在你当前所处的S曲线上进行逐步攀升的。是的，那就像是第37步。我认为那将是一次飞跃之类的。你觉得扩展法则是否依然有效，包括预训练、后训练和测试阶段的计算？你反过来是否预计AI的进展会遇到瓶颈？我们确实感受到，在扩展方面还有很大的空间。事实上，所有阶段，包括预训练、后训练和推理时间都有可能改进。
So there's sort of three scalings that are happening concurrently. And 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 incredible researchers and people like Noem Shazir who came up with Transformers and Dave Silver who led the AlphaGo project and so on. And that research base means that if some new breakthrough is required like in 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, because then there's more from just engineering to true research or research plus engineering.
同时，有三种规模正在发生变化。这再次说明了创新能力的重要性。我们引以为豪的是我们拥有最广泛、最深入的研究团队。我们有杰出的研究人员，比如发明了Transformers的Noem Shazir和领导了AlphaGo项目的Dave Silver等。这种研究基础意味着，如果需要像AlphaGo或Transformers那样的新突破，我相信我们是能够实现这一点的地方。因此，我实际上很喜欢当面临挑战时的情形，因为这时候不仅仅需要工程技术，还需要真正的研究，或者说研究与工程技术的结合。
And that's our sweet spot. And I think that's harder. It's harder to invent things than to fast follow. And so we don't know. I would say it's 50-50 whether new things are needed or whether the scaling of the existing stuff is going to be enough. And so in true empirical fashion, we're pushing both of those as hard as possible. The new blue sky ideas and maybe about half our resources on that. And then scaling to the max, the current capabilities.
这正是我们擅长的领域。我认为这更加艰难。发明新事物比快速跟随要困难得多。因此，我们不确定。我想说，有一半的可能性需要新事物，而另一半可能现有技术的扩展就足够了。所以，在真正实事求是的方式下，我们将这两个方面都推向极限。大约一半的资源用于开发全新的蓝天创意，而另一半则用于最大化现有能力的扩展。
And we're still seeing some fantastic progress on each different version of Gemini. That's interesting the way you put it in terms of the deep bench that if progress towards a GI is more than just scaling compute, so the engineering side of the problem. And as more on the scientific side where there's breakthroughs needed, then you feel confident deep mind as well, Google deep mind as well, positioned to kick ass in that domain. Well, I mean, if you look at the history of the last decade or 15 years, it's been maybe, I don't know, 80-90% of the breakthroughs that underpin modern AI feel today was from, you know, originally Google brain, Google research and deep minds.
我们在每个不同版本的Gemini上仍然看到了一些出色的进展。你用"深厚的基础"来形容这种进展确实有趣，如果说向通用智能（GI）的进步不仅仅是扩大计算规模的问题，而更多地需要在科学领域取得突破，那么你对DeepMind，以及Google的DeepMind在这个领域取得成功充满信心。事实上，如果你回顾过去十年或十五年的历史，现代人工智能的突破中，大约80-90%是来自谷歌大脑、谷歌研究和DeepMind的成果。
So yeah, I would back that to continue hopefully. So on the data side, are you concerned about running out of high quality data, especially high quality human data? I'm not very worried about that partly because I think there's enough data and it's been proven to get the systems to be pretty good. And this goes back to simulations again. If you do have enough data to make simulations so that you can create more synthetic data that are from the right distribution. Obviously, that's the key. So you need enough real world data in order to be able to create those kinds of data generators.
所以，是的，我希望这能够继续。在数据方面，你是否担心高质量数据，特别是高质量的人类数据会耗尽？我对此并不太担心，部分原因是我认为已经有足够的数据，并且事实证明这些数据可以使系统表现得相当不错。这又回到了模拟。如果你有足够的数据来进行模拟，你就可以创建更多来自正确分布的合成数据。显然，这是重点。因此，你需要足够的真实世界数据来创建这样的数据生成器。
And I think that we're at that step at the moment. Yeah, you don't have a lot of incredible stuff on a set of science and biology during a lot with not so much data. Yeah, I mean, still a lot of data, but I guess enough take off. Yeah, that going exactly. Yeah, exactly. How crucial is the scaling of compute to building a GI? This is a question that's an engineering question. It's a almost geopolitical question because it also integrated into that is supply chains and energy. I think that you care a lot about, which is potentially fusion. Innovating on the side of energy also.
我认为我们现在正处在这个阶段。是的，虽然在科学和生物学领域的数据并不算太多，但已经做了很多令人惊叹的事情。我的意思是，数据依然不少，但我猜已经足够推进发展。对，完全正确。那么，计算能力的扩展对于构建通用人工智能（AGI）有多重要呢？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。我认为你非常关心这个问题，尤其是潜在的核聚变技术。在能源方面的创新也是如此。
Do you think we're going to keep scaling compute? I think so for several reasons. I think compute, there's the amount of compute you have for training often it needs to be co-located. So actually, even like, you know, bandwidth constraints between data centers can affect that. So it's, there's additional constraints even there. And that, 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.
你认为我们会继续扩大计算能力吗？我认为会，原因有几个。首先，对于训练所需要的计算资源来说，往往需要放在一起进行，这样即使是数据中心之间的带宽限制也会产生影响。因此，在训练过程中会有额外的限制，这很重要，特别是在训练最大规模的模型时。此外，随着人工智能系统现在已经被应用到产品中，并且被全球数十亿人使用，现在我们需要大量的计算能力来进行推理。
And then on top of that, there's the thinking systems, the new paradigm of the last year that 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 they'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 that's required.
除此之外，还有思维系统，这是去年出现的新范式。这些系统在测试时给予它们更多推理时间，它们就会变得更聪明。所有这些都需要大量的计算能力，我不认为这个趋势会放缓。随着人工智能系统的提高，它们会变得更加有用，人们对它们的需求也会随之增加。因此，从训练方面来看，训练实际上只是其中的一部分，可能甚至成为整个计算需求中的较小部分。
Yeah, that's one sort of almost meami kind of thing, which is like the success and the incredible aspects of VO3. There's people kind of make fun of like the more successful it becomes. The, you know, the servers are sweating. Yes. 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 is you know we have our own TPU line, and we're looking at like inference only things, inference only chips, and how we can make those more efficient.
是的，这有点类似于一种"模因式"的东西，比如VO3的成功和它令人难以置信的方面。有些人会调侃说，VO3越成功，服务器的负担就越重。确实是这样。我们还拍了一个服务器煎蛋的视频，没错，我们得想办法解决这个问题。我们正在进行许多有趣的硬件创新，比如我们有自己的TPU生产线，正在研究仅用于推理的芯片，以及如何提高它们的效率。
We're also very interested in building AI systems. And we have done the 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 good room temperature. Super conductors has always been on my list of dream breakthroughs and optimal batteries.
我们也对构建人工智能系统非常感兴趣，并且在能源使用方面提供了帮助。比如，帮助数据中心提高冷却系统的效率，优化电网，最终帮助等离子体约束聚变反应堆的运行。我们已经与Commonwealth Fusion公司在这些方面展开了大量合作，并且可以设想在反应堆设计方面的应用。此外，材料设计也是我认为最令人兴奋的新领域之一，比如用于制造太阳能电池板的新型太阳能材料，性能极佳的室温超级导体，以及优化电池，这些一直是我梦寐以求的突破。
And I think a solution to any, you know, one of those things would be absolutely revolutionary for, you know, climate and energy usage. And we're probably close, you know, and again, in the next five years to having AI systems that can materially help with those problems. If you were to bet, sorry, for the ridiculous question, what is the main source of energy in like 20, 30, 40 years? Do you think it's going to be nuclear fusion? I think fusion and solar are the two that I would bet on.
我认为，解决其中任何一个问题的方案对于气候和能源使用来说都会是彻底的革新。而且，在接下来的五年里，我们可能会接近拥有能够实质性帮助解决这些问题的人工智能系统。如果要打个赌——抱歉，这个问题有点可笑——在未来二三十或者四十年里，您认为主要的能源来源会是什么？您认为会是核聚变吗？我认为我会押注于核聚变和太阳能这两者上。
Solar, I mean, you know, it's the fusion reactor in the sky, of course. And I think really the problem there is batteries and transmission. So, you know, as well as more efficient, more more efficient solar material, perhaps eventually, you know, in space, you know, these kind of disinsphere type ideas. And fusion, I think, is definitely doable, seems, if we have the right design of reactor and we can control the plasma and fast enough and so on. And I think both of those things will actually get solved.
太阳能，就是说，它当然是天空中的一个“聚变反应堆”。我认为，问题主要在于电池和传输。因此，我们需要更高效的太阳能材料，也许最终在太空中，实现类似戴森球的概念。而聚变能，我认为在设计出合适的反应堆并能迅速控制等离子体的情况下，肯定是可行的。我相信这些问题最终都会得到解决。
So we'll probably have at least those are probably the two primary sources of renewable, clean, almost free, or perhaps free energy. 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 1 Kardashev scale civilization? I would not be that surprised if there's a like a 100 year time scale from here.
所以，我们可能至少有这两个主要来源的可再生、清洁、几乎免费的，甚至可能是免费的能源。活在这样一个时代真是令人兴奋。如果我和你一起穿越到100年后的未来，当我们达到卡尔达舍夫文明等级表的1型文明时，你会有多惊讶？如果从现在开始算起有一个100年的时间跨度的话，我不会感到太惊讶。
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, you know, fairly wealthy countries like Singapore and Israel and so on, like actually use it.
我的意思是，我认为很明显，如果我们能通过刚才讨论的方式之一解决能源问题，比如核聚变或高效太阳能，那么如果能源变得几乎免费、可再生和清洁，那就可以解决许多其他问题。例如，水资源获取的问题将不再是问题，因为你可以利用海水淡化技术。我们已经拥有这个技术，只是它过于昂贵。所以目前也只有一些富裕国家，比如新加坡和以色列等，才真正使用这种技术。
But if it was cheap, then then all countries that have a coast could. 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.
但如果成本降低，那么所有拥有海岸线的国家都可以参与。此外，你将拥有无限的火箭燃料。只需使用能源将海水分解为氢气和氧气，这就是火箭燃料。因此，配合埃隆那种可以自行着陆的火箭，太空旅行可能就像公交服务一样普及。这将开启令人难以置信的新资源和领域，我认为小行星采矿将成为可能，并推动人类在星际间的最大繁荣。
That's what I dream about as well as 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. Yeah, I wonder what it would look like. If you just a tourist flying through space, you would probably notice Earth because if you saw the energy problem, you would see a lot of space rockets probably.
我也梦想着这个，就像卡尔·萨根的想法那样，把意识带给宇宙，让宇宙“醒来”。我认为，人类文明最终会做到这一点，只要我们能正确运用人工智能，解决其中一些问题。是的，我在想，这会是什么样子。如果你只是一个在太空中旅行的游客，你可能会注意到地球，因为如果能解决能源问题，你可能会看到很多太空火箭。
So it would be like traffic here in London, but in space. That's a lot of rockets. And then you would probably see floating in space, some kind of source of energy like solar. Yeah, potentially. So Earth would just look more on the surface, more technological. And then you would use the power of energy than to preserve the natural yes, like the rainforest and all that. Exactly. Because for the first time in human history, we wouldn't be resource constrained. And I think that could be 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, you know, if the tigers have there for us, then the local villages can't, what are they going to use? I think that this will help a lot.
这就像是在伦敦的交通，只不过是在太空中。这意味着很多火箭可能会在太空中穿梭。然后你可能会看到在太空中漂浮着某种能源，比如太阳能。对，很有可能。地球表面会显得更加科技化。然后你会利用这种能量来保护自然资源，比如雨林等等。没错。因为在人类历史上，我们将不再受资源的限制。我认为这可能是人类的一个新的、令人惊叹的时代，而不是零和的局面。对吧？比如，我拥有这块土地，而你没有。或者说，如果老虎在它们的栖息地生活，我们就无法使用那块土地，那么当地村民该怎么办？我认为这将带来很大的帮助。
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, I think, one of the big vectors, which is scarcity of resources, including land and more materials and energy. And we should be, as I'm just calling it, like another's called about this kind of radical abundance era, where there's plenty of resources to go around. Of course, the next big question is making sure that that's fairly shared, fairly, and everyone in society benefits from that. So there is something about human nature where I go, you know, it's like, bore at my neighbor, like you start trouble, we do start conflicts.
不，这不会解决所有问题，因为人类的其他弱点仍然会存在。但我认为，至少能消除一个大问题，那就是资源稀缺，包括土地、材料和能源的稀缺。我们应该称之为一个“资源极大丰富的时代”，这个时代资源充足，人人受益。当然，接下来的一个大问题是如何确保资源的公平分配，让社会中的每个人都能受益。至于人性的问题，我觉得有时候我们就像邻居之间的争吵，总会引发冲突。
And that's why games, throughout, as 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 pull us, they give us that scratch the edge of conflict, whatever that is about us, the human nature, and then avoid the actual hot wars that would come with, increasingly sophisticated technologies, because we're now, with long past the stage where the weapons we're able to create can actually just destroy all of human solutions. So it's no longer that's no longer a great way to start shit with your neighbor.
正因为如此，正如我在学习中越来越发现的那样，游戏在古代历史中也起到了让人们远离战争特别是热战的作用。也许我们可以创造出越来越复杂的视频游戏，以满足人性中对冲突的渴望，从而避免用愈加复杂的技术进行的真正热战。因为我们已经远远超越了那个我们制造的武器能够毁灭整个人类解决方案的阶段。所以，与邻居搞矛盾再也不是一个好方法了。
It's better to play a game of chess. Or football. Or football. Yeah. And I think, I mean, I think that's what my modern sport is. So and 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 in its tribal. And I think it does channel a lot of those energies into a, which I think is a kind of human need to belong to some group. But into a fun way, a healthy way, and not a not destructive way, kind of constructive thing.
下棋或者踢足球更好。是的，我认为这就是我的现代运动。我喜欢看足球比赛，而且我以前也经常踢足球。足球给人的感觉很原始，很有部落性。我认为它确实能把很多人内心的能量引导到一种人类需要，比如归属感，但又是以一种有趣、健康，而非破坏性的方式实现，是一种建设性的活动。
And I think going back to games again is, I think they're originally why they're so great as well for kids to play things like chess is their great little microcosm simulations of the world. They're simulation of the world too. They're simplified versions of some real world situation whether it's poker or, or go or chess, different aspects or diplomacy, different aspects of the real world. And it allows you to practice at them too. And because, you know, how many times do you get to practice a massive decision moment in your life? You know, what job to take, what university go to, you know, you get maybe, I don't know, a dozen or so key decisions one has to make.
我认为回到游戏这一话题，之所以游戏特别适合孩子们，比如下棋，是因为游戏本质上是世界的一个小模拟。游戏实际上也是世界的模拟。它们是对某些现实情况的简化版本，比如扑克、围棋、国际象棋，甚至是外交，涉及到现实世界的不同方面。游戏让你有机会在其中练习。毕竟，在生活中有多少次你能有机会练习做出重大决策？比如选择什么工作，去哪个大学学习，这样的重要决定可能只有十来个吧。
And you've got to make those as best as you can. And games is 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. Well, I think it's also really important to practice losing and winning. Right. Like losing is a really, you know, that's why I love games, that's why I love even things like Brazilian Jiu-Jitsu, 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.
你必须尽量做到最好。而游戏是一种安全且可重复的环境，你可以在其中提升自己的决策能力。它可能还有额外的好处，就是引导一些能量投入到更具创意和建设性的活动中。我认为，练习如何输赢也是非常重要的。对我来说，这就是我喜欢游戏的原因，也包括像巴西柔术这样的活动，在这些安全的环境中，你可以不断地被打败。这提醒我们关于物理法则、世界运作方式：有时你会失败，有时你会成功。
You can still be friends with everybody. Yeah. That feeling of losing. I mean, it's a weird one for us humans to like really like make sense of, like that's just part of life. That is a fundamental part of life is losing. Yeah. And I think the martial arts as I understand it, but also in things like light chess is 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 being the other person. It's about maximizing your own potential.
你仍然可以和每个人做朋友。是的，那种失去的感觉。对我们人类来说，这是一种很难理解的感觉，但它就是生活的一部分。失去是生活的基本部分。是的，我认为武术和像象棋这样的活动，至少在我的理解中，很多都是关于自我提升和自我认知的。你会意识到自己做过的事情，并且明白这并不只是为了打败对方，而是为了最大化自己的潜力。
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 just 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 you can mentor you. You know, I think you learn that. I'm pretty sure 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, and it can be very hardcore and very important. You of course, you want to win, but you also need to learn how to.
如果你以一种健康的方式去面对胜负，你会学会如何正确地利用胜利和失败。不要因为胜利而得意忘形，认为自己就是世界上最棒的。而失败则让你保持谦逊，并且时刻意识到自己还有很多东西需要学习。总有更为资深的专家可以指导你。我相信在武术中你会明白这一点，而我个人也是在国际象棋的训练中这样培养起来的。同样道理，这是非常重要的一课。当然，你会想赢，但你也需要学会如何去面对和处理胜负。
Deal with setbacks in a healthy way that and why are that feeling that you have when you lose something into a constructive thing of next time, I'm going to improve this, right? Or get better at this. There is something that's a source of happiness, a source of meaning, that improvements that it's not about the winning or losing. Yes, the mastery. There's nothing more satisfying in a way. It's like, oh, wow, this thing I couldn't do before. Now I can.
以健康的方式处理挫折，把失去时的那种感觉转化为一种建设性的态度，比如下一次我要改进这个，对吧？或者我要在这方面变得更好。有一种东西是幸福和意义的来源，那种进步并不在于胜败，而在于掌握技能。没有什么比这种体会更令人满足了，就像“哇，以前我做不到的事情，现在我可以做到”一样。
And again, games and physical sports and mental sports, they're ways of measuring. They're beautiful because you can measure that progress. There's something about, I guess, why I love role playing games. Like the number go up of like on the skill tree. Like literally, that is a source of meaning for us humans. Whatever, we're quite addicted to this sort of, yeah, these numbers going up. And and maybe that's why we made games like that because obviously. that is something we're heel climbing systems ourselves, right? It would be quite sad if we didn't have any mechanism. We call it bells. We do this everywhere, right? Where we just have this thing that's great. I don't want to dismiss that. That is a source of deep meaning. Yeah, it's humans.
再一次，游戏、体育运动和智力运动，它们都是衡量进步的方式。它们之所以美妙，是因为你可以看到进步的具体数值。这也是我为什么喜欢角色扮演游戏的原因之一，比如技能树上的数值增加。这种数值的增长对我们人类来说是一种意义的来源。我们对这种数字增长非常痴迷。也许这就是为什么我们设计出这样的游戏，因为我们本身就是一个不断攀登的系统。如果我们没有任何机制来激励进步，那就太可悲了。我们称之为奖励，不论在哪里，我们都有这样的机制，这很棒。我并不是想贬低这一点，因为它确实是深刻意义的来源。是的，这就是人类。
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 at one five. And now it's winning, which I went to five. And you took the helm and you 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?
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取得的成就。我认为可以公平地说，一年前谷歌在大型语言模型（LLM）产品上处于劣势，当时的产品是Gemini 1.5。但现在，谷歌已经取得了胜利，即Gemini 2.5。你接管了这个工作，并带领团队取得了这样的成就。在这一年间，从所谓的“劣势”到所谓的“优势”，究竟需要付出什么努力呢？
Yeah, well, firstly, it's an absolutely incredible team that we have, you know, led by Corre and Jeff Dean and and Oriol and the amazing team we have on Gemini. Absolutely world class. So you can't do it without the best talent. And of course, you have, you know, we have a lot of great compute as well. But then it's the research culture we created, right? And basically coming together, both different groups in in Google, you know, there was Google Brain, world class team and and then the old deep mind and pulling together all the best people and the best ideas and gathering around to make the absolute greater system we could.
是的，首先，我们拥有一支绝对令人难以置信的团队，由Corre、Jeff Dean和Oriol领导，以及我们在Gemini项目中的出色团队，绝对是世界级的。要成就这一切，就需要最优秀的人才。当然，我们也有很多强大的计算资源。但关键是我们创建的研究文化。基本上，我们汇聚了谷歌内不同团队的力量，包括世界一流的Google Brain团队和原DeepMind团队，汇集了所有最优秀的人才和最好的想法，致力于打造出绝对顶尖的系统。
And it was being hard. But we're all very competitive. And we, you know, love research. This is so fun to do. And we, you know, it's great to see after jetty wasn't a given, but we're very pleased with the where we are in the rate of progress is the most important thing. So if you look at where we've come to from two years ago to one year ago to now, you know, I think how we call it relentless progress, along with relentless shipping of that progress is being very successful. And, you know, it's unbelievably competitive. The whole space, 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 very, you know, been pleasing for us to see that progress.
这过程一直都很艰难。但我们都有很强的竞争意识，而且我们热爱研究，这让事情变得非常有趣。尽管起初并不确定结果，但我们对取得的进展感到非常满意，这才是最重要的。回顾两年前、一年前到现在的历程，我们称之为不懈的进步，加上不断推出这些进展是极为成功的。而且，你知道，整个领域竞争异常激烈。整个人工智能领域汇聚了世界上一些顶尖的企业家、领袖和公司竞相角逐，因为大家都意识到了人工智能的重要性。看到这样的进步让我们感到非常欣慰。
You know, Google is a gigantic company. Can you speak to the natural things that happen in that case is the bureaucracy that emerges like you want to be careful? Like, you know, like the natural kind of there's there's meetings and there's managers and that like what, what are some of the challenges from a leadership perspective breaking through that in order to like you said ship like the number of products. Gemini related products has been shipped over the past years just insane. Right. It is.
你知道，谷歌是一家庞大的公司。在这种情况下，自然会出现官僚主义，需要小心应对。比如说，自然会有很多会议和管理层面的问题。那么，从领导的角度来看，要突破这些挑战有哪些呢？正如你所说的，过去几年与Gemini相关的产品发布数量实在是惊人，对吧？确实如此。
Yeah, exactly. That's what relentless this looks like. I think it's it's a question of like any big company, you know, ends up having a lot of layers of management and things like that is sort of the nature of how it works. But I still operate and I was always operating with old deep mind as a as a startup still large one, but still as a startup. And that's what we still act like today as with Google deep mind and acting with the size of nurse and the energy that you get from the best smaller organizations.
是的，确切地说，这就是不懈努力的样子。我认为这就像任何大型公司一样，最后总是会有很多管理层和其他类似的东西，这是运作的本质。但我一直以来并且现在仍然以一个大型初创公司的方式来运营原来的DeepMind，而现在在Google DeepMind，我们仍然以这种方式运作。我们就像一个大规模的初创公司，保持着小型组织所带来的活力和热情。
And we try to get the best of both worlds where we have this incredible billions of users surfaces and credible products that we can power up with our AI and our and our research. And that's amazing. And you can, you know, that's 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.
我们努力获得两全其美的结果，一方面我们拥有数十亿用户的巨大平台，另一方面我们有优秀的产品，这些都可以通过我们的人工智能和研究来增强。这真的令人惊叹。世界上很少有地方能做到这一点：既能进行世界级的卓越研究，又能将其应用于实践，下一天就能改善数十亿人的生活。这种组合实在是太了不起了。
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, you know, as you have to be as a large company and also with a number of, you know, huge product surfaces that we have.
我们不断地努力应对并削减官僚作风，以便让研究文化和不断推进的产品发布文化得以蓬勃发展。我认为我们在这方面找到了一个不错的平衡，同时也在负责地管理这些事情。毕竟，作为一家大公司，我们必须这样做，特别是考虑到我们拥有众多大型产品平台。
So funny thing you mentioned about like the surface of the billion. I had a conversation with a guy named brilliant guy here at the British Museum called Irvin Finkle. He's a world expert at Kineha Forms, which is a ancient writing on tablets. And he doesn't know about Chad Gbertier Gemini. He doesn't even know anybody. But his first encounter with this AI is AI mode on the tool. He's like, is that what you're talking about? The AI mode.
你提到“十亿的表面”这件事真有趣。我在大英博物馆与一位名叫欧文·芬克尔的厉害人物进行了交谈。他是世界上研究楔形文字（一种古代泥板文字）的专家。他对Chad Gbertier Gemini（注：应为ChatGPT或类似的AI的拼写错误）一无所知，也不认识任何相关的人。但是他第一次接触到这个人工智能是在一个工具的AI模式上，他问：“你是在说这个AI模式吗？”
And then, you know, it's just a reminder that there's a large part of the world that doesn't know about this AI thing. Yeah. I know it's funny because if you live on X and Twitter and I mean, it's sort of at least my feed, it's all AI and there's certain places where, you know, in the valley and certain pockets where everyone's just all they're thinking about is AI. But a lot of the normal world hasn't hasn't come across it yet. But that's a great responsibility to their first interaction. Yeah. The grand scale of the rural India or anywhere across the world. Right. Right. And we want it to be as good as possible. And in a lot of cases, it's just under the hood, powering, making something like maps or search work better. And it's ideally for a lot of those people to just be seamless. It's just new technology that makes their lives more, you know, productive and helps them.
然后，你知道，这让我们意识到，世界上有很大一部分人还不了解这个人工智能的事情。是的，我知道这有点好笑，因为如果你经常用X（推特）之类的平台，至少在我的信息流中，全是关于人工智能的内容。在硅谷和某些地方，大家脑子里全是AI。但对于很多普通人来说，他们还没接触到这个技术。首次接触人工智能是一项重大的责任。对大规模的农村印度或世界其他地方来说，也一样。我们希望这些技术尽可能好地应用。在许多情况下，人工智能在幕后运行，比如让地图或搜索功能更好用。理想情况下，对很多人来说，这项新技术应该是无缝衔接的，它能让他们的生活更高效、更有帮助。
A bunch of folks on the Gemini product and engineering teams spoken extremely highly of you on another dimension that I almost didn't even expect because I kind of think of you as the like deep scientists and caring about these big research scientific questions. But they also said you're a great product guy. Like how to create a thing that a lot of people would use and enjoy using. So can you maybe speak to what it takes to create a AI-based product that a lot of people would enjoy using? Yeah. Well, I mean, again, that comes back from my game design days where I used to design games for millions of gamers. People would forget about that. I've had experience with cutting edge technology in product that is how games was in the 90s. And so I love actually the combination of cutting edge research and then being applied in a product and to power a new experience. And so I think it's the same skill really of, you know, imagining what it would be like to use it viscerally and having good taste coming back to earlier.
在 Gemini 的产品和工程团队中，有一群人对你在另一个方面的表现评价极高，这让我几乎感到意外，因为我一直把你看作是那种专注于重大科学研究问题的深度科学家。但他们还说你也是个优秀的产品专家，知道如何创造一个让很多人都会使用并乐在其中的产品。所以，你能谈谈创造一个受大众喜爱的基于 AI 的产品需要什么吗？
好的，这其实与我过去的游戏设计经历有关，那时候我为数百万玩家设计游戏。人们可能不知道，我在上世纪90年代就已经在产品中应用尖端技术了。所以我很喜欢将尖端研究与产品相结合，从而推动新的体验。我认为这实际上需要相同的技能，就是能够想象使用时的切身感受，并拥有良好的品味，这又回到了之前提到的点。
The same thing that's useful in science, I think is can also be useful in product design. And I've just had a very, you know, always been a sort of multi-disciplinary person. So I don't see the boundaries really between, you know, arts and sciences or product and research. It's a continuum for me. I mean, I only work on, I like working on products that are cutting edge. I wouldn't be able to, you know, have cutting edge technology under the hood. I wouldn't be excited about them if they were just run at the mill products. So it requires this invention creativity capability. What are some specific things you kind of learned about when you, even on the LLM side, you're interacting with Gemini.
我认为在科学中有用的东西，在产品设计中也可能有用。我一直以来都是一个跨学科的人，所以我并不认为艺术和科学或者产品和研究之间有明确的界限。对我来说，这些是一种连续的状态。我只喜欢从事那些前沿的产品。如果没有尖端技术的支持，我对这些普通产品就不会感兴趣。所以，这需要具备创新和创造力的能力。在与Gemini互动时，包括在LLM（大型语言模型）方面，你学到了一些具体的东西吗？
You know, like this doesn't feel like the layout, the interface, maybe the trade-off between the latency, like how, how to present to the user, how long to wait and how that waiting is shown or the reason capability. Is there some interesting things? Because like you said, it's a very cutting edge. We don't know how to present it, how to present it correctly. So is there some specific things you've learned? I mean, it's such a false 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 the what 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. This relentless progress we talked about earlier, you know, you look at 2.5 versus 1.5 and it's just a gigantic improvement.
你知道，这感觉不像是理想的布局和界面，或者在延迟方面的权衡：比如如何展现给用户看，用户等待多久以及这种等待如何表现，或是高阶的功能。这些是否有一些有趣的地方？因为如你所说，这是非常前沿的东西。我们不知道如何呈现它，或者如何正确地展示。所以你有没有学到一些特别的东西？我是说这是一个快速发展的领域，我们一直在评估这个。但在目前的情况下，你想要不断简化东西。不论是界面还是你基于模型构建的东西，你都希望不妨碍模型的发展。模型的发展犹如一列快速行驶的火车，进步速度令人难以置信。我们之前讨论过的这种不断进步，你看 2.5 和 1.5，相比之下就是一个巨大的提升。
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've got a 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've 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 six months or a year's time? So you kind of got to intercept where this highly changing technology is going. As well as the new capabilities are coming online all the time that you didn't realize before that can allow like de-repec search to work.
我们也对未来版本抱有相同的期望。模型的能力正在不断提升。在当今世界设计空间中，AI优先的产品具有一个有趣的特征：你需要设计的不是它目前能做到的事情，而是它在一年后可能实现的功能。因此，你需要成为一个具有很强技术能力的产品人员。你需要具备良好的直觉和判断力，能够预测目前无法实现的想法，在接下来的六个月或一年内，研究进展是否能赶上这些想法。所以你需要预测这个快速变化的技术走向。此外，新功能不断上线，你之前可能未曾注意到的功能现在可以实现，例如使搜索功能更有效的技术。
Or now we've got video generation. What do we do with that? This multimodal stuff, you know, is it one question I have? Is it really going to be the current UI that we have today? These text box chats seems very unlikely. Give it 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 co-in a kind of collaborative way? Right? It seems very restricted to that. I think we'll look back on today's interfaces and products and systems as quite archaic in maybe an 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.
现在，我们有了视频生成技术。我们应该怎么利用它呢？关于这些多模态技术，我有个问题：我们现在的用户界面是否真的合适？用文本框聊天的方式来处理这些超级多模态系统，好像不太可能。你想想看，难道不应该像电影《少数派报告》那样，以一种协作的方式与系统互动吗？现在的方法似乎太受限了。我认为我们会在几年后回头看今天的界面、产品和系统，觉得它们很古老。所以我觉得在产品和研究方面，其实都有很大的创新空间。
And then we're offline talking about the ski board is the open questions. 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, type, 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, you know, maybe a hundred X of what is today? I think that, you know, under appreciated art form is the interface design that I think you can not unlock the power of the intelligence of a system if you don't have the right interface.
然后，在线下我们讨论滑雪板时提到了一些开放性问题：我们何时以及如何转向以音频为主要方式与周围的机器互动，而不是依赖打字？是的，我的意思是，即使你打字速度很快，打字仍然是一种低带宽的交流方式。我认为我们必须开始利用其他设备，无论是智能眼镜、音频耳塞，最终可能还有某种神经设备，以便将输入和输出带宽提升到今天的100倍。我认为，一个被低估的艺术形式是界面设计。如果没有合适的界面，你无法释放系统的智能潜能。
The interface is really the way you unlock its power. Yeah, it's such an interesting question of how to do that. Yeah. So how you would think like getting out of the way is in real art form? 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 that 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, you know, that, can you make it interfaces beautiful as that?
界面确实是释放其强大功能的关键。对，如何做到这一点真是一个有趣的问题。是的，你可能会认为不干扰用户体验是一种真正的艺术形式。是的，你知道，这类似于史蒂夫·乔布斯常提到的理念，对吧？我们追求的是简单、美感和优雅。就我看来，目前还没有人真正达到了那种境界。这是我希望我们能够实现的目标。再次，比喻说，就像围棋这款游戏，是最优雅和美丽的游戏。你能否将界面设计得像围棋一样优美呢？
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, you're aesthetic, you'll 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. Yeah, because some people are power users and they want every single parameter on screen, everything, everything based, like perhaps me with a keyboard, keyboard based navigation, like to have shortcuts for everything. And some people like the minimalism just to provide all of that complexity, yeah, exactly.
实际上，我认为我们即将进入一个由人工智能生成界面的时代，这些界面可能会根据你的个人需要进行定制。它们会符合你的审美，并且符合你大脑的运作方式。人工智能会根据具体任务生成相应的界面，我们最终可能会走向这样的方向。因为有些人是高级用户，他们希望在屏幕上看到每个参数，喜欢用键盘操作，想要为所有功能都设定快捷键。而另一些人则喜欢简约风格，只需展示所有复杂功能中必需的信息。
Yeah. Well, I'm glad you have a Steve Jobs mode in you as well. This is great. Einstein most Steve Jobs mode. All right, let me try to trick you into answering a question. When will Gemini 3 come out? This is 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? This is about a completely different flavor of an experience.
好的。很高兴你也有一些像史蒂夫·乔布斯那样的特质。这太棒了。就像爱因斯坦的变身成乔布斯的状态。好吧，让我试着挑战你回答一个问题。Gemini 3何时会发布？是会在GTA 6之前还是之后发布呢，全世界都在期待这两者。而且，从2.5版本升级到3.0需要些什么？因为2.5版本已经有很多的发布，它们在性能上已经有了很大的飞跃。所以，升级到新版本究竟意味着什么？是关于性能的提升吗？还是一种完全不同的体验风格？
Yeah, well, so the way it works with our different version numbers is we, you know, we try to collect. So maybe it takes, you know, roughly six 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, though, you know, you could imagine the last six 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.
好的，我们的版本编号方式是这样的：我们会尝试收集各种新概念。完成一个完整的新版本及其产品化大约需要六个月时间。在此期间，会有大量有趣的研究迭代和想法涌现。我们会把这些想法集中起来，比如说最近六个月内在架构或数据方面的有趣想法，然后将它们打包，对其中可能有助于下一个迭代的内容进行测试，最后把这些一起整合到新版本中。
And then we start the new, you know, giant hero training run. Right. And then, 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 here. So there's a whole experiment and 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 train model. And then the interior inversions of 2.5, you know, and the different sizes and the different little additions there often patches or post training ideas that can be done afterwards off the same basic architecture.
然后我们开始新的，大型英雄训练。对吧。然后，当然，这个过程会被监控。在训练结束时，会有预训练的部分，然后是后续训练。有很多不同的方法来进行这两个阶段，以及不同的方法来修补这个过程。因此，这是一个完整的实验和阶段，你也可以从中获得很多收益。这通常就是版本号中提到的基础模型和预训练模型所指的地方。而内部的 2.5 版本、不同的规模和一些小的附加项，经常是用于修补或在同一基本架构之后进行的后期训练的想法。
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 promote prioritise performance or speed, right, and cost. And we like to think of this Pareto Frontier of, you know, on the one hand, the y-axis is, you know, like performance. And then the x-axis is, you know, 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.
当然，除此之外，我们还有不同的尺寸，如专业版、闪电版和手电筒版，这些通常是从最大的型号中提取出来的，比如闪电版是从专业版演变而来的。这意味着我们为开发者提供了一系列的选择，您需要决定是优先考虑性能、速度还是成本。我们将这个选择思维为帕累托前沿问题，一方面是y轴，代表性能；另一方面是x轴，代表成本或延迟和速度的综合。我们的模型完全定义了这一前沿，因此无论您作为个人用户还是开发者需要做何种取舍，都可以在我们的模型中找到满足您要求的选择。
So behind diversion changes, there is a big hero run. Yes. 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 each step you take, you realise there might be a cool product, the side quests. Yes. Exactly. And then you also don't want to take too many side quests because then you have a million versions of a million products. Yes. Yes. Sorry. It's very unclear. Yeah. But you also get super excited because it's super cool.
在分散注意力的变化背后，有一个重要的主线任务。是的。然后就是产品化过程的复杂性，这让人无法想象。接着是沿着帕累托前沿对不同尺寸的提炼。在迈出每一步的时候，你会意识到可能会有很酷的产品——一些“支线任务”。对，就是这样。但你也不想做太多支线任务，因为这会导致有上百万种产品的上百万个版本。是的，是的。对不起，这可能不太清楚。不过你也会感到非常兴奋，因为这实在是太酷了。
Yeah. Like how does even look at VL's very cool. How does it fit into the bigger thing? Exactly. Exactly. And then you 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. So then the main model, the main Gemini track, becomes more and more general and eventually, you know, AGI.
好的。像这样，它在VL的整体运作中看起来非常酷。那么，它如何融入更大的整体呢？没错，没错。然后你会不断经历这种从上游收敛的过程，我们称之为这种过程。无论是来自产品表面的想法，还是培训后的反馈，甚至比这更下游的东西，你都将其引入核心模型的训练，为下一次运行做准备。这样的话，主要的模型，也就是主要的Gemini路线，将会变得越来越通用，最终可能实现通用人工智能（AGI）。
One hero run. Yes. Exactly. Few hero runs later. Yeah. So sometimes when you release these new versions or every version, really, a benchmark's productive or counterproductive for showing the performance of a model. You need them. And I bet it's important that you don't overfit to them, right? So there shouldn't be the end with a be all and end all. So there's LM Arena or used to be called a LEMSIS that's one of them that turned out sort of organically to be one of the main ways people like to test these systems, at least the chat bots.
一个英雄的运行。是的，正是如此。几次英雄运行之后。对。所以，有时候，当你发布这些新版本的时候，或者实际上每个版本，一项基准测试可能对展示模型的性能是有帮助的，也可能是无益的。你需要这些测试。我敢打赌，确保你不过度依赖这些测试很重要，对吧？所以不应该把它们当作最终的标准。有一个被称为"LM Arena"的平台，之前叫做"LEMSIS"，它自然成为人们喜欢测试这些系统（至少是聊天机器人）的主要方法之一。
Obviously, there's loads of academic benchmarks on from the 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, you know, optimization problem, right? You know, 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. So where you improve in like, you know, coding, but it doesn't reduce your performance in other areas, right?
显然，在测试中有许多学术基准，用来衡量数学、编码能力、语言能力、科学能力等多个方面。除此之外，我们还有自己关注的内部基准。这实际上是一种多目标优化问题。我们不希望只擅长一个领域，而是希望构建在各方面都表现良好的通用系统。我们的目标是进行“无遗憾”改进，比如提高编码能力的同时，不降低其他领域的表现。
So that's the hard part because you can, of course, you could 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 in your translation systems and other things that you care about? So it's, you've got to kind of continually monitor this increasingly larger and larger suite of benchmarks. And also, there's, 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, or the average person using using the chat interfaces.
这就是难点所在，因为你当然可以加入更多的编码数据或者游戏数据，但这会不会影响你的语言系统、翻译系统和其他你关心的功能呢？因此，你需要不断监控这些越来越多的基准测试。此外，当你将这些模型应用到产品中时，你还需要关注直接的使用情况和从终端用户——无论是程序员还是使用聊天界面的普通人——那里获得的直接统计和反馈信号。
Yeah, because ultimately you want to measure the usefulness, 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 I'll, it will be just terrifying to me to, you know, you have a much smarter model, but it's just something vibe based. It's not, not, not quite working. That's just scary 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 a sudden solving programming problems.
好的，因为最终你是想评估其有用性，但要把这个转化成一个数字实在是太难了。对吧。更多的是一种基于直觉的标准，涉及大量用户。而这个确实很难掌握。而且，我会感到非常害怕，因为尽管你拥有一个更智能的模型，但它只是基于直觉的东西，没能完全发挥作用。这让人感到害怕，因为你说的一切都必须在许多领域中既聪明又有用。你会因为它突然解决了编程问题而感到超级激动。
It never been able to solve before, but now it's crappy of poetry or something. And it's just, I don't know, that's a stressful, it's so difficult to balance. Yeah, to balance and because you can't really trust the benchmarks, you really have to trust the end users. Yeah. And then other things that are even more so Terry come into play like, you know, the style of the persona of the, the, the system, you know, how it, you know, is it verbose, is it succinct, is it humorous, you know, and different people like different things.
以前从未解决过的问题，但现在这个东西变得像垃圾诗一样。我不知道，这让我感到压力很大，很难平衡。是的，需要平衡，因为你不能完全信任那些基准测试，你真的需要信任最终用户。还有其他更重要的因素，比如系统的风格和个性，它是啰嗦的还是简洁的，是幽默的还是严肃的，不同的人有不同的喜好。
So, you know, it's very interesting. It's almost like cutting edge part of psychology research or personal personality research. You know, I used to do that in my PhD, like, five factor personality. What do we actually want our systems to be like? And different people will like different things as well. So these are all just sort of new problems in product space. I don't think I've ever really been tackled before, but we're going to sort of happily have to deal with now. I think it's a super fascinating space developing the character of the thing. Yeah. 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?
你知道吗，这非常有趣。这几乎是心理学研究或个性研究的前沿领域。我以前在攻读博士学位时研究过五因素人格理论。我们究竟希望系统是什么样子的呢？不同的人会喜欢不同的东西。这些都是产品领域的新问题，我觉得以前几乎没人真正解决过，但现在我们高兴地面对这些挑战。我认为这是一个非常迷人的领域，发展事物的个性特征。在此过程中，这也反映了我们自己：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，但是产品能否让你更容易地控制不同体验的风格和你所互动的角色特征呢？
Yeah, exactly. So, so what's the probability of Google did mind winning? Well, I don't see it sort of winning. I mean, I think we need to, I think winning is the wrong way to look at it, given how important and consequential what it is we're building. So, finally enough, I don't, 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 have those of us at the leading edge of our 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.
好的，确实如此。那么，Google获得成功的概率有多大呢？我并不认为这是种意义上的“获胜”。我觉得我们需要重新看待这个问题，因为我们正在开发的技术非常重要且意义非凡。因此，我并不把这视为一场游戏或竞争，尽管这常常是我的思维方式。在我看来，我们这些位于前沿的人有责任引导这种难以置信的技术，它可以被用于巨大的善行，但也存在风险。我们要安全地将其带入世界，为人类造福。这一直是我的梦想，也是我们一直努力的方向。我希望，当我们越来越接近人工智能通用化（AGI）时，国际社会能够意识到这点，并共同努力。
I agree with you. I think that's beautifully put. You've said that you talked to and are in good terms with the leads of some of these labs as the competition heats up. How hard is it to maintain those relationships? It's been okay. So, if I try to pry myself in being collaborative, I'm a collaborative person. Research is a collaborative endeavor. Science is a collaborative endeavor. It's all good for humanity in the end. If you cure incredible, you know, terrible diseases and you can't with an incredible cure, this is net win for humanity. And the same with energy, all of the things that I'm interested in helping solve with AGI. So, I just want that technology to exist in the world and be used for the right things and the kind of the benefits of that, the productivity benefits of that, being shared for the benefit of everyone. So, I try to maintain good relations with all the leading lab. People, they're very interested in characters. Many of them, as you might expect. But yeah, I'm in good terms.
我同意你的观点。我觉得你说得非常好。你提到过你与一些实验室的负责人进行了交谈，并且在竞争日益激烈的情况下，你们的关系保持得很好。这种关系维持起来有多难呢？目前还不错。我努力保持协作精神，因为我就是一个喜欢合作的人。研究需要合作，科学需要合作，最终这对人类有好处。如果你能找到治疗可怕疾病的突破性疗法，那对人类就是一个巨大的胜利。对于能源也是一样，我对解决这些问题抱有兴趣，尤其是通过人工智能技术。我希望这些技术能在世界上存在，并被用于正确的事物，其生产力的好处能为每个人共享。因此，我努力与主要实验室保持良好关系。大家都是很有趣的角色，很多情况下如你所料。不过，目前我们的关系还是不错的。
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's what we're required, especially on things like safety. Yeah, I hope there's some collaboration and stuff that's sort of less high stakes. And in so doing services and mechanism for maintaining friendships and relationships. So, for example, I think the internet would love it if you and Elon some Hulk elaborate on creating a video game, that kind of thing. I think that enables camaraderie and good terms. And also you tour the gig game or so it's just fun to find a place.
我希望能与几乎所有人合作。我觉得这是非常重要的，尤其是在事情比现在更严重的时候。有这样的沟通渠道非常关键，因为它能促进合作。如果我们需要合作，特别是在安全等重要事项上。是的，我希望能有一些合作，甚至在那些没有那么高风险的事情上。在这个过程中，这种服务和机制有助于维持友谊和人际关系。例如，我觉得如果你和Elon共同创造一个视频游戏，互联网会非常喜欢。这种合作能够促进友情和良好的关系。而且如果你巡演这个游戏或者类似的项目，这会很有趣，找到一个共同的地方。
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, there'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's kind of builds up that collaborative muscle. I see the scientific endeavor as that kind of side project for humanity. Yeah. And I think Google DeepMind has been really pushing that. I would love it if to see other labs do more scientific stuff and then collaborate because it just seems like easier to collaborate on the big scientific questions.
是的，那会很棒。我们过去也讨论过这个问题。或许这真是我们可以做的一件很酷的事情。我同意你的看法，开展一些侧重于合作的项目会很不错，这样人们就可以专注于合作的方面。这对双方来说都是双赢的，也有助于培养合作的能力。我认为科学探究就是人类这样的一个“副项目”。我认为谷歌DeepMind在这方面做了很大的推动。我希望能看到其他实验室做更多科学研究，然后进行合作，因为在一些重大的科学问题上合作似乎要容易些。
I agree. And I would love to see a lot of people, a lot of 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 Alpha Fold 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 Alpha Fold to do drug discovery and it's going really well and build sort of, you know, you can think of build additional Alpha Fold type systems. So going to chemistry space to help accelerate drug design. And the examples I think we need to show and society needs to understand a well AI can bring these huge benefits.
我同意。我非常希望看到许多人和其他实验室讨论科学。不过，我认为我们是唯一真正将其用于科学并取得成果的团队。这也是为什么像 Alpha Fold 这样的项目对我意义重大。我们的使命是展示人工智能如何以一种非常具体的方式为人类造福。我们还从 Alpha Fold 项目中衍生出像 Isomorphic 这样的公司，用于药物研发，目前进展非常顺利。可以想象，我们正在建立类似 Alpha Fold 的额外系统，进入化学领域，加速药物设计。我认为，我们需要展示这些例子，让社会理解人工智能可以带来的巨大益处。
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 it and still talking about PE equals that. I mean, it's just incredible. So I love it. 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 deep minds are really great place to do cutting edge work for the reasons that you've outlined is like there's this vibrant scientific culture.
从心底里感谢你们以严谨、趣味和谦逊的态度推动科学研究的努力。我真的很喜欢看到这一点，尤其是我们仍在谈论类似“PE”等问题，真的很棒。我喜欢这种氛围。最近似乎出现了一场人才争夺战，有些只是玩笑，我不太清楚。你怎么看待Meta公司用高薪吸引人才，以及这场人才之战的加剧？我还想说的是，许多人认为DeepMind是从事尖端研究的绝佳场所，正如你所描述的那样，因为那里有活跃的科学文化。
Yeah, well look, I of course you know, 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's what that responsibility entails. I think they're mostly doing it to be like myself to be on the frontier of that research. So you know, they can help influence the way that goes and steward that technology safely into the world. And you know, meta right now are not at the frontier. Maybe they'll manage to get back on there.
好的，听我说。你知道，Meta现在正在采取某种策略。至少从我的角度来看，我认为那些真正相信通用人工智能（AGI）使命的人，了解其中的利与弊，并理解所承担的责任。他们大多是为了站在这项研究的前沿，像我一样，希望影响这项技术的发展方向，并确保其安全引入世界。目前，Meta并不在这个前沿，但也许他们将来会设法重新回到那里。
And you know, 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's more important things than just money. Of course, one has to pay, you know, people their market rates and all of these things. And that continues to go up. But as prop and 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.
你知道，从他们的角度来看，他们所做的事情可能是理性的，因为他们处于落后状态，需要采取行动。但是，我认为有比金钱更重要的事情。当然，人们需要支付员工的市场工资，而这样的费用在不断上升。我预料到这一点，因为越来越多公司的领导者终于意识到，我在过去30多年里一直知道的一件事，那就是通用人工智能 (AGI) 可能是有史以来最重要的技术。所以从某种意义上来说，他们这样做是有道理的。
But I also think there's a much bigger question. I mean, people in AI these days are very well paid. You know, I remember when we were starting out back in 2010, you know, I didn't even pay myself for a couple of years because it was enough money. We couldn't raise money. And these days interns are being paid, you know, the amount that we raised as our first entire sea round. So it's pretty funny. And I remember the days were we 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.
但我认为还有一个更大的问题。现在，人工智能领域的人薪酬都很高。我记得我们在2010年刚开始的时候，甚至有好几年都没给自己发工资，因为资金不足，无法筹集到资金。而现在，实习生的薪水相当于我们当初整个种子轮融资的金额。这真是挺有趣的。我还记得那时候我们得免费工作，几乎是自费去实习。如今一切都反过来了。不过这就是现在的新世界。
And but I think that, you know, 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, you know, in the economy and we're going to have much bigger issues to work through. And how does the economy function in that world and companies? So I think, you know, it's a little bit of a side issue about salaries and things of like that today. Yeah. When you're facing such gigantic consequences and gigantic fascinating scientific questions, which maybe are only a few years away.
我认为，你知道，我们一直在讨论人工智能发展到通用人工智能（AGI）之后会发生什么，比如能源系统的问题得到解决等。在那样的情况下，金钱的意义会是什么？所以我认为，在经济领域，我们将有更大、更重要的问题需要解决。在这样的世界里，经济和公司将如何运作？因此，我觉得现在讨论工资之类的问题有点次要。当我们面临如此巨大的后果和可能只有几年之遥的极具吸引力的科学问题时，这些问题显得不那么重要。
So on the practical sort of pragmatic sense for zooming on jobs, we can look at programmers because it seems like AI systems are currently doing incredibly well at 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 sort of adjust to the new reality and ensure that you survive and thrive as a human in the programming world?
从实用和务实的角度来看，我们可以关注程序员，因为目前人工智能系统在编程方面表现得非常出色，而且这种趋势还在加强。因此，很多以编程为生并热爱编程的人担心他们会失去工作。对此你认为他们应该多担心？为了适应新的现实，以确保你作为一个人在编程世界中生存和发展，正确的方法是什么？
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 are turned out maybe to be the easier ones for various reasons. But, you know, coding a maths because you can create a lot of synthetic data and verify if that data is correct. So because of that nature of that, 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.
编程这件事很有趣，现在看来，一些我们过去认为很难的技能，可能实际上反而是比较容易的。比如编码和数学，因为我们可以创造很多合成数据并验证这些数据是否正确。所以因为这个特点，我们更容易制作合成数据来进行训练。此外，编程也是我们大家都很感兴趣的领域，因为作为程序员，我们都希望通过这些手段来提高速度和效率。
So I think for the next era, like the next 5, 10 years, I think what we're going to find is people who are kind of embrace these technologies become almost at one with them, whether that's in the creative industries or the technical industries will become superhumanly productive, I think. So the great programmers will be even better, but they'll be even 10X even what they are today. And because there you'll be able to use their skills to utilize the tools to the maximum, exploit them to the maximum.
所以我认为在未来的5到10年里，我们会发现那些接受这些技术并与之融为一体的人，无论是在创意行业还是技术行业，都会变得非常高效。我认为，优秀的程序员会变得更出色，他们的效率甚至将是现在的10倍。因为他们可以最大限度地运用这些工具，充分发挥它们的潜力。
And so I think that's what we're going to see in the next domain. So that's going to cause quite a lot of change, right? And so that's coming. A lot of people benefit from that. So I think one example of that is if coding becomes easier, it becomes available to many more creatives to do more. And but I think the top programmers will still have huge advantages as terms of specifying, going back to specifying what the architecture should be. The question should be how to guide these coding assistants in a way that's useful, you know, check whether the code they produce is good. So I think there's plenty of headroom there for the foreseeable, you know, next few years.
我认为在下一个领域中，我们将看到这种变化。这将引起相当大的变化，对吧？而且这即将到来，会有很多人从中受益。举个例子，如果编程变得更简单，将会有更多的创意人士能够参与其中，做出更多的事情。然而，我认为顶尖的程序员仍然会有巨大的优势，他们能更好地指定架构。关键在于如何有效引导这些编程助手，确保它们生成的代码是好的。我认为在今后几年内，这方面还有很大的发展空间。
So I think there's several interesting things there. One is there's a lot of imperative to just get better and better consistently of using these tools. So they're regretting the way of the improvement improving models versus like competing against them. But sadly, but 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, it could be like, you know, front and web design might be more amenable to, to, to, as you mentioned, to generation by AI systems and maybe, for example, game engine design or something like this or backhand designers or guiding systems in high performance situations, high performance programming type of design decisions that might be extremely valuable.
我认为这里有几个有趣的点。首先，人们迫切希望能够持续改进使用这些工具的能力。因此，他们的关注点在于如何提升模型的能力，而不是与这些模型竞争。但遗憾的是，这就是地球生命的本质。在某些前沿领域的编程可能会有巨大的价值，而其他领域的价值可能较低。例如，前端网页设计可能更容易被AI系统取代，而对于游戏引擎设计、后台设计或高性能编程中需要做出的设计决策，这些可能具有极高的价值。
But it will shift where the humans are needed most and that's scary for people to access. Yeah, I can't, I think that's right. Any time where there's a lot of disruption and change, you know, we've had this is not just this time, we've had this in many times in human history with the internet mobile, but before that, we see industrial revolution. And it's going to be one of those areas 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. And then those people with the right skill sets to write that wave will become incredibly valuable, right, those skills. But maybe people will have to relearn or adapt a bit their current skills.
这会改变人类在工作中的需求重点，这对人们来说有些可怕。是的，我觉得这是对的。每当发生重大变化和扰动的时候，比如这次，不仅仅是这次，在人类历史上多次发生过这样的情况。互联网、移动技术的发展如此，在这之前还有工业革命，这些都是改变巨大的时期。我认为会出现一些我们现在无法想象的新工作，就像互联网创造的新机会一样。那些拥有合适技能的人将会变得非常有价值，因为他们能适应这波潮流的变化。但是，可能人们需要重新学习，或者稍微调整一下自己现有的技能。
And it's the thing that's going to be harder to deal with this time around is that I think what we're going to see is something like probably 10 times the impact the industrial revolution had. And but 10 times faster as well. Right. So instead of a hundred years, it takes 10 years. And so that's going to make, you know, it's like a hundred X, the impact and the speed combined. So that's what's I think going to make it more difficult for society to deal with. And it's good. There's a lot to think through and I think we need to be discussing that right now.
这次更难以应对的一点是，我认为我们将看到的影响可能是工业革命的十倍，并且速度也要快十倍。也就是说，这个变化将不是需要一百年，而是只要十年。所以，这代表影响和速度的结合将是之前的百倍。我认为这就是为什么社会应对起来会更困难的原因。而这实际上是件好事，因为这需要我们多方面的思考，我认为我们现在就应该开始讨论这个问题。
And I, you know, I encourage top economists in the world and philosophers to start thinking about how should society going to be affected by this and what should we do, including things like, you know, a universal basic provision or something like that where a lot of the increased productivity gets shared out and distributed to society and maybe in the form of surface services and other things where if you want more than that, you still go and get some incredibly rare skills and things like that and make yourself unique.
我鼓励世界顶尖的经济学家和哲学家开始思考：这种情况会如何影响社会，我们应该采取什么措施。比如，考虑实施一种全民基本保障或类似的措施，把生产力的增长成果分配到社会中。这样，大家可以通过基本服务等方式共享。如果个别人想要获得更多，就需要培养一些特别稀有的技能，使自己变得独特。
But, but there's a basic provision that is provided. And if you think of government as a technology, there's also interesting questions, not just the economics, but just politics. How do you design a system that's responding to the rapidly changing times such that you can represent the different pain that people feel from the 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 in a way that doesn't lead to division because politicians are often really good at sort of fueling the division and using that to get elected the other defining the other and then saying that's bad and sort of based on that.
但是，政府提供了一项基本的保障。如果你把政府看作一种技术，那么不仅有经济层面的问题，还有政治层面的问题。我们该如何设计一个能够应对快速变化时代的系统，以便能够代表不同群体所感受到的各种痛苦？又如何重新分配资源，以应对这些痛苦，同时还能代表不同人的希望、痛苦和恐惧，而不导致分裂？因为政客们通常很擅长制造分裂，并利用这点来竞选，经常通过将对手标签化并将其贬低为“坏的一方”来达到目的。
I think that's often counterproductive to leveraging a rapidly changing technology, how 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. Definitely. And I think we'll need new governance structures, institutions probably to help with this transition. So I think political philosophy and political science is going to be key to that.
我认为这样做常常会对利用快速变化的技术产生反效果，以及如何帮助世界繁荣。因此，我们几乎也需要快速改进我们的政治体系。如果你把它们看作一种技术的话。我肯定地认为，为了应对这一转变，我们很可能需要新的治理结构和机构。因此，我认为政治哲学和政治科学在这方面将发挥关键作用。
But I think the number one thing, first of all, is to create more abundance of resources. So that's the number one thing. Increased productivity, get more resources, maybe eventually get out of the zero sum situation. Then the second question is how to use those resources and distribute those resources. But yeah, you can't do that without having that abundance first.
首先，我认为最重要的事情是创造更多的资源丰富性。这是首要任务。提高生产力，获得更多资源，可能最终摆脱零和状态。然后，第二个问题才是如何使用和分配这些资源。不过，是的，没有这些资源的充裕性，后面的事情就无法实现。
You mentioned to me the book, The Maniac by Benjamin Lovatoot, a book on, first of all, about you. There's a bio about you. It's strange. Yeah, it's unclear. Yes, sir. It's unclear how much is fiction, how much is reality. But I think the central figure that is John Vaughn Neumann, I would say it's a haunting and beautiful exploration of madness and genius. And let's say the double edged sword of discovery. And for people who don't know, John Vaughn Neumann is a kind of legendary mind. He contributed to quantum mechanics. He was on the Manhattan Project. He is widely considered to be the father of over pioneer the modern computer and AI and so on. So there's many people say he's like one of the smartest humans ever. So it's just fascinating.
你跟我提到过一本书，《疯子》（The Maniac），作者是Benjamin Lovatoot。这本书首先是关于你的。书中包含了一部分关于你的传记。真是奇怪。不过，是的，很难说清楚书里有多少是虚构的，有多少是真实的。我认为书中的核心人物是约翰·冯·诺依曼，这本书对疯狂与天才进行了深刻而美丽的探讨，也可以说是对发现这把双刃剑的诠释。对于不了解的人来说，约翰·冯·诺依曼是一个传奇性的思想家。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人工智能的奠基人之一。因此，很多人认为他是有史以来最聪明的人之一。这真是令人着迷。
And what's also fascinating is as a person who saw nuclear science and physics become the atomic bomb. So you got to see ideas become a thing that has a huge amount of impact on the world. He also foresaw the same thing for computing. And that's a little bit, again, beautiful and haunting aspect of the book, then taking a leap forward and looking at this at least at all Alpha Zero, Alpha Go Alpha Zero big moment that maybe John Vaughn 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 Vaughn Neumann now? What would he say about what's going on?
这段话的意思是：令人着迷的是，有些人亲眼目睹了核科学和物理学的发展，并最终成为原子弹。他们见证了某些想法变成对世界有巨大影响的事物。此外，这位科学家还预见到计算机领域可能会出现类似的变革。这种预测能力是书中既美丽又让人心生敬畏的部分。举个例子，像AlphaZero和AlphaGo这样的里程碑时刻，也许实现了约翰·冯·诺依曼（John Von Neumann）当初的设想。那么，问题是如果你现在能和约翰·冯·诺依曼一起交流，他会怎么看待当前的发展？
Well, that would be an amazing experience. He's a fantastic mind. And I also love the way he spent a lot of his time at Princeton at the Institute of Advanced Studies, a very special place for thinking. And it's amazing how much of a polymath he was in the spread of things he helped invent, including, of course, the Vaughn Neumann architecture that all the modern computers are based on. And he had amazing foresight. I think he would have loved where we are today. And he would have, I think he would have really enjoyed Alpha Go being a game theory. I think he foresaw a lot of what would happen with learning machines, systems that kind of grown, I think he called it rather than programmed. I'm not sure how even maybe he wouldn't even be that surprised. There's the fruition of what I think he already foresaw in the 1950s.
那将会是一次令人惊叹的经历。他是一个了不起的头脑。我也很欣赏他在普林斯顿高级研究院度过的大量时间，那是一个非常特殊的思考之地。令人惊讶的是，他在很多领域都是博学多才的人，包括他帮助发明的冯·诺依曼架构，所有现代计算机都建立在此之上。他有着非凡的远见。我认为他会喜欢我们今天所处的时代，他也会很欣赏Alpha Go作为博弈论的一部分。我觉得他预见到了很多机器学习的发展，即那些“成长出来”的系统，而不是简单通过编程构建的系统。我不确定他是否会对此感到惊讶，因为我觉得这只是他在20世纪50年代已预见的发展结果。
I wonder what advice he would give. He got to see the building of the atomic bomb with them ahead in project. Yeah, I'm sure there's interesting stuff that maybe he's not talked about enough. Maybe some bureaucratic aspect, maybe the influence of politicians, maybe maybe not enough of picking up the phone and talking to people that are called enemies by the said politicians. There might be some like deep wisdom that we just may have lost from that time actually. Yeah, I'm sure there is. I mean, I've sure we, you know, studied, I read a lot of books for that time as a well-cronical time. And some brilliant people involved. I agree with you. I think maybe there needs to be more dialogue and understanding.
我想知道他会给出什么建议。他亲眼见证了原子弹的建造，那时候他们在项目中处于领先地位。是的，我相信其中有些有趣的东西可能他没有足够讲述。也许是一些官僚方面的事情，或者是政治家的影响，也许是因为政治家把某些人称作敌人而没有足够多的交流。我们可能失去了来自那个时代的一些深刻智慧。是的，我相信是这样。我也读了很多关于那个时代的书，那是一个被详细记录的时代。有很多杰出的人参与其中。我同意你的看法，我觉得可能需要更多的对话和理解。
I hope we can learn from those times. I think the difference here is that the AI has so many, it's a multi-use technology. Obviously, we're trying to do things. I like that that solve, you know, all diseases, help with energy and scarcity. These incredible things. This is why all of us and myself, you know, I worked started on this journey 30 plus years ago. But of course, there are risks too. And probably, Von Neumann, my guess is he foresaw both. And I think he sort of said, I think he is to his wife that it would be, this is, the computers would be even more impactful in the world.
我希望我们能从那些时刻中学习。我认为这里的不同之处在于，人工智能是一种多用途的技术，具有许多应用场景。显然，我们正在努力做的事情，比如解决所有疾病问题、帮助能源和资源短缺。这些都是令人难以置信的事情。这就是为什么我们所有人，包括我自己，30多年前就开始了这段旅程。当然，也存在风险。可能，冯·诺依曼（Von Neumann）在某种程度上预见到了这两者。我觉得他曾对妻子说，计算机将在世界上产生更大的影响。
And as we just discussed, you know, 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. So I think he would have been, I imagine, fascinated by where we are now. And I think one of the, maybe you can correct me, but one of the takeaways from the book is that reason has said in the book, mad dreams of reason, it's not enough for guiding humanity as we build these super powerful technology that there's something else. I mean, there's also like a religious component, whatever God, whatever religion gives, it pulls it something in the human spirit that raw, cold reason doesn't give us.
正如我们刚刚讨论过的，我认为你是对的。我觉得这将至少是工业革命影响的十倍。所以我认为他是对的。我想象他会对我们现在所处的时代感到非常着迷。而且，我想，或许可以纠正我，但从那本书中得出的一个结论是，书中所提到的“理性的疯狂梦想”，仅靠理性是不足以在我们构建这些超级强大的技术时引导人类的。还需要其他东西，比如说一种宗教成分。不论是上帝还是其他宗教所给予的，它赋予了人类精神中一些原始、冷静的理性所不能给予的东西。
And I agree with that. I think we need to approach it with whatever you want to call it, the spiritual dimension or humanist dimension 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 and technology. I've always seen technology as the enabler, right? The tools that enable us to flourish and to understand more about the world. And I'm sort of with Feynman on this. And he used to always talk about science and art being companions.
我同意这一点。我认为我们需要用一种所谓的精神层面或人文层面的方法来对待它，而不一定与宗教有关。没错。这个关于灵魂的概念，也就是让我们成为人类的火花，也许与意识有关，当我们最终理解它时。我认为这必须是我们努力和技术的核心。我一直把技术看作一种支持工具，对吧？是能够让我们繁荣发展并更好了解世界的工具。我有一点像费曼的观点，他常常谈论科学与艺术是密不可分的伴侣。
Right. 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 evolve like that. Right. That just makes it more beautiful, that just the intrinsic beauty of the flower. And I've always sort of seen it like that. And maybe in the Renaissance times, the great discoverers then, like people like Da Vinci, you know, I don't think he saw any difference between science and art and perhaps religion. Right. Everything was, it's just part of being human and being inspired about the world around us. And that's what I, the philosophy I try to take.
好的。你可以从两个角度理解这个问题：一方面是花的美丽，它本身就非常美。另一方面是理解花为何会演变出这样的颜色。理解这些只会让花更加美丽，而不仅仅是花的内在美。我一直是这样看的。也许在文艺复兴时期，像达·芬奇这样的伟大发现者，并不觉得科学、艺术和宗教有任何区别。对他们而言，一切都是人之为人的一部分，都是对周围世界的灵感来源。这也是我试图秉持的一种哲学观。
And one of my favorite philosophers is Spinoza. And I think he combined that all very well, you know, this idea of trying to understand the universe and understanding our place in it. And that was his kind of way of understanding religion. And I think that's quite beautiful. And for me, all of these things are related into related, the technology. And what it means to be human. And I think it's very important, though, that we remember that as when we're immersed in the technology and the research, I think a lot of researchers that I see in our field are a little bit too narrow and only understand the technology.
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些思想，比如努力理解宇宙以及我们在宇宙中的位置。而这正是他理解宗教的一种方式。我觉得这很美。在我看来，这些理念与科技和人类本质息息相关。但我认为，当我们沉浸在科技和研究中时，非常重要的一点是要记得这一点。我注意到，在我们领域中，许多研究人员的视野有些过于狭窄，他们只懂得技术。
And I think also that's why it's important for this to be debated by society at large. And I'm very supportive of things like the AI summits that will happen and governments understanding it. And I think that's one good thing about the chatboard error and the product error of AI is that every day person can actually feel and interact with cutting AI and feel free of it for themselves. Yeah, because they force the technologist to have the human conversation.
我认为这也是为什么让整个社会参与讨论是重要的原因。我非常支持像即将举行的人工智能峰会这样的活动，以及政府对人工智能的理解。我认为关于AI的一个优点是，普通人可以每天亲身接触和体验最前沿的AI技术，从中获得自由的感受。因为这迫使技术人员进行人性化的对话。
Yeah, for sure. That's the whole aspect of it. Like you said, it's a dual use technology that we're forcefully integrating the entire humanity into it by into the discussion about AI. Because ultimately AI, AGI will be used for things that states use technologists for, which is conflict and so on. And the more we integrate humans into this picture by having some chats with them, the more we will guide.
当然，这就是整个问题所在。正如你所说，这是一种双重用途的技术，我们正努力将全人类引入关于人工智能的讨论中。因为最终，人工智能和通用人工智能（AGI）将会被用于国家通常用技术解决的事情，比如冲突等等。我们越多地通过交流将人类融入这个领域，就能更好地引导方向。
Yeah, 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 in the past. Do you think there would be something like a Manhattan project where there will be an escalation of the power of the technology in states in their old way of thinking we'll try to use it as weapon technologies and there will be this kind of escalation? I hope not. I think that would be very dangerous to do.
是的，社会能够适应这些技术。就像我们过去发明的那些不可思议的技术一样，我们总是能适应。你认为会不会出现类似曼哈顿计划的情况，各国在旧有思维模式的驱动下，尝试将这些技术作为武器，从而导致一系列的升级？我希望不会出现这种情况，我认为这样做会非常危险。
And I think also not the right use of the technology. I hope we'll end up with more collaborative if needed, like more like a certain project, you know, 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 you don't like deploying it to the world. We'll see. I mean, it's difficult with the current geopolitical climate, I think, to see cooperation, but things can change.
我也认为这不是技术的正确使用方式。我希望如果有需要，我们可以更具合作性，就像某个项目那样，专注于研究，聚集世界上最优秀的头脑，谨慎地完成最后步骤，并确保在部署到全球之前是负责任地进行的。我们拭目以待。我觉得在目前的地缘政治环境下，看到合作是不容易的，但事情是可能改变的。
And 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 on those kinds of topics. Yeah, and I personally believe on the education side and immigration side, it would be great if both directions are people from the West, immigrated China and China back. I mean, there is some like family human aspect of people just intermixing.
我认为至少在科学层面上，研究人员之间保持联系和紧密合作是很重要的。在教育和移民方面，我个人认为东西方之间的人员双向流动将会非常有益，例如西方的人移居到中国，中国的人移居到西方。这样的人口交流涵盖了一定的人文和家庭层面的意义。
Yeah. And thereby those ties grow strong so you can't sort of divide against each other at this kind of old school way of thinking. And so multi-cultural, multidisciplinary research teams working on scientific questions that's like the hope. Don't let the warm leaders that are warm on girls because they divide us. I think science is the ultimately really beautiful connector.
好的。在这样的背景下，这些联系变得更加紧密，因此你无法通过过去那种老旧的思维方式来互相分裂。因此，多元文化、多学科的研究团队共同解决科学问题，这就是我们的希望。不要让那些对女性怀有偏见的领导者来分裂我们。我认为科学最终是一个非常美好的纽带。
Yeah, science has always been I think quite a very collaborative endeavor. And you know, science is known 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-dume? Probability of the human civilization destroys itself.
是的，我一直认为科学是一项非常合作的事业。你知道，科学被认为是一个集体的努力，我们都可以相互学习。因此，科学可能成为促进合作的一种途径。你有什么荒唐的问题呢？你对人类文明自我毁灭的概率有什么看法？
Well, look, I don't have a p-dume 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-dume numbers. I think it's a kind of a little bit of a ridiculous notion because what I would say is it's definitely non-zero and it's probably non-negligible. So that in itself is pretty sobering. And my view is it's just hugely uncertain.
好吧，你看，我没有一个所谓的“p-dume”数字。我之所以没有，是因为我认为制定这么一个数字会给人一种精确度很高的错觉，但实际上并不是这样。我不知道人们是如何得到他们的“p-dume”数字的。我觉得这个概念有点荒谬，因为我会说，这个数字肯定不是零，而且可能也不容忽视。这本身就足够引人深思了。在我看来，这个问题充满了巨大的不确定性。
Right. What these technologies are going to be able to do, how fast are they going to take off, how controllable they're going to be. Some things may turn out to be and hopefully like way easier than we thought. Right. But it may be there some really hard problems that are harder than we guess today. And I think we don't know that for sure. And so in under those conditions of a lot of uncertainty but huge stakes both ways. You know, on the one hand, we could solve all diseases, energy problems, the scarcity problem and then travel to the stars and consciousness of the stars and maximum human flourishing. On the other hand is this sort of p-dume scenarios.
好的。这些技术将能够实现什么，它们的发展速度有多快，以及它们的可控性如何。这些都是我们关心的问题。有些事情可能会比我们想象的更简单，这是我们所希望的。但也可能存在一些非常困难的问题，比我们今天猜想的更难。我认为我们对此不能确定。在这种不确定性很大的情况下，两种结果都可能带来巨大影响。一方面，我们可能会解决所有疾病、能源问题、资源稀缺问题，然后实现星际旅行，甚至达到星际意识，这将极大地促进人类的发展。另一方面，也存在一些可能导致消极结局的情景。
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 of the amazing things that AI can bring. And actually I would be really worried for humanity if I if given the other challenges that we have, climber, dizzy, 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 we could be amazingly transformative for good.
考虑到围绕人工智能的不确定性及其重要性，对我来说，唯一合乎理性的明智方法是以谨慎的乐观态度推进。我们期望得到结果和利益，当然也包括人工智能能带来的各种惊奇事物。实际上，面对我们现在所面临的其他挑战，如气候变化、疾病、人口老龄化、资源匮乏等等，如果没有像人工智能这样的技术正在发展，我会对人类感到非常担忧。对吧？我们要如何解决所有这些其他问题呢？我认为这很难。所以我认为人工智能可能在好的方面具有惊人的变革潜力。
But on the other hand, there are these risks that we know are there but we can't quite quantify. So the best things 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 they probably needs to be 10 times more effort of that than there is now as we're getting closer and closer to the to the AGI line. Will be the source of worry for you more. Would it be human caused or AI, AGI caused?
但是另一方面，有一些我们知道存在但无法精确量化的风险。因此，最好的办法就是运用科学方法进行更多的研究，尝试更精确地定义这些风险，并且当然要去解决它们。我认为这正是我们现在正在做的事情。不过，我觉得我们需要在这方面投入比现在多十倍的努力，因为我们越来越接近通用人工智能（AGI）这条界线。对你来说，更让你担心的是人类引起的问题，还是人工智能或通用人工智能带来的问题呢？
Yeah. Humans abusing the technology versus AGI itself through mechanism that you've spoken about which is fascinating deception or this kind of stuff. Yes. Getting better and better and better secretly and then I think they operate over different time scales and they're equally important to address. So there's just the common garden or velarity of like, you know, bad actors using new technology in this case general purpose technology and we purpose it for harmful ends.
好的。人类滥用科技与广义人工智能（AGI）自身以你提到的那些机制，例如引人入胜的欺骗等方式运作之间，是有区别的。是的，人工智能在暗中不断改进。我认为这两者在不同的时间尺度上运作，但同样重要，需要我们关注。一般来说，就是有些不良分子利用新技术（在这个例子中是通用技术）来达到有害目的。
And that's a huge risk and I think there has a lot of complications because generally, you know, I mean huge favor of open science and open source and in fact we did it with all our science projects like Alpha Fold 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 individuals or even rogue states and but enable access at the same time to good actors to to maximally build on top of. It's a pretty tricky problem that there's I've not heard a clear solution to.
这是一项巨大的风险，我认为会有很多复杂的情况。一般来说，我非常支持开放科学和开源项目，事实上，我们已经在所有科学项目上实践了这一点，比如Alpha Fold等，都是为了造福科学界。但是，如何限制不良行为者——无论是个人还是流氓国家——访问这些强大的系统，同时允许善意的使用者最大限度地进行创新呢？这个问题相当棘手，我还没有听到一个明确的解决方案。
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. Yeah, I tend to maybe on my mind is limited worry more about the humans to the bad actors 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 again geopolitical technology perspective how do you reduce the number of bad actors in the world that's that's also an interesting human problem.
所以，我们有恶意行为者的使用案例问题，再加上随着系统变得更加自主化，更接近通用人工智能（AGI），我们如何确保设置好保护措施，让它们按照我们的意愿行事并保持在我们的控制之下。这确实是一个值得关注的问题。 另一方面，我个人或许更担心的是人类中的那些恶意行为者。这里涉及到一个问题：如何防止将具有破坏性的技术落入恶意行为者手中。同时，从地缘政治和技术的角度来看，还有另一个值得探讨的问题，那就是如何减少世界上的恶意行为者数量。这同样是一个有趣的人类问题。
Yeah, it's a hard problem. I mean look we we can maybe also use the technology itself to help early warning on some of the bad actor use cases right whether that's bio or nuclear or whatever it is like AI could be potentially helpful there as long as the AI that you're using is itself reliable right 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 right.
是的，这是一个棘手的问题。我的意思是，我们或许可以利用技术本身来对一些恶意行为（比如生物或核方面的）进行预警。只要你所使用的人工智能本身是可靠的，AI在这方面可能会有帮助。这是一个互相关联的问题，这也正是其复杂之处。此外，这可能需要至少在中美之间达成一些基础标准方面的国际协议。
I have to ask you about the book the maniac there's this the hand of god moment we said all is moved 78 that perhaps the last time a human did a move of pure human genius and beat alpha-go or like broke its brain. Yes, sorry to anthropomorphize but it's an interesting moment because I think in so many domains it will keep happening.
我必须问你关于《疯子》这本书的事情，其中有一个“上帝之手”的时刻，我们说那是第78步，那也许是最后一次人类凭借纯粹的人类智慧做出棋步并打败AlphaGo，或者说让它“崩溃”。对不起，把它拟人化了，但这是个有趣的时刻，因为我认为在许多领域，这种情况会不断发生。
Yeah, it's a special moment and you know it was great for Lisa Dole and you know I think it's in a way there was some of inspiring each other we as a team were inspired by Lisa Dole's brilliance and nobleness and then maybe he got inspired by you know what alpha-go was doing to then conjure this incredible inspirational moment it's all you know captured very well in the in the documentary about it and I think I'll continue in many domains where there's this at least for again for the foreseeable future of like the humans bringing in the ingenuity and asking the right question let's say and then utilizing these tools in a way that then cracks a problem.
是的，这是一个特别的时刻，你也知道这对李世乭来说是很棒的。我觉得在某种程度上，我们相互激励。我们整个团队都被李世乭的聪明才智和高贵风范所激励，而他可能也因为AlphaGo的表现而受到启发，从而创造了这个令人难忘的时刻。这一切都在相关纪录片中得到了很好的展现。我认为在许多领域，这种情况将持续存在，至少在可预见的未来，人类将继续展现创造力并提出正确的问题，然后利用这些工具来解决问题。
Yeah, what is the AI becomes smarter and smarter one of the interesting questions we can ask ourselves is what makes humans special does feel perhaps biased that we humans are deeply special I don't know if it's our intelligence it could be something else that that other thing that's outside the mad dreams of reason I think that's what I've always imagined when I was a kid and starting on this journey of like I was of course fascinated by things like consciousness did in neuroscience PhD to look at how the brain works especially imagination and memory I focused on the hippocampus and it's sort of going to be interesting I always thought the best way of course one can come philosophise about it and have thought experiments and maybe even do actual experiments like you do neuroscience on on real brains but in the end I always imagine that building AI a kind of intelligent artifact and then comparing that to the human mind and 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 and I suspect there probably is but it's going to be hard to you know I think this journey where Ron will help us understand that and define that and you know there may be a difference between carbon based substrates that we are and silicon ones when they process information.
是的，当人工智能变得越来越聪明时，我们可以问自己一个有趣的问题：是什么让人类如此特别？我们人类常常认为自己深具独特性，但这是否带有某种偏见呢？我不知道是否因为我们的智力，或者可能是别的什么东西，一个超出理性疯狂梦想的东西。我想这就是我小时候对这个旅程的想象。当时我对意识之类的事物很着迷，为了研究大脑如何运作，特别是想象力和记忆，我做了神经科学的博士研究，重点研究了海马体。我觉得这将会很有趣。当然，可以通过哲学思辨来探索这个问题，进行思想实验，甚至像在真正的脑子上做神经科学实验。但最终，我一直认为最好的方法是建造一种智能人工制品，然后将其与人类的大脑进行比较，看它们之间的差异，以此揭示人类大脑的特殊性——如果确实有什么特别之处的话。我怀疑很可能是有的，但这将会是一个困难的过程。我认为这段旅程将帮助我们理解并定义这种特殊性。你知道的，碳基生命体（我们）与硅基生命体（人工智能）在处理信息时可能确实存在差异。
You know one of the best definitions I like of consciousness is it's the way information feels when we process it right it could be I mean it doesn't have it's not very helpful scientific explanation I think it's kind of interesting in true in true intuitive one and and so you know on this this this journey this scientific journey where Ron will I think help uncover that mystery yeah what I cannot create I do not understand that's somebody deeply admired Richard five million can mention you also reach for the the Wagner dreams of universality that he saw in constrained domains but also broadly generally in mathematics and so on so so many aspects on which you're pushing towards not to start trouble at the end but Roger Penrose yes so you know do you do you think consciousness does 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 classical computer yeah or is it a quantum mechanical nature.
你知道，我最喜欢的关于意识的定义之一是：意识是我们处理信息时对信息的感受。这可能不是一个非常有用的科学解释，但我认为这是一种有趣且直观的理解。在这段科学探索的旅程中，Ron可能会帮助揭开这个谜团。正如受人敬佩的理查德·费曼所说，“我无法创造的东西，我就无法理解。”他也追求瓦格纳般的普遍性梦想，在受限领域和更广泛的数学等领域中都体现着这一点。因此，在你努力探索的过程中，有许多方面可以挖掘。最后不想制造麻烦，但提到罗杰·彭罗斯，你是否认为意识，也就是关于意识这个难题——信息的感受方式，首先是可以通过计算实现的吗？如果是，若它和信息处理相关，那么它是否可以由经典计算机进行建模？或者说，它具有量子力学的特性呢？
Well look at penrose amazing think of one of the greatest stuff for the modern era and he we've had a lot of discussions about this of course we quarterly disagree which is you know I I feel like I mean he collaborates with a lot of good neuroscientist to see if he could find mechanisms for quantum mechanics behavior in the brain and they to my knowledge they haven't found anything convincing yet so my betting is there is is that that it's mostly you know it is just classical computing that's going on in the brain which suggests that all the phenomena are modulable or mimicable by a classical computer but we'll see you know there there may be this final mysterious things 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 if we do things like neural link and and or have neural interfaces to the 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 right so and maybe that will tell us so I think it's it's going to be interesting.
看看彭罗斯的惊人发现，他被认为是现代时代最伟大的思想之一。我们当然就此进行了很多讨论，我们常常意见不一。要知道，我觉得他与很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。不过，据我所知，他们还没发现任何令人信服的证据。所以我倾向于认为，大脑中主要发生的还是经典计算，这意味着所有现象都可以被经典计算机模拟或模仿。但是，我们会拭目以待，尤其是意识的感觉、本质等哲学家争论的话题，可能是独特于某种载体的。当我们进行类似脑机接口或神经连接的实验时，可能会对这些有更深的理解。我认为最终我们可能会为了跟上AI系统的发展，能够亲身体会在硅基上计算的感觉。也许那时我们会得到答案。我认为这一切都将非常有趣。
I had a debate once with the late Daniel Dennett about why do we think each other are conscious okay so it's for two reasons one is you're exhibiting the same behavior that I am so that's one thing 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 pass a moment is 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 that behavior looks like a behavior of a conscious being it might even claim it is but we but we wouldn't know how it actually felt and it probably couldn't know we what we felt at least in the first stages maybe when we get to super intelligence and the technologies that builds perhaps will will be able to bridge that.
我曾经和已故的丹尼尔·丹尼特就为什么我们认为彼此是有意识的进行了辩论。这个问题有两个原因。其一，你表现出的行为与我相似，所以从行为上看，你似乎是一个有意识的个体，就像我一样。其二，这一点常常被忽视，那就是我们运行在同样的基础上，所以如果你表现得与我相同并且我们在同样的基础上运行，最自然的假设是你正体验着和我一样的感受。
但是，对基于硅芯片的人工智能来说，我们无法依靠第二点来判断，即便它表现出第一点，就像一个有意识的存在，甚至声称是有意识的，但我们无法知道它实际上有什么感受。它可能也无法理解我们感受到的东西，至少在初期是这样。也许当我们进入超级智能时代，并拥有相关技术时，才能或许能够弥补这一点。
No that's a huge test for the radical empathy is to empathize with a different substrate right exactly we never had to confront that before yeah so maybe maybe through brain computer interfaces be able to truly empathize what it feels like to be a computer well for information to be computed not on a carbon system I mean that's deeply excited me some people kind of think about that with plants with other life forms which could be exactly similar substrate but sufficiently far enough and the evolutionary tree yeah that it requires a radical empathy but to do that with the computer.
这是一个对极端共情能力的巨大考验，即去理解不同的基质。没错，我们以前从未需要面对这样的挑战。所以，也许通过脑机接口，我们能够真正理解成为一台计算机的感觉，或者在非碳基系统上进行信息计算的体验。这让我感到非常兴奋。有些人也在考虑对植物或其他生命形式进行类似的共情，这些生命形式可能与我们在基质上类似，但在进化树上足够遥远，因此需要极端共情。不过，要对计算机这样做就更具挑战性。
I mean no we sort of there are animal studies on this of like of course higher animals like you know killer whales and dolphins and dogs and and monkeys you know they have some and elephants you know they have some aspects certainly of consciousness right even though they're not might not be that that smart on 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 dolphin Gemma you know which can one a version of our system was trained on dolphin and whale sounds and maybe we'll be able to build an interpreter or translator at some point should be pretty cool.
我的意思是，我们在某种程度上进行了动物研究，比如一些高级动物，如虎鲸、海豚、狗和猴子等，它们确实有某些意识的方面。尽管它们的智商可能不算高，但我们已经能够与它们产生共鸣。也许某一天，我们的某些系统可以做到这点。比如我们建立了一个叫做“海豚 Gemma”的东西，其中一个版本的系统是以海豚和鲸鱼的声音为基础进行训练的。也许未来我们能开发出一种解释器或翻译器，那会非常酷。
What gives you hope for the future of human civilization well what gives me hope is that I think our almost limitless ingenuity first of all I think the best of us and the best human minds are incredible and you know I love you know meeting and watching any human that's the top of their game whether that sport or science or art you know it's it's just nothing more wonderful than that seeing them in their element in flow I think it's almost limitless you know 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.
是什么让我对人类文明的未来充满希望？首先，我认为人类几乎无尽的创造力让我感到乐观。我相信我们中最杰出的人才和最优秀的大脑是非常了不起的。我很喜欢观察和接触那些在各自领域表现卓越的人，无论是体育、科学还是艺术。当他们在他们的领域里如鱼得水，展现出极致状态时，没有什么能比这更美妙的了。我认为我们的智能几乎是无尽的，我们的大脑是通用智能系统，因此我认为我们有无尽的潜力可以开发。除此之外，人类极强的适应能力也让我对未来充满信心。
I think it's going to be okay in terms of there's going to be a lot of change but but look where we are now without effectively our hunter gatherer brains how is it we can you know we can cope with the modern world right flying on planes doing podcasts you know playing computer games and virtual simulations I mean it's already mind blowing given that our mind was was developed for you know hunting buffaloes on the on the tundra and and so I think this is just the next step and and it's actually kind of interesting to see how society's already adapted to this mind blowing AI technology we have today already it's like oh I talk to chat bots really fine 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 it not to the level that you can do it like so I don't think I.
我觉得一切都会好起来的。虽然会有很多变化，但看看我们现在的状态，即使我们拥有的仍是狩猎采集时代的大脑，我们依然能应对现代世界。比如我们能坐飞机、做播客、玩电脑游戏和进行虚拟模拟。考虑到我们的思维模式最初是为了在苔原上狩猎野牛而发展出来的，这已经令人惊叹了。所以我认为这只是下一个发展阶段，有趣的是社会已经在适应当今令人瞠目结舌的人工智能技术了。比如，人们已经习惯于与聊天机器人交流。很有可能，我正在参与的这种播客活动将完全被人工智能取代，我是非常容易被替代的，我也在等待这一刻的到来，但我认为那一天还没这么快到来。
Thank you that's that's what we humans do to each other we compliment 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 exactly all of those human things that are deeply human 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 well thank you very much thanks.
谢谢，这就是我们人类彼此之间的相处方式，我们会互相赞美。我非常感激我们人类拥有无限的好奇心、适应能力、同情心以及爱的能力，正如你所说的，这些都是深深扎根于人类的特质。这是一个极大的荣幸，Demis，你是世界上真正特别的人之一，非常感谢你所做的一切，以及今天的交流。非常感谢。
Thanks for listening to this conversation with Demis this office 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 like to submit questions including in audio and video form go to lexframing.com slash AMA 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.
感谢收听与Demis的对话。为了支持这个播客，请查看描述中的赞助商并考虑订阅此频道。现在让我回答一些问题，并尝试表达我一直在思考的一些事情。如果你想提交问题，包括以音频和视频形式，请访问 lexframing.com/AMA。我收到了很多来自大家的精彩问题、想法和请求。我会尽量随机挑选一些，并在每集的最后进行评论。
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 canian 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 the writing his writing were kind of his notes from the front lines of that battle.
今年5月21日，我收到一张便条，上面写着：“嗨，Lex，20年前的今天，大卫·福斯特·华莱士在凯南学院发表了他著名的《这是水》演讲。你怎么看待这个演讲？”我认为，这可能是有史以来最伟大和最独特的毕业演讲之一。当然，我还有很多其他的最爱，包括史蒂夫·乔布斯的演讲。大卫·福斯特·华莱士是我最喜爱的作者之一，也是我最喜欢的人之一。他的作品中有一种悲剧性的诚实，总让人感到他在与自己的内心进行不断的斗争，而他的写作就像是这场斗争中的前线笔记。
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 the 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.
现在进入演讲，我想引用一些内容。当然有一个关于鱼和水的寓言。故事中有两条小鱼正在游泳，它们遇到了一条迎面游来的老鱼。老鱼对它们点头并说：“早上好，孩子们，水怎么样？”两条小鱼继续游了一会儿，其中一条鱼看着另一条鱼说：“水到底是什么？”
在演讲中，大卫·福斯特·华莱士继续说，这个鱼的故事的重点只是说明：最显而易见的重要现实，往往是最难被看见和讨论的。虽然说成一个简单的英文句子，这似乎只是一个陈词滥调，但事实是，在成人生活的日常中，这些平凡的道理可能具有生死攸关的重要性，这就是我想在这个干燥而美好的早晨向你们表达的观点。
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 the little less arrogant to have just a little more critical awareness about myself and my certain ties because a huge percentage of the stuff that I tend to be automatically certain of is it turns out totally wrong and eluded all right back to me what's speaking.
我从这个寓言和接下来的演讲中有几个重要的收获。首先，我认为我们必须质疑一切，特别是关于我们现实、生活和存在本质的最基本假设。而且，这个探索的过程从根本上来说是非常个人化的，几乎没有人能够真正帮助你。大卫·福斯特·华莱士在这方面的呼吁是，让自己变得稍微不那么自以为是，对自己和自己的确信多一点批判性意识。因为事实证明，我习惯性地认为理所当然的很多东西实际上是完全错误的。
second takeaway is that the central spiritual battles of our life are not fought on a mountain tops 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 unborrable 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 and since uh a divisive office of this very podcast episode and I are such fans of Richard Feynman allow me to uh 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.
第二个领悟是，我们生活中的核心灵性战斗并不是在某个山顶的冥想静修中进行的，而是在日常生活的平凡时刻中进行的。第三个领悟是，我们太容易把时间和注意力交给世界上无数让人分心的事物，这些就像是没有止境的注意力黑洞。大卫·福斯特·华莱士在这一点上的行动呼吁是，要深刻意识到每个瞬间的美丽，并在平凡中找到意义。我经常引用大卫·福斯特·华莱士的建议，他认为生活的关键是不被无聊打败，我认为他正好说中了。每个瞬间、每个事物、每次经历，当仔细观察后，都会包含无穷无尽的丰富性可供探究。由于这次播客节目的某位嘉宾和我都是理查德·费曼的忠实粉丝，我也想引用费曼先生在这个话题上的话：“我有个朋友是艺术家，他有时候持一种我不太同意的观点。他会举起一朵花说，'看看这有多美。' 我会同意。然后他说，'作为艺术家，我能看到这有多美，但你作为科学家，把它拆解开来后，就变得无趣了。'”
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 could imagine the cells in there the complicated actions inside which also have the beauty I mean it's not just beauty at this dimension at one centimeter there's also beauty at the smaller dimensions they in the structure also the processes the fact that the colors and 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.
我觉得这有点匪夷所思。首先，他所看到的美其实其他人也能看到，我也能看到，虽然我在美学上的修养可能没有他那么高，但我同样能欣赏花朵的美丽。同时，我看到的花不只是表面的美。我可以想象其中的细胞，以及内部复杂的活动，这些同样蕴含着美。这不仅仅是一厘米范围内的美，还有更微小层次的美，比如结构和过程。此外，花的颜色是为了吸引昆虫来授粉，这也挺有趣的，因为这意味着昆虫能看见这些颜色。这引发了一个问题：低等生物是否也有审美能力？为什么会有这种审美？这些有趣的问题，加上科学知识，只会让我们对花朵的欣赏更加兴奋、神秘和敬畏。科学只是让这种体验更加丰富。
all right back to David Fossil Wallace's speech he has a great story there that a particular 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 felt in my knees in the snow and cried out oh God if there is a God I'm lost in this blizzard and I'm gonna 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 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.
好的，我们回到大卫·福斯特·华莱士的演讲中，他讲了一个我特别喜欢的故事：
在遥远的阿拉斯加荒野的一家酒吧里，有两个男人坐在一起。其中一个是宗教信徒，而另一个是无神论者。两人正在激烈争论上帝是否存在——这是一种在喝了四杯啤酒后特有的强烈争论。无神论者说：“听着，我不是没有理由不相信上帝。我也不是没有尝试过相信上帝和祈祷。就在上个月，我在一个可怕的暴风雪中迷路了，我什么都看不见，气温已经降到零下50度。所以，我试了一下。我跪在雪地里，喊道：‘哦，神啊，如果你存在，我在暴风雪中迷路了，如果你不帮助我，我就会死。’”
回到酒吧里，那个宗教信徒对无神论者感到困惑：“那么你现在应该相信了吧，毕竟你还活着。”无神论者翻了个白眼，说：“不，兄弟，事情就是，有两个爱斯基摩人正好路过，他们带我回到了营地。”
这个故事告诉我们，一切都在于视角的问题。如果我们能谦逊地不断转换并拓展我们对世界的看法，智慧可能就会随之而来。
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 if I may one more thing I wanted to briefly comment on I find 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 the soul 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 I mean 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 wiring my heart on my sleeve as much as I can for me that's the only way to live 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.
感谢您允许我谈一谈戴维·福斯特·华莱士，他是我最喜欢的作家之一，他是一个美丽的灵魂。如果可以的话，我还想简单评论一下另一件事：我发现自己常常在网上受到来自各方的攻击，包括被选择性地误解，甚至直接被撒谎抹黑，说实话，这让我心碎。但我理解，这就是互联网的运作方式，也是我选择的道路的代价。有些日子真的很艰难，从心理上来说，尤其是当有关我的事情被谎言扭曲时，这些事情本是我长期以来幸福和快乐的源泉。不过，这就是生活。我会继续怀着同情心和严谨的态度探索人和想法的世界，尽可能地坦诚待人，因为对我来说，这是生活的唯一方式。人们常常攻击我与麻省理工学院和德雷克塞尔大学的关系，这两所大学我非常热爱并尊重。关于这些话题，网上积累了许多关于我的谣言，其中有些既让人悲哀又让人觉得可笑。我想为那些可能关心的人再次陈述一些显而易见的事实。
TLGR 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 from my bachelor's masters and doctorate degrees second I am a research scientist at MIT and have been there and it paid research position for the last ten years allow me to elaborate a bit more on these two things now but please skip that this is not at all interesting so like I said a common attack on me is that I have no real affiliation with MIT the accusation I guess is an 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 ten 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.
我常常提到两个方面，最近在一个被数百万人收听的播客节目中也提到了。首先，我自豪地在德雷克塞尔大学完成了本科、硕士和博士学位。其次，我是麻省理工学院（MIT）的研究科学家，并且在过去十年里一直在那从事有偿研究工作。我来详细说明这两点，但是如果不感兴趣可以跳过。正如我所说，常常有人对我攻击，说我与MIT没有真正的关系。他们指控我虚假地宣称与MIT相关联，因为我曾在那里讲过一次课。这个指控完全是谎言。我在MIT工作已经超过十年，从2015年至今一直是有偿研究职位。为更清楚起见，我是MIT的一名研究科学家，在计算学院的信息与决策系统实验室（LIDS）工作。目前我仍在MIT，您可以在目录和各个实验室页面找到我的信息。
I have indeed given many lectures that are my tea 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.
多年来，我确实讲过许多我感兴趣的课程，其中只有一小部分发布在网上。对我来说，教学一直只是为了乐趣，并不是我研究工作的一部分。我个人认为自己在教学上并不擅长，但我总能从中学习和成长。正如费曼所说，如果你想深入理解某件事，试着去教别人是个好方法。不过，正如我所提到的，我的主要精力一直放在研究上。我发表了许多经过同行评审的论文，可以在我的Google Scholar个人资料中看到。在麻省理工学院的前四年，我的工作非常密集，大多数时候每周工作80到100小时。之后，在2019年，我仍然保留我的研究科学家职位，但我分出一些时间去追求麻省理工学院以外的人工智能和机器人项目，并投入大量精力到播客上。正如我所说，我一直对准备一集节目所需的时间感到惊讶。
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. 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 more smarter than me many of whom have become lifelong colleagues and friends MIT is a place I go to escape the world the 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.
自2020年以来，我一直没有积极发表研究论文，因为很多播客的每一期节目都需要我花费100到200个小时甚至更多的时间进行阅读、写作和思考，跨越数周甚至数月的时间。我认为这就像播客一样，是一个需要全职投入的严肃工作。不过，不发表论文和不做全职研究也让我感到有些不安，因为我热爱研究，喜欢编程和构建系统来测试有趣的技术想法，尤其是在人与人工智能或人与机器人交互的背景下。我希望在接下来的几个月和几年里可以改变这种状况。我意识到，如果我不发表论文或者不发布供人们使用的系统，我会感到自己的生活缺少了一部分。这些事情确实是我快乐的来源。不管怎样，我对自己在MIT的时光感到自豪，我一直被那些比我聪明的人围绕，许多人都成了终生的同事和朋友。MIT是一个让我远离世俗、专注于探索科学和工程前沿问题的地方，这让我感到非常快乐。当我因为这些被攻击的时候，心理上确实会受到很大影响，也许我哪里做错了，如果是的话，我会努力改进。
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 now to talk about Druxy University which I also love and proud of and am deeply grateful for my time there as I said I want to Druxy'll for my bachelor's masters and doctor degrees in computer science and electrical engineering I've talked about Druxy'll 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 Druxy'll and how grateful I am for it.
在所有关于学术工作的讨论中，我希望你知道，我从来没有在播客或私人生活中宣称自己是某方面的专家。我并不自以为聪明，事实上，我经常自嘲是个傻瓜，并且是真心这么认为的。我努力尽可能地调侃自己，并总是庆祝他人的成就。现在，我想谈谈我也非常喜爱并引以为豪的德鲁克西大学，我非常感激在那里度过的时光。正如我所说，我在德鲁克西尔获得了计算机科学和电气工程的学士、硕士和博士学位。我多次谈到德鲁克西尔，包括在最近一个播客结尾提到的那次，主题是唐纳德·特朗普。有趣的是，那期节目有上百万人收听，在节目中我回答了关于研究生院的一个问题，并解释了我在德鲁克西尔的学习经历以及对此的感激之情。
if it's at all interesting to you please go listen to the end of that episode or watch the related clip at Druxy'll 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 that gained for my time at Druxy'll 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 program 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 develop a love for programming including yes emex 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 of 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.
如果你对此感兴趣，请一定去听一下那个节目的结尾，或观看相关片段。在Druxy'll工作的期间，我遇到了很多聪明的研究人员和导师，从他们身上我学习到了许多关于工程、科学和生活的知识。在Druxy'll，我获得了很多宝贵的经验。首先，我参加了大量的数学和理论计算机科学的课程，这些课程让我学会了如何深入思考和严谨对待问题，也教会了我怎样努力工作，不轻言放弃，即便有时觉得自己太笨解不开技术难题。其次，我在那段时间编写了大量的程序，主要是用C和C++，我编程的内容包括机器人、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和各种物理系统的模拟。在这里，我真正培养了对编程的热爱，包括对Emacs编辑器和Kinesis键盘的喜爱。同时，那段时间我还阅读了很多书籍，弹了很多吉他，写了不少拙劣的诗歌，并大量练习了柔道和巴西柔术。巴西柔术无时无刻不让我谦卑，它在我整个20多岁期间如此，现在仍是，只要我有机会训练。
anyway I hope that the folks who occasionally get swept up in the chanting online crowds that want to tear down others don't lose themselves in it too much 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 shit I can get a rational emotional I can be too much of a dick 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
无论如何，我希望那些偶尔被网络上的咒骂、抨击声裹挟的人，最终不要在其中迷失自我。我始终认为，人性当中善多于恶，不过我们每个人都是优缺点共存的个体。我自身也是很不完美的，我说话笨拙，有时会说些愚蠢的话；我可能会在情绪上失去理智；在应该友善的时候表现得太过刻薄，有时会陷入偏见的思维漩涡，直到才醒悟过来看清现实的全貌。我们都是不完美的人类，不论是好是坏，但我仍然相信我们共同经历着这美丽的混乱。我爱你们所有人。