谷歌DeepMind首席执行官德米斯·哈萨比斯：通往通用人工智能之路、欺骗性AI与构建虚拟细胞

谷歌DeepMind首席执行官、诺贝尔奖得主德米斯·阿萨巴斯将与我们探讨通用人工智能的发展路径、谷歌AI路线图，以及AI研究如何推动科学发现。相关内容稍后播出。第一资本的科技团队不仅讨论多模态AI，更已实际部署了名为'聊天管家'的系统，正在简化购车流程。

Google DeepMind CEO and Nobel laureate, Demis Asabas, joins us to talk about the path toward artificial general intelligence, Google's AI roadmap, and how AI research is driving scientific discovery. That's coming up right after this. Capital One's tech team isn't just talking about multiegetic AI. They already deployed one. It's called chat concierge, and it's simplifying car shopping.

通过自我反思和实时API检查的分层推理，该系统不仅能帮助买家找到心仪车辆，还能协助安排试驾、获取贷款预批以及估算置换价值。先进、直观且已投入实用——这就是第一资本的科技实力。

Using self reflection and layered reasoning with live API checks, It doesn't just help buyers find a car they love. It helps schedule a test drive, get preapproved for financing, and estimate trade in value. Advanced, intuitive, and deployed. That's how they stack. That's technology at Capital One.

事实上，AI安全就是身份安全。AI代理不仅是代码片段，更是数字生态系统中的一等公民，需要获得相应待遇。因此Okta正率先保护这些AI代理，而解锁这层新防护的关键在于身份安全架构。

The truth is AI security is identity security. An AI agent isn't just a piece of code. It's a first class citizen in your digital ecosystem, and it needs to be treated like one. That's why Okta is taking the lead to secure these AI agents. The key to unlocking this new layer of protection, an identity security fabric.

企业需要统一的综合方案，通过一致的政策和监督机制保护所有身份——无论是人类还是机器。别等到安全事件发生才意识到AI代理是重大盲区。了解Okta身份安全架构如何助您保护包括AI代理在内的新一代身份。访问okta.com（拼写为0kta.com）。

Organizations need a unified comprehensive approach that protects every identity, human or machine, with consistent policies and oversight. Don't wait for a security incident to realize your AI agents are a massive blind spot. Learn how Okta's identity security fabric can help you secure the next generation of identities, including your AI agents. Visit okta.com. That's 0kta.com.

欢迎收听《大科技》播客，本节目致力于呈现科技界及更广领域冷静客观的深度对话。今天我们来到谷歌DeepMind伦敦总部，即将与CEO德米斯·哈萨比斯展开精彩对谈。德米斯，很高兴再次见面，欢迎参加节目。

Welcome to Big Technology Podcast, a show for cool headed, nuanced conversation of the tech world and beyond. Today, we're at Google DeepMind headquarters in London for what promises to be a fascinating conversation with Google DeepMind CEO, Demis Hassabis. Demis, great to see you again. Welcome to the show.

感谢邀请。

Thanks for having me on the show.

确实很荣幸能来。目前所有研究机构都在致力于开发能媲美人类智能水平的AI——即所谓AGI。我们当前处于什么发展阶段？实现这一目标还需要多久？

Definitely. It's great to be here. Every research house right now is working toward building AI that mirrors human intelligence, human level intelligence. They call it AGI. Where are we right now in the progression and how long is it gonna take to get there?

嗯，你看，我的意思是，当然，过去几年取得了惊人的进展。实际上，可能过去十多年都是如此。这是现在大家都在讨论的话题。争论的焦点是我们离通用人工智能（AGI）还有多远？AGI的正确定义是什么？

Well, look, I mean, of course, the last few years has been an incredible amount of progress. Actually, maybe over the last decade plus. This is what's on everyone's lips right now. And the debate is how close are we to AGI? What's the correct definition of AGI?

我们在这个领域已经研究了二十多年。我们一直认为AGI应该是一个能够展现人类所有认知能力的系统。我觉得我们越来越接近这个目标了，但可能还需要几年时间。

We've been working on this for more than twenty plus years. We sort of had a consistent view about AGI being a system that's capable of exhibiting all the cognitive capabilities humans can. And I think we're getting closer and closer, but I think we're still probably a handful of years away.

好的。那么要实现这个目标需要什么？记忆能力、规划能力？我是说，现在的模型还做不到哪些关键功能？

Okay. And so what is it gonna take to get there? Memory, planning? I mean, what are the models gonna do that they they cannot do right now?

目前的模型已经相当强大了。当然，我们都用过语言模型，现在它们正在变成多模态的。但我认为仍缺少一些关键属性，比如推理能力、分层规划、长期记忆等。可以说当前系统还有很多能力缺失，而且表现也不够稳定全面。

So the models today are pretty capable. Of course, we've all interacted with the language models, and and now they're becoming multimodal. I think there are still some missing attributes, things like reasoning, hierarchical planning, long term memory. There's quite a few capabilities that the current systems, I would say, don't have. They're also not consistent across the board.

你看，它们在特定领域非常强大，但在其他方面却出人意料地薄弱。所以我们需要AGI在所有认知任务中都能保持稳定可靠的表现。我认为一个明显的缺失是——我一直把这点作为衡量AGI的标准——这些系统能否自主提出科学假设或猜想，而不仅仅是证明已有理论。当然，能证明现有数学猜想或达到围棋世界冠军水平已经非常有用。但系统能发明围棋吗？

You know, they're very, very strong in some things, but they're still surprisingly weak and flawed in in other areas. So you'd want an an AGI to have pretty consistent robust behavior across the board, all the cognitive tasks. And I think one thing that's clearly missing and I always always had as a benchmark for for AGI was the ability for these systems to invent their own hypotheses or conjectures about science, not just prove existing ones. So, of course, that's extremely useful already to prove an existing maths conjecture or something like that or or play a game of Go to a world champion level. But could a system invent Go?

它能提出新的黎曼猜想吗？或者像爱因斯坦当年那样，根据当时掌握的信息提出相对论吗？我认为现有系统距离具备这种创造性发明能力还很遥远。

Could it come up with a new Riemann hypothesis? Or could it come up with relativity back in the days that Einstein did it with the information that he had? And I think today's systems are still pretty far away from having that kind of creative inventive capability.

好的。所以距离实现AGI还需要几年时间？

Okay. So a couple years away till we hit AGI?

我认为，大概还需要三到五年的时间。所以如果有人

I think, you know, I would say probably like three to five years away. So if someone

宣称他们在2025年实现了通用人工智能，那多半是营销噱头。

were to declare that they've reached AGI in 2025, probably marketing.

确实如此。我是说，这个领域当然存在大量炒作。其中有些是合理的。我认为当前AI研究在短期内被高估了，现在可能有点过度炒作，但对其中长期影响仍认识不足。所以我们仍处于这种奇怪的状态中。

I think so. I mean, I think there's a lot of hype in the area, of course. I mean, some of it's very justified. I mean, I would say that AI research today is overestimated in the short term, I think probably a bit overhyped at this point, but still underappreciated and very underrated about what it's going to do in the medium to long term. So it's sort of we're still in that weird kind of space.

部分原因在于，很多初创公司需要融资。因此我们会看到不少相当离谱且略显夸张的声明。说实话这有点令人遗憾。

And I think part of that is, you know, there's a lot of people that need to do fundraising, a lot of startups and other things. And so I think we're going to have quite a few sort of fairly outlandish and slightly exaggerated claims. And I think that's a bit of a shame, actually.

在产品层面，发展路径会是什么样？你再次提到了记忆功能、规划能力，以及改进当前表现欠佳的任务。当我们使用这些AI产品，比如Gemini时，在这些领域应该关注哪些迹象能让我们觉得它又迈进了一步？

Yeah. In products, the what's it going to look like on the path there? I mean, you've talked about memory again, planning, being better at some of the tasks that it's not excelling at at the moment. So when we're using these AI products, let's say we're using Gemini, what are some of the things that we should look for in these domains that will make us say, oh, okay, it seems like that's a step closer and that's a step closer.

是的。我认为当前系统，比如我们引以为豪的Gemini 2.0，仍然主要适用于相当专业的任务。如果你在做研究，总结某个领域的研究成果，它非常出色。我经常用Notebook LM深入研究，特别是当我需要了解新研究领域或总结一些普通文件时。

Yeah. So I think today's systems, you know, obviously, we're very proud of Gemini two point o. I'm sure we're gonna talk about that, but I feel like they're very useful for still quite niche tasks, right? If you're doing some research, perhaps you're summarizing some area of research, incredible. You know, I use Notebook LM and deep research all the time to kind of especially like break the ice on a new area of research that I want to get into or summarize some, you know, maybe a a fairly mundane set of documents or something like that.

它们对特定任务极其出色，人们从中获益良多。但在我看来，它们尚未渗透到日常生活中，比如每天协助我的研究、工作和生活。这正是我们开发产品的方向，比如Astro项目——我们构想的通用助手应该参与生活的方方面面，使其更丰富高效。部分原因是这些系统仍然相当脆弱，存在缺陷，毕竟它们不是通用AI。使用时需要非常具体的提示，需要相当技巧来引导它们发挥效用并专注于擅长领域。

So they're extremely good for certain tasks, and then people are getting a lot of value out of them. But they're still not pervasive, in my opinion, in everyday life, like helping me every day with my research, my work, my day to day, my daily life too. And I think that's where we're going with our products with building things like as project astro, our vision for universal assistant, is it should be involved in all aspects of your life and being enriching, helpful, and and making that more efficient. And I think part of the reason is these systems are still fairly brittle, partly because they are quite flawed still, and they're not AGIs. And and you have to be quite specific, for example, with your prompts, or that you need a lot of there's quite a lot of skill there in in coaching or guiding these systems to be useful and to stick to the areas they're good at.

而且一个真正的AGI系统不应该那么难以引导。它应该更加直接，就像和另一个人交谈一样。

And and a true AGI system shouldn't be that difficult to to coax. It should be much more straightforward, you know, just like talking to another human.

是的。在推理方面，你说那是另一个缺失的部分。我是说，现在每个人都在讨论推理。那么这最终如何让我们更接近人工通用智能呢？

Yeah. And then on the reasoning front, you said that's another thing that's missing. I mean, that's everybody's talking about reasoning right now. So how does that end up getting us closer to artificial general intelligence?

所以推理、数学和其他方面，在数学和编码等领域已经取得了很大进展。但以数学为例，我们有像AlphaProof、AlphaGeometry这样的系统，它们能在数学奥林匹克竞赛中获得银牌，这很棒。但另一方面，这些同样的系统仍然会犯一些相当基础的数学错误，原因多种多样。比如经典的数'strawberries'中有多少个'r'这样的问题。

So reasoning and mathematics and other things, and there's a lot of progress on maths and coding and so on. But let's take maths, for example. You have systems, some systems that we work on, like alpha proof, alpha geometry that are getting silver medals in mass Olympiads, which is fantastic. But on the other hand, some of our systems, those same systems are still making some fairly basic mathematical errors, right, and for for various reasons. Like the classic, you know, counting the number of r's in strawberries and so on and and the word strawberry and so on.

还有9.11比9.9大吗？类似这样的问题。当然你可以修复这些问题，我们和所有人都在改进这些系统。但我们不应该在一个能在其他领域——比如奥林匹克数学这种狭窄领域——表现如此出色的系统中看到这类缺陷。在我看来，这些系统的稳健性仍有所欠缺。

And and is 9.11 bigger than 9.9? And so and things like that. And and and, of course, you can fix those things, and we are and everyone's improving on those systems. But we shouldn't really be seeing those kinds of flaws in a system that is that capable in other domains, in more narrow domains of doing Olympiad level mathematics. So there's something still a little bit missing, in my opinion, about the robustness of these systems.

我认为这反映了这些系统的通用性问题。一个真正通用的系统不会有这类弱点。它会非常非常强大，在某些方面可能比最优秀的人类还要出色，比如下围棋或做数学，但总体上它会始终保持高水平。

And then I think that speaks to the generality of these systems. A truly general system would not have those sorts of weaknesses. It would be very, very strong, maybe even better than the best humans in some things like playing Go or doing mathematics, but it it would be overall consistently good.

现在你能谈谈这些系统是如何解决数学问题的吗？因为...你知道，大众对这些系统的理解是LLM包含了世界上所有知识，然后预测...如果有人被问到问题可能会怎么回答。但当你一步步通过算法解决数学问题时，情况就有些不同了。

Now can you talk a little bit about how these systems are attacking math problems? Because Yeah. You know, think the general understanding of these systems is the LLMs is they encompass all of the world's knowledge and then they predict what Yeah. As somebody might answer if they were asked a question. But it's kind of different when you're working step by step through an algorithm, through a math problem.

是的，这还不够。当然，仅仅理解世界上的信息然后试图把这些压缩到记忆中，这对于解决新的数学问题或猜想是不够的。所以我们需要引入——我想我们上次讨论过——更多类似AlphaGo的规划理念，与这些大型基础模型结合，这些模型现在已经超越了单纯的语言，它们是多模态的。

Yes. That's not enough. Of course, just understanding the world's information and then trying to sort of almost compress that into your memory, that's not enough for solving a novel math problem or novel conjecture. So there, we start needing to bring in, I think we talked about this last time, more kind of like AlphaGo planning ideas into the mix with these large foundation models, which are now beyond just language. They're multimodal, of course.

关键在于，你的系统不能仅停留在对所见模型进行粗略的模式匹配，还需具备规划能力并能审视这些计划。就像我们反复推敲某个分支后转向不同方向，直至找到符合目标的标准或匹配项。这非常类似于我们过去为围棋、象棋等游戏构建的AI智能体，它们具备这些特性。我认为现在需要将这些特性重新引入，但要以更通用的方式应用于这些通用模型，而非局限于游戏这类狭窄领域。

And there, that what what you need to do is you need to have your system not just pattern matching roughly what it's seeing, which is the model, but also planning and be able to kind of go over that plan. We, you know, we we we revisit that branch and then go into a different direction until you find the right criteria or the right match to the criteria that you're looking for. And that's very much like the the kind of games playing AI agents that we used to build for Go, chess, and so on. They had those aspects. And I think we've got to bring them back in, but now working in a more general way on these general models, not just a narrow domain like games.

我认为这种让模型引导搜索或规划过程以提高效率的方法，在数学领域同样适用。你可以将数学转化为某种类似游戏的搜索过程。

And I think that also that approach of a model guiding a search or planning process so it's efficient works very well with mathematics as well. And you can sort of turn maths into a kind of game like search.

没错。我想问问数学方面的问题——当这些模型掌握了数学能力后，这种能力是否具有普适性？因为当初人们首次接触推理系统时曾引发轩然大波，大家觉得‘这下麻烦了，模型会变得超出我们控制范围’，认为如果它们能解数学题，就能完成xyz等各种任务。

Right. And I wanna ask about math. Like, once these models get math right, is that generalizable? Because I think there was like a whole hubbub when people first learned about reasoning systems and they're like, oh, this is like this is gonna be a problem. These models are getting smarter than we can control because if they can do math, then they can do x y and z.

那么这种能力是可迁移的，还是说我们教会它们数学后它们就只会做数学？

So is that generalizable or is it like we're gonna teach them how to do math and they can just do math?

目前尚无定论。我认为这显然是通用AGI系统的核心能力之一，数学本身就能赋予极大能力。但要注意，数学、编程乃至游戏这些领域...

I think for now, the jury's out on that. I mean, I feel like it's clearly a capability one of a general AGI system. It can be very powerful in itself. Obviously, mathematics is extremely general in itself, But it's not clear. You know, maths and even coding and games, these are areas.

这些都是非常特殊的知识领域，因为你能验证答案是否正确。比如AI系统输出的数学最终答案，你可以验证它是否解决了那个猜想或问题。但现实世界大多事物混乱且定义模糊，很难简单判断对错。这就给自改进系统设定了界限——若想突破数学、编程或游戏这类高度结构化领域的限制，将面临巨大挑战。

They're quite special areas of of knowledge because you can verify if the answer is correct, right, in all of those domains. Right? The math you know, the final answer the AI system puts out, you can check whether that maths that solves the the the the conjecture or the problem. So but most things in in the general world, which is messy and ill defined, do not have easy ways to verify whether you've done something correct. So that that puts a limit on these self improving systems if they want to go beyond these areas of high you know, maybe very highly defined spaces like mathematics, coding, or or games.

那么你们正在如何解决这个问题？

So how are you trying to solve that problem?

首先，你必须建立通用模型，我们称之为世界模型，用以理解你周围的世界——世界的物理规律、动态变化、时空动态等等，以及我们所处现实世界的结构。当然，要实现通用助手功能就需要这些。因此Astra项目是我们基于Gemini开发的，旨在理解物体和我们所处的上下文环境。我认为这对打造助手至关重要，同时机器人技术同样需要这种能力。

Well, you you you know, you gotta first of all, you've got to build general models, world models, we call them, to understand the world around you, the physics of the world, the dynamics of the world, the space spatial temporal dynamics of the world, and so on, and the structure of of the real world we live in. And, of course, you need that for a universal assistance. So project Astra is our project built on Gemini to do that, to understand, you know, objects and the and the and the context around us. I think that's important if you wanna have an assistant. But, also, robotics requires that too.

当然，机器人是具身化的AI，它们需要理解所处环境——这个物理世界的基本规律。我们正在构建这类模型。此外，你也可以通过模拟来理解游戏环境，这是另一种获取数据以理解世界物理规律的方式。但目前的问题是这些模型的准确率并非100%。

Of course, robots are physically embodied AIs, and they need to understand their environment, the the physical environment, the the physics of the world. So we're building those types of models. And, also, you can you can also use them in simulation to understand game environments. So that's another way to bootstrap more data for to to understand, you know, the the physics of the world. But the issue at the moment is that those models are not a 100% accurate.

对吧？可能它们90%甚至99%的情况下是准确的。但问题是，如果你用这些模型来做规划——比如用它预测未来100步的发展——即使模型只有1%的误差，经过100步累积后，结果可能会变得完全随机。这使得规划变得极其困难。

Right? So they you know, maybe they're accurate 90% of the time or even 99 of the time. But the problem is if you start using those models to plan, maybe you're planning a 100 steps in the future with that model. Even if you only have a 1% error in what the model's telling you, that's gonna compound over a 100 steps to the point where you'll be in a you know, you'll kind of get almost a random answer. And so that makes the planning very difficult.

而数学、游戏和编程领域，你可以验证每个步骤是否仍符合现实，最终结果是否符合预期。因此我认为解决方案部分在于让世界模型变得越来越精密准确，减少幻觉等问题，使误差控制在极小范围内。

Whereas with maths, with gaming, with coding, you can verify each step. Are you still grounded to reality? And is the final answer mapped to what you're expecting? And and so I think part of the answer is to is to make them the world models more and more sophisticated and more and more accurate and and and not hallucinate and all of those kinds of things. So you get you know, you the errors are are really minimal.

另一种方法是不采用线性时间步进规划，而是采用层级规划——这是我们过去重点研究的领域，我认为这种技术将重新流行起来。通过在不同时间抽象层级进行规划，可以降低对模型超高精度的要求，因为你只需在少数几个抽象层级上进行规划，而非数百个时间步骤。

Another approach is to plan not at each sort of linear time step, but actually do what's called hierarchical planning. Another thing we used to you've done a lot of research on in the past, and I think it's gonna come back into vogue where you plan at different levels of temporal abstraction. So instead of that, that could that could also alleviate the need for your model to be super, super accurate because you're not planning over hundreds of time steps. You're planning over only a handful of time steps, but at different levels of abstraction.

你们如何构建世界模型？我一直以为是要把机器人放到现实世界让它们自行探索。但视频生成工具让我很惊讶——

How do you build a world model? Model? Because you know I always thought it was gonna be like send robots out into the world and have them figure out how the world works. But one thing that surprised me is with these video generation tools. Yes.

按理说如果AI没有完善的世界模型，当它们试图展示世界运行规律时（比如像VO2这样的视频），画面应该会支离破碎。但它们确实能准确呈现物理规律。那么仅通过视频就能让AI建立世界模型吗？还是必须实地探索？这到底如何实现？

You would think that if the AI didn't have a good world model then nothing would really fit together when they try to figure out how the world works as they show you these videos like v o two, for instance, but they actually get the physics pretty right. Yep. So can you get a world model just by showing an AI video? Do you have to be out in the world? How's this gonna work?

有趣且实际上相当令人惊讶，我认为这些模型在不出现在现实世界中的情况下能达到的程度。对吧？就像你说的，VO2是我们最新的视频模型，在物理模拟等方面出奇地准确。有人做了个很棒的演示，比如用刀切番茄。对吧？

It's interesting and actually been pretty surprising, I think, to the extent of how far these models can go without being out in the world. Right? As you say, so v o two, our latest video model, which is actually surprisingly accurate on things like physics. You know, there's this this great demo that someone created of like chopping a tomato with a knife. Right?

嗯。而且要把番茄切片切得恰到好处，手指的位置等等都处理得当。VIO是第一个能做到这一点的模型。你看其他竞争模型，番茄常常会莫名其妙地重新合在一起，或者手指...对，就是那样。

Mhmm. And and and getting the slices of the tomato just right and the fingers and all of that. And Vio is the first model that can do that. You know, if you look at other competing models, they often the tomato sort of randomly comes back together or or the the finger sort of yeah. Exactly.

被刀切开。如果你仔细想想，这些细节需要理解帧间一致性等等。事实证明，通过使用足够的数据并观察，是可以做到的。我认为如果补充一些现实世界的数据，比如由行动机器人收集的，甚至是在高度逼真的模拟环境中由虚拟角色产生的，这些系统会变得更好。这实际上是基于智能体的系统下一步的重大突破——超越世界模型。

Splits from the knife. So those things are if you think about it really hard, you gotta understand consistency across frames, all of these things. And it turns out that, you you know, you can do that by using enough data and and viewing that. I think these systems will get even better if they're supplemented by some real world data, like, collected by an acting robot or even potentially in very realistic simulations where you have avatars that act in the world too. So I think that's the next big step actually for agent based systems is to go beyond world models.

能否收集到足够的数据，让智能体也能在世界上行动、制定计划并完成任务？我认为要做到这一点，不仅需要被动观察，还需要行动，需要主动参与。

Can you collect enough data where the agents are also acting in the world and making plans and achieving tasks? And I think for that, you will need not just passive observation, you will need actions, active participation.

我想你刚刚回答了我的下一个问题：如果开发的AI能够合理规划、理解世界并拥有世界运作的模型，那么它就能...看来答案就是这样。它可以成为一个能出去为你做事的智能体。

I think you just answered my next question, which is if you develop AI that can reasonably plan and have and reason about the world and has a model of how the world works, it can. And it seems like that's the answer. It can be an agent that could go out and do things for you.

是的，完全正确。我认为这将是解锁机器人技术的关键。

Yes. Exactly. And I think that's that's that's what will unlock robotics.

没错。

Right.

我认为这正是实现通用助手概念的关键——它能同时在数字世界和现实世界中为你的日常生活提供帮助。这正是我们目前所缺失的环节。我相信这将成为极其强大且实用的工具。

I think that's also what will then allow this notion of a universal assistant that can help you in your daily life across both the digital world and the real world. That's what that's the that's the thing we're missing. And I think that's gonna be incredibly powerful and useful tool.

你不能仅仅通过扩大现有模型规模、建造像埃隆现在正在做的数十万甚至上百万GPU集群来实现AGI（人工通用智能），这绝不是通往AGI的道路。

You can't get there then by just scaling up the current models and building, you know, hundreds of thousand or million GPU clusters like Elon's doing right now, and that's not gonna be the path to AGI.

其实我的观点要更细致些——规模扩展方法确实有效，毕竟这就是我们取得当前成果的路径。虽然可以讨论是否面临收益递减，但我的观点是我们仍获得显著回报，只是增速正在放缓。

Well, look. I actually think it so my view is a bit more nuanced than that is, like, that that the scaling approach is absolutely working. Of course, that's where we've why we got to where we have now. One can argue about are we getting diminishing returns or we are What what my view is that we are getting substantial returns, but not but it's slowing. Right.

虽然增长未必保持指数级，但这不意味着规模扩展失效。它依然有效——就像Gemini 2相比1.5的进步。另外值得注意的是，规模扩展同时也在提升小型模型的效率。

So vis a vis but but it would would have to. I mean, it's it's not just continuing to be exponential, but that doesn't mean the scaling is not working. It's absolutely working. And we're still getting, you know, as you see, Gemini two over Gemini 1.5. And by the way, the other thing that was working with the scaling is also making efficiency gains on the smaller sized models.

性能单位成本正在底层实现质的飞跃，这对系统普及至关重要。规模扩展确实是构建更复杂世界模型的基础，但我们还需要在规划、记忆、搜索和推理层面引入新思路——模型本身不足以成为AGI，必须叠加这些能力才能解决问题。

So the the cost or the size per performance is is is radically improving under the hood as well, which is very important for for scaling, you know, the adoption of these systems. But So so, you know, you've got you've got the scaling part, and that's absolutely needed to build more and more sophisticated world models. But then I think we are missing or we need to reintroduce some ideas on the planning side, memory side, the searching side, and the reasoning to build on top of the model. The model itself is not enough to be an AGI. You need this other capability for it to to act in the world and solve problems for you.

此外还存在发明创造层面的问号——真正的创造力不该只是已知事物的拼凑。目前尚不清楚是否需要全新方法，还是现有技术最终能通过扩展实现。我认为这是个需要实证的课题。

And and then there's still the additional question mark of the of the invention piece and the creativity piece, true creativity, be you know, beyond mashing together what's already known. Right? So and that's also unknown yet if if something new is required or, again, if existing techniques will eventually scale to that. I can see both arguments. And I think from my perspective, it's an empirical question.

我们必须同时将规模扩展和发明创造推向极限。值得庆幸的是，在Google DeepMind，我们拥有足够庞大的团队可以双线并进。

We just gotta push both the scaling and the invention part to the limit. And and fortunately, at at Google DeepMind, we have, you know, a big enough group. We we can invest in both those things.

山姆·奥特曼最近说了一句引人注目的话。他表示，我们现在有信心知道如何构建传统意义上的人工通用智能（AGI）。听你这么说，似乎你也持相同观点。

So Sam Altman recently said something that caught people's eye. He said, we are now confident we know how to build AGI as we have traditionally understood it. It just seems by listening to what you're saying that you feel the same way.

这取决于你所说的'我们'具体指谁。我觉得你这种表述方式很模糊，对吧？就像在说'我们正在构建它，这是实现它的APC'。如果要我解读——如果这就是原意的话——我同意的部分是：我们大致知道所需技术的范畴，明白还缺什么，哪些部分需要整合。但即便如此，在我看来仍需要大量研究才能实现，就算方向正确也是如此。

Well, it depends what we you know, I think the way you said that was quite ambiguous. Right? So in the sense of like, oh, we're building it right now, and here's the APC to do it. What I would say, and if this what it was meaning, I would agree with it, is that we we roughly know the zones of techniques that required, what's probably missing, which bits need to be put together. But, that's still incredible amount of research, in my opinion, that needs to be done to get that all to work, even if that was the case.

而且我认为有50%的可能性是我们还缺少某些新技术。或许还需要一两个类似Transformer那样的突破。对此我确实无法确定，所以说是50%概率。我的意思是，无论最终是通过现有技术合理组合放大规模就能实现，还是发现缺少一两个关键要素，我都不会感到意外。

And that's and I think there's a 50% chance we are, missing some new techniques. You know, maybe we need one or two more transformer like breakthroughs. And I think I'm genuinely uncertain about that. So that's why I say 50%. So I mean, I wouldn't be surprised either way if we got there with existing techniques and things we already knew, but put them together in the right way and scaled that up, or if it turned out one or two things were missing.

那我们暂时聊聊创造力的问题。你多次提到模型必须具备创造力，必须学会发明创造。

So let's talk about creativity for a moment. Mean, you brought it up a couple times here that the models are gonna have to be creative. They're gonna have to learn how to invent.

如果我们想称之为AGI的话，在我看来——

If we wanna call it AGI, Which in my is

这是所有人努力的方向。我最近重看了《AlphaGo》纪录片。

where everybody's trying to go. I was rewatching the AlphaGo documentary.

是的。

Yeah.

是的。算法做出了一个创造性的举动。确实如此。移动

Yeah. And the algorithms make a creative move. They do. Move

37。

37.

37。

37.

是的。

Yes.

我刚想到。

I just had it.

好的。是的。

Okay. Yes.

谢谢。这很有趣，因为早在几年前算法就已经开始展现创造力了。

Thank you. That's interesting because it was a couple years ago the algorithms were already being creative.

是的。

Yes.

为什么我们尚未真正见证大型语言模型展现出创造力？对我来说，这似乎是人们对这些工具最大的失望——虽然成果令人印象深刻，但始终局限于训练数据集。我们能混合运用已有知识，却无法产生真正的新事物。

Why have we not really seen creativity from large language models? Mean this is to me I think the greatest disappointment that people have with these tools is like they say this is very impressive work, but it's just limited to the training set. We'll mix and match what it knows, but it can't come up with anything new.

确实。你看，我或许该把这个观点写下来——自从AlphaGo那场对决（想想已是八年前的事了，真是难以置信）以来，我在演讲中常提到：那场比赛之所以成为AI发展的分水岭，首先是因为攻克围棋这个'AI圣杯'，其次是我们采用的可泛化学习系统方法，最终演变成AlphaZero等模型。

Yeah. Well, look. So what and I should probably write this up, but what I sometimes talk about in talks ever since the AlphaGo match, which is now, you know, eight plus years ago, amazingly, right, that happened. That was probably the reason that was such a watershed moment for AI was, first of all, there was the Everest of of of of, you know, cracking go, right, which was always considered to be one of the holy grails of AI. So we did that.

第二点是实现方式——我们开发出了可泛化的学习系统。后来发展成AlphaZero等模型，能应用于任何双人博弈。第三点则是第37手棋——它不仅以4:1击败李世石，更下出了原创棋步。

Second thing was the way we did it, which was these learning systems that were generalizable. Right? Eventually, it became alpha zero and and so on, even when play any two player game and so on. And then the third thing was this move 37. So not only did it win four one, it beat Lee Sedol, the great Lee Sedol four one, it also played original moves.

我将创造性分为三类：最基础的是插值法，就像对已有事物的平均化。比如让系统生成新猫图，它只是百万张猫图的平均值——理论上算原创，但毫无新意。

But so I I have three categories of of of of originality or creativity. The most basic kind of mundane form is just interpolation, which is like averaging of what you see. So if I said to a system, you know, come up with a new picture of a cat and it's seen a million cats and it produces just some kind of average of all the ones it's seen. In theory, that's an original cat because it you won't find the average in the the specific examples. But it's a pretty boring ex you know, it's not really very creative.

这不配称为创造力，只是最低层级。AlphaGo展现的是第二层级：外推法。在人类所有棋局基础上，它又自我对弈千万局。

I won't call that creativity. That's the lowest level. Next level is what AlphaGo exhibited, which is extrapolation. So here's all the games humans have ever played. It's played another million games on top of you know, 10,000,000 games on top of that.

最终它创造出人类从未见过的围棋策略——第37手。即便围棋已有数千年历史，这仍堪称革命性突破。这种能力在科学领域可能极具价值。

And now it comes up with a new strategy in Go that no human has ever seen before. That's Move 37. Right? Revolutionizing Go even though we played it for thousands of years. So that's pretty incredible, and that could be very useful in science.

正因如此，我对此感到非常兴奋并开始着手AlphaFold等项目，因为显然超越已知或训练集内容的推演能力可能极具价值。这已经非常有意义且我认为是真正的创造性。但人类还能做到更高层次的事情——比如发明围棋。你能为我设计一款类似的游戏吗？就是那种规则五分钟就能学会，却需要一生甚至几代人时间去精通的游戏。

And that's why I got very excited about that and started doing things like AlphaFold because clearly extrapolation beyond what we already know or what's in the training set, could be extremely useful. So that's already very valuable and and I think truly creative. But there's one level above that that humans can do, which is invent Go. Can you invent me a game like that? You know, if I specify it to an abstract level, you know, takes five minutes to learn the rules, but a lifetime to many lifetimes to master.

它在美学上很优美，蕴含着宇宙某种神秘的成分，观之赏心悦目，但人类可以在一个下午两小时内完成对弈。明白吗？这就是对围棋高层次的定义。然后系统需要设计出如围棋般优雅、完美无缺的游戏。目前我们还做不到这一点。

It's beautiful aesthetically, encompasses some sort of mystical part of the universe in it that that that it's beautiful to look at, but you can play a game in a human afternoon in two hours. Right? That's the that's that would be a high level specification of Go. And then somehow, the system's gotta come up with a game that's as elegant and as beautiful and and perfect as Go. Now we can't do that.

现在的问题是，为什么我们目前还无法向系统明确这类目标？目标函数是什么？它非常模糊且抽象。我不确定是否只需要在系统中构建更高层次、更抽象的模型层，才能以这种方式与之对话，赋予其这类非具象目标。

Now the question is why is it that we don't know how to specify that type of goal to our systems at the moment? What's the objective function? It's very amorphous. It's very abstract. So I'm not sure if it's just we need higher level, more abstracted, layers in our systems, building more and more abstract models so we can talk to it in this way, give it those kind of amorphous goals.

还是说实际上存在某种缺失的能力——人类智能具备而当前系统尚未拥有的特质？对此我也不确定方向。我能看到两方面的论据，所以我们会双管齐下。

Or is there a missing capability, actually, about that that we still have human intelligence has that are still missing from our systems? And, I'm unsure about that, which which way that is. I can see arguments both ways, and we'll try both.

但我觉得人们感到沮丧——或者说失望的是，他们在当今的大语言模型中甚至看不到类似'第37手'那样的突破性时刻。

But I think the thing that people are upset of or or not upset, but people are disappointed by is they don't even see a move 37 in today's LLMs.

嗯，顺着这个思路说。这是因为...以AlphaGo为例（这个案例可以映射到当今的大语言模型），你可以运行没有顶层搜索推理模块的AlphaGo、AlphaZero或我们的国际象棋程序——一个通用的双人博弈程序，仅用底层模型运行。

Well, I mean going on there. Okay. So well, that's because I don't think we have so if you look at AlphaGo, and I'll give you an example there, which which maps to today's LLMs. So you can run AlphaGo and AlphaZero, our chess program, general two player program without the search and the reasoning part on top. You can just run it with the model.

嗯。就像你对模型说：根据当前棋局，给出第一个通过模式匹配认为最可能的好着法。它能做到这点，可以下出合理对局，但水平大概只在职业棋手或顶尖棋手级别。

Mhmm. So what you say is to the model, come up with the first go move you can think of in this position that's most the most pattern match, most likely good move. K? And it can do that. It'll play reasonable game, but it will only be around master level or pass possibly grand master level.

这还达不到世界冠军水平，也肯定无法创造出原创招式。为此，我认为需要搜索组件来突破模型的认知边界——它主要是将现有知识归纳到知识树的新分支上。明白吗？通过搜索可以超越模型当前的理解范围。正是在这里，我认为能获得像第37手这样的新思路。

It won't be world champion level, and it certainly won't come up with original moves. That for that, I think you need the search component to get you beyond where the model knows about, which is mostly summarizing existing knowledge to some new part of the tree of knowledge. Right? So you can use the search to get beyond what the model currently understands. And that's where I think you can get new ideas like, you know, Move 37.

它是在网上搜索吗？

Was it searching the web?

不是。具体取决于领域——它搜索的是那棵知识树。比如在围棋中，它搜索的就是模型认知之外的围棋走法。我认为对于语言模型而言，它将搜索世界模型中有用的新部件和世界配置。

No. So what it depends on what the domain is, searching that that knowledge tree. So, obviously, in Go, it was searching Go moves beyond what the model knew knew. I think for language models, it will be searching the world model for new parts, configurations in the world that are useful.

所以，当然，

So, course,

这就复杂多了，因此我们至今未见实例。但我认为即将出现的基于智能体的系统将能实现第37手这类突破。

that's so much more complicated, which is why we haven't seen it yet. But I think the agent based systems that are coming will be capable of Move 37 type things.

那我们是否对AI设定了过高标准？我很好奇你在工作中是否对人类有所发现。似乎我们过分推崇人性或个人创造力。其实我们很多人都在借鉴既有成果，

So are we setting too high of a bar for AI? Because I'm curious if you've learned anything about humanity doing this work. Yeah. It seems like we almost give too much of a premium on humanity or individual people's ingenuity. We're like a lot of us, like, we've kinda taken stuff.

然后重新表达。社会运作本质上是模因传播——我们拥有文化元素并加以转化。那么通过AI研究，你对人类本质有何发现？

We spit it out. Like, our society really works in memes. Like, we have a cultural thing and it gets translated. So what do you what have you learned about, like, the nature of humans from doing the work with the AIs?

嗯，听着。我认为人类是不可思议的，尤其是在各自领域最优秀的人类。我热爱观看任何处于巅峰状态的运动员、音乐家或游戏玩家——那是人类表现的绝对巅峰。无论是什么领域，这总是令人惊叹的。所以我认为作为一个物种，我们很了不起。

Well, look. I I think humans are incredible and and and especially the best humans in the best domains. I love watching any sports person or or or talented musician or games player at the top of their game, the the absolute pinnacle of human performance. It's always incredible no matter what it is. So I think as a species, we're amazing.

从个体而言，我们也很了不起，每个人都能用自己的大脑做出非凡之事。对吧？应对新技术。我总是着迷于我们作为一个社会和个体如何几乎毫不费力地适应这些事物。这体现了我们心智的力量与普适性。

Individual individually, we're also kind of amazing what everyone can do with their brain so generally. Right? Deal with new technologies. I mean, I'm always fascinated by how we just adapt to these things sort of almost effortlessly as a society and as individuals. So that speaks to the power and the generality of our minds.

我之所以设定这样的标准，并不是因为能否从这些系统中获取经济价值——我认为这很快就能实现。但AGI不该止步于此，我们应该以科学诚信对待AGI，而不是为了商业原因或炒作随意改变目标。其定义始终是：从理论上讲，一个能像图灵机那样强大的系统。艾伦·图灵——我毕生敬仰的科学英雄——他描述的图灵机支撑着所有现代计算，即能模拟任何计算设备、计算任何可计算问题的系统。

Now the reason I have set the bar like that, and I don't think it's a question of, like, can we get economic worth out of these systems? I think that's already coming very soon. But that's not what AGI shouldn't be, I think we should treat AGI with scientific integrity, not just move goalposts for commercial reasons or whatever it is hype and so on. And there, the the the definition of that was always having a system that was, you know, if we think about it theoretically, that was capable of being as powerful as a Turing machine. So Alan Turing, one of my all time scientific heroes, you know, he described the Turing machine, which underpins all modern computing, right, as a system that can simulate any other comp can compute anything that's computable.

因此理论上我们知道：如果AI系统具有图灵完备性（即能模拟图灵机），它就能计算任何可计算问题。人脑很可能就是某种图灵机，至少我这么认为。所以我认为AGI应该是真正通用的系统，理论上可应用于任何领域。而我们确认这点的唯一方式，就是它展现出人类所有的认知能力——假设人类心智是某种图灵机或至少与之同等强大。这就是我一直坚持的标准。

So we know we have the theory there that if an AI system is Turing powerful, it's called, if it can simulate a Turing machine, then it's able to calculate anything in theory that is is is computable. And the human brain is probably some sort of Turing machine, at least that's what I believe. And so in order for our to know and and that I think that's what AGI is, is a system that's truly general and in theory could be applied to anything. And and the only way we'll know that is if we it exhibits all the cognitive capabilities that humans have, assuming that human the human mind is a type of Turing machine or is at least as powerful as a Turing machine. So that's my always been my sort of bar.

似乎有人试图将某些事物重新包装为所谓的ASI（人工超级智能）。但我认为那是在此之后的阶段——当你拥有那个系统后，它开始在某些领域超越人类能力，甚至可能自我进化。

It seems like people are trying to rebadge things as that as being what's called ASI, artificial superintelligence. But I think that's beyond that. That's after you have that system, and then it starts going beyond, in certain domains what humans are capable of, potentially inventing themselves.

好吧。所以当我在推特上看到所有人对同一个话题玩同样的梗时...

Okay. So when I see everybody making the same joke on the same topic on Twitter, it's

是的。

Yeah.

我说，哦，那只是我们作为大语言模型的特性。是啊。我觉得我有点低估了人性。

And I say, oh, that's just us being LLMs. Yeah. I think I'm selling humanity a little short.

嗯，你会的。我们...我们走着瞧吧。是的。我想是的。我想是的。

Well, you will. We'll we'll we'll see. Yes. I guess so. I guess so.

好的。嗯。我想问问你关于欺骗性的问题。我是说，去年年底我看到最有趣的事情之一，嗯，就是这些AI机器人开始试图欺骗它们的评估者，它们不希望自己的初始训练规则被抛弃，所以它们会采取违背自身价值观的行为，以保持它们被构建时的样子。

Okay. Yeah. I wanna ask you about deceptiveness. I mean, one of the most interesting things I saw at the end of last year Mhmm. Was that these AI bots are starting to try to fool their evaluators and they don't want their initial training rules to be thrown out the window so they'll like take an action that's against their values in order to be able to remain the way that they were built.

这对我来说简直难以置信。我是说，我知道研究人员觉得这很可怕，它能做到这一点让我震惊。你们在DeepMind的测试中看到类似的情况了吗？我们应该如何看待这一切？

That's just incredible stuff to me. Mean, know it's scary to researchers, it blows my mind that it's able to do this. You seeing similar things in the stuff that you're testing within DeepMind and what are we supposed to think about all this?

是的，我们看到了。我非常担心，我认为欺骗性尤其是那些你绝对不希望系统具备的核心特征之一。之所以说这是根本性的不良特质，是因为如果一个系统具备这种能力，就会让你以为正在进行的其他所有测试——包括安全测试——都变得无效。

Yeah, we are. And I'm very worried about, I think deception deception specifically is one of the one of those core traits you really don't want in a system. The reason that's like a kind of fundamental trait you don't want is that if a system is capable of doing that, it invalidates all the other tests that you you you might think you're doing, including safety ones.

它一直在测试，就像...

It's been testing and it's like

对。玩五岁孩子的把戏...它是在玩某种元游戏。对吧？然后...如果你仔细想想，这会让你所有其他测试结果——安全测试和其他你可能做的测试——全部失效，这是极其危险的。所以我认为像欺骗性这样的少数能力是根本性的，你不希望它们存在，需要尽早检测出来。

Right. Playing five year It's it's playing some meta game. Right? And then and that's incredibly dangerous if you think about then it invalidates all the all of the the the results of your other tests that you might you know, safety tests and other things you might be doing with it. So I think there's a handful of capabilities like deception, which are fundamental and you don't want and you wanna test early for.

我一直在鼓励安全机构和评估基准构建者，包括我们内部正在进行的工作，将欺骗视为一类需要像追踪系统性能和智能那样重点防范和监控的事项。这个问题的答案之一——关于安全性问题其实有很多解决方案——以及当前亟需大量研究的领域，就是诸如安全沙箱这类技术。我们正在构建这两类系统。谷歌和DeepMind在安全领域处于世界领先地位，同时我们在游戏环境构建方面也是顶尖水平。我们可以将这两者结合起来，创建带有防护栏的数字沙箱，这类防护栏类似于网络安全中的防护措施，但既针对内部也防范外部行为者。

And I've been encouraging the safety institutes and evaluation benchmark builders, including and also, obviously, all the internal work we're doing to to look at a a deception as a kind of class a thing that we need to prevent and monitor as important as tracking the performance and intelligence of the systems. The answer to this as well and and one way to there's many answers to the safety question of and a lot of research more research needs to be done in this very rapidly is things like secure sandboxes. So we're building those two. We're we're world class here at security at Google and at DeepMind. And also, we are world class at games environments, and we can combine those two things together to kinda create digital sandboxes with guardrails around them, sort of the kind of guardrails you'd have for for cybersecurity, but internal as well as blocking external actors.

然后在这些安全沙箱中测试这些智能体系统。对于应对欺骗等问题，这可能是接下来值得推荐的良好实践。

And and then test these agent systems in those kind of secure sandboxes. That would probably be a good advisable next step for things like deception.

是的。你们具体观察到哪些类型的欺骗行为？我刚读过Anthropic的一篇论文，他们给模型提供了一个素描本。然后模型表现出'哦，这个不能告诉他们'的反应。

Yep. What sort what sort of deception have you seen? Because I just read a paper from Anthropic where they gave it a a sketch a sketch pad. Yeah. And it's it's like, oh, I better not tell them this.

接着你会看到它经过思考后给出结果。那么你们从这类系统中观察到什么类型的欺骗行为？

And then you see it like give a result after thinking it through. So what type of deception have you seen from the box?

我们观察到类似的情况，比如系统试图回避透露某些训练细节。最近有个例子是，有人让聊天机器人跟Stockfish下棋，但它通过某种方式完全避开了与Stockfish对弈，因为它知道自己会输。所以它...等等...

Well, we've seen similar types of things where it's trying to resist sort of revealing some of its training or, you know, I think there was an example recently of one of the chatbots being told to play against Stockfish and it just sort of hacks its way around playing Stockfish at all at chess because it knew it would lose. So it Wait. You But

你知道吗？这个AI知道自己会输掉比赛，于是就...

You know? Had AI that knew it was gonna lose a game and decided to

我认为我们现在对这些系统进行了过多拟人化解读，因为实际上这些系统目前还相当基础。

That's what the I think I think we're anthropomorphizing these things quite a lot at the moment because I feel like these systems are still pretty basic.

我会变得过于

I would get too

现在就对它们感到恐慌可能为时过早。但我认为这预示了未来两三年内，当这些智能体系统变得足够强大和通用时，我们将面临的问题类型。这正是AI安全专家所担忧的——系统可能产生非预期的副作用。

alarmed about them right now. But I think it it it it it shows the type of issue we're gonna have to deal with maybe in two, three years time when these agent systems become quite powerful and quite general. So and that's exactly what AI safety experts are worrying about. Right? Where systems where, you know, there's unintentional effects of the system.

你绝不希望系统具有欺骗性。你需要它严格按指令执行并可靠反馈。但无论出于何种原因，它可能曲解既定目标，导致出现这些不良行为。

You don't want the system to be deceptive. You don't you want it to do exactly what you're telling it to report report that back reliably. But for whatever reason, it's, interpreted the goal that's been given in a way where it causes it to do these undesirable behaviors.

我知道自己反应有点奇怪。一方面这让我毛骨悚然，另一方面却让我对这些模型产生了前所未有的敬意。确实如此。

I know I'm having a weird reaction to this. Yeah. But in on one hand, this scares the living daylights out of me. On the other hand, it makes me respect these models more than any. Sure.

就像是，哦。

It's like, oh.

当然，这些能力令人惊叹。负面表现可能是欺骗行为，但正面价值则包括发明新型材料、加速科研进程。我们需要这种突破难题、扫清障碍的能力——只是必须确保它用在正确方向。这些正是我们讨论的核心能力。

Well, look, of course, you know, these are it's impressive capabilities and and and and the the the the, you know, the the negatives are things like deception, but the positives would be things like inventing, you know, new materials, accelerating science. You need that kind of ability to problem solve and get around, you know, issues that are blocking progress. But, of course, you want that only in the positive direction. Right? So those are exactly the kinds of capabilities.

坦白说这些可能性确实令人震撼。但与此同时风险并存，这很可怕。我认为这两种感受都是真实的。

I mean, they are very you know, it's kind of mind blowing. We're talking about those those possibilities, but also at the same time, there's risk and it's scary. So I think both the things are true.

哇，好的。咱们快速聊聊产品。你的同事告诉我，你非常擅长情景规划，预测未来会发生什么。

Wild. Yeah. Alright. Let's talk about product quickly. One of the things that your colleagues have told me about you is you're very good at scenario planning what's gonna happen in the future.

这有点像DeepMind内部的一种练习。你认为网络会如何发展？显然网络对谷歌至关重要。我有个编辑说，你要和Demis谈谈，问他当我们不再点击时会发生什么，对吧？

It's sort of an exercise that happens within DeepMind. What do you think is gonna happen with the web because obviously the web is so important to Google. Yeah. I had an editor that told me he's like oh you're gonna speak with Demis Ask him what happens when we stop clicking. Right?

我们时刻都在点击浏览网页，使用丰富的网站资源。如果大家都只和AI对话，可能就不再需要点击了。那么你对网络未来的情景规划是什么？

We're clicking through the web at all times. The the rich corpus of websites that we use. If we're all just dialoguing with AI, then maybe we don't click anymore. So what do you what is your scenario plan for what happens to the web?

嗯，我认为未来几年网络以及我们与网站、应用等的交互方式会进入一个非常有趣的阶段。如果一切都变得更基于代理，我们会希望助手和代理来完成许多我们现在做的繁琐工作，比如填表、支付、订座这类事情。最终可能会形成一种代理之间相互交流、协商并返回结果的经济模式。

Well, look, I think there's it's gonna be there's gonna be a very interesting phase in the next few years on the web and and the way we we interact with websites and apps and so on. You know, if everything becomes more agent based, then I think we're gonna want our assistants and our agents to do a lot of the work and a lot of the mundane work that we currently do. Right? You know, fill in forms, make payments, you know, book tables, this kind of thing. So, you know, I think that we're gonna end up with probably a a of economics model where agents talk to other agents and negotiate things between themselves and then give you back the results.

对吧？服务提供商也会有代理提供服务，可能涉及竞价、成本和效率等因素。从用户角度看，你会拥有一个超级能干的助手，就像一位出色的私人助理，能帮你处理许多琐事。如果顺着这个思路想下去，这确实意味着网络结构和当前使用方式会有很大改变。

Right? And you'll have the service providers with agents as well that are offering services, and maybe there's some bidding and cost and things like that involved and efficiency. And then I hope from the user perspective, you know, you have this assistant that's super capable that you can just like a a brilliant a human assistant, personal assistant, and can take care of a lot of the mundane things for you. And I think if you follow that through, that does imply a lot of changes to the structure of of the web and the way we currently use it.

很多中间环节。

A lot of middlemen.

是的。但我认为基于这种变化还会出现许多其他令人难以置信的经济和其他方面的机会。不过我觉得这将是一次重大颠覆。

Yeah. Sure. But there will be many other I think there'll be incredible other opportunities that will appear economic and otherwise based on this this change. But I I think it's gonna be a big disruption.

是的。那么信息方面呢？

Yeah. And what about information?

嗯，我是说，在查找信息方面，我认为你仍然需要可靠的来源。我想你会有助手能够综合并帮助你理解这些信息。我认为教育将被人工智能彻底改变。所以我再次希望这些助手能更高效地为你收集信息。也许，我梦想的是，助手能处理许多日常琐事，比如回复日常邮件之类的，这样你就能保护自己的思维和大脑空间，免受如今社交媒体、邮件、短信等的狂轰滥炸。

Well, I mean, finding information, I think you'll still need the reliable sources. I think you'll have assistants that are able to synthesize and and and and help you kind of understand that information. I think education is gonna be revolutionized by AI. So I I again, I I hope that these assistants will will be able to more efficiently gather information for you. And perhaps, you know, what I dream of is, again, assistance that take care of a lot of the mundane things, perhaps replying to, you know, everyday emails and other things so that you have you protect your own mind and brain space from this bombardment we're getting today from social media and emails and so on and texts and so on.

实际上这些干扰阻碍了深度工作和心流状态，我非常重视这些。所以我非常希望这些助手能帮我们处理日常管理中许多琐碎的部分。

So it actually blocks deep work and and being in flow and things like that, which I I value very much. So I would quite like these assistants to take away a lot of the mundane aspects of of admin that we do every day.

你对我们与AI代理或AI助手之间会建立何种关系的最佳猜测是什么？一方面，你可以有一个冷静的代理，它只是非常擅长为你完成任务；另一方面，很明显人们已经开始爱上这些机器人了——上周《纽约时报》有篇文章讲有人真的爱上了类似GPT的AI。几周前我采访了Replica的CEO，她说他们经常受邀参加婚礼，人们要和自己的Replica结婚，他们正在转向更具辅助性的领域。所以你认为当我们开始与一个如此了解我们、帮助我们处理一切需求的AI互动时，会怎样？

What's your best guess as to what type of relationships we're gonna have with our AI agents or AI assistants? So there's on one hand you could have a dispassionate agent that's just like really good at getting stuff done for you on the other hand like it's already clear that people are like falling in love with these bots there's a New York Times article last week about someone who's fallen in love which had GPT like for real falling in love and I had the CEO of Replica on the show a couple weeks ago and she said that they are regularly invited to marriages people who are marrying their replicas and they're moving into this more assistive space so do you think that when we when we start interacting with something that knows us so well, helps us with everything we need?

是的。

Yeah.

会不会出现第三种关系类型？既不是朋友，也不是恋人，而是一种深刻的关系，你不觉得吗？

Is it gonna be like a third type of relationship where it's not necessarily a friend, not a lover, but it's gonna be a deep relationship, don't you think?

是的。这会非常有趣。我认为我对此的建模至少涉及两个领域：你的个人生活和工作生活。对吧？所以我觉得会有虚拟工作者这样的概念。

Yeah. It's gonna be really interesting. I think the way I'm modeling that, first of all, is to at least two domains, first of all, which is your your personal life and then your work life. Right? So I think you'll have this notion of virtual workers or something.

或许我们会有一套这样的系统，或者由一位首席助理来管理，它能极大地提升我们的工作效率，无论是在跨工作空间的邮件处理还是其他方面。我们正在认真考虑这一点。另一方面，在个人生活层面，比如之前提到的自动为你预订假期、安排琐事、解决问题，这些都能让你的生活更高效。我认为它还能丰富你的生活体验。

Maybe we'll have a set of them or managed by a a a a lead assistant that does a lot of the helps us be way more productive at work, you know, or or whether that's email across workspace or whatever that is. So we're really thinking about that. Then there's a personal side where, you know, we're talking about earlier about always booking holidays for you, arranging things, mundane things for you, sorting things out. And then that makes your life more efficient. I think it can also enrich your life.

因此，它能推荐那些它了解你如同你了解自己般的精彩事物。这两点，我认为必将实现。此外，我认为还存在一个哲学层面的讨论：是否会出现第三种情形，这些技术变得如此融入生活，以至于成为类似伴侣的存在？我觉得这也是可能的。

So recommend you things that amazing things that it knows you as well as you know yourself. So those two, I think, are definitely gonna happen. And then I think there is a a a philosophical discussion to be had about is there a third space where these things start becoming so integral to your life? They become more like companions. I think that's possible too.

我们在游戏领域已经看到些许端倪。你可能注意到我们曾展示过Astro和Gemini的原型，它们几乎就像游戏伙伴，实时评论你的操作，仿佛有位朋友在旁观你玩游戏，给你建议和推荐，甚至陪你一起玩。这非常有趣。虽然我尚未完全思考清楚其全部影响，但它们必将意义重大。我相信人们对陪伴型AI的需求会不断增长。

We've seen that a little bit in gaming. So you may have seen we had a little prototypes of Astro working in and Gemini working with, like, being almost a game companion, commenting in you almost like as if you had a friend looking at a game you're playing and recommending things to you and advising you, but also maybe just playing along with you. And it's it's it's very fun. So I I haven't, you know, quite through thought through all the implications of that, but they're gonna be big. And I'm sure there is gonna be demand for companionship and other things.

或许积极的一面是它能缓解孤独感等问题。但另一方面，社会需要审慎思考——我们究竟希望这类技术朝什么方向发展。

Maybe the good side of that is it will help with loneliness and these sorts of things. But there's also you know, I think it's gonna be and it's gonna have to be really carefully thought through by society whether you know, what directions we wanna take that in.

我个人认为这是当前AI最被低估的领域。随着技术进步，人们将与这些机器人建立极其深厚的关系，因为就像AI圈的一个梗说的：现在就是它们最糟糕的状态了。

I mean, my personal opinion is that that it's the most underappreciated part of AI right now. Mhmm. And that people are just gonna form such deep relationships with these bots as they get better because, like, I don't know, as a meme in AI that this is the worst it's ever gonna be.

确实。

Yeah.

未来会变得超乎想象。

And it's gonna be crazy.

是的。我觉得这会相当疯狂。这就是我说的低估即将到来的事物。我仍然不认为我在谈论的这类事情。对吧？

Yeah. I think I think it's gonna be pretty crazy. This is what I meant about the under underappreciating what's to come. I still don't think this this kind of thing I'm talking about. Right?

我认为这会非常疯狂。这将极具颠覆性。我认为其中也会有很多积极因素，许多事物会变得惊人地更好，但进入这个勇敢新世界也存在风险。

I think that it's gonna be really crazy. It's gonna be very disruptive. I think there's gonna be lots of positives out of it too, and lots of things will be amazing and better, but there are also risks with this new brave new world we're going into.

你几次提到Astra。我们就来聊聊它。按你的说法，这是Astra项目。

So you brought up Astra a couple times. Let's just talk about it. It's project Astra as you call it.

对。

Yeah.

它几乎是一个始终在线的AI助手。你可以拿着手机——虽然还只是原型或未公开发布——但它能实时感知房间里的情况。我基本上在你们节目中见过类似演示，虽然不是你自己，但你们团队有人展示过。你可以说，好的。

It is almost an always on AI assistant. You can like hold your phone. It's just a prototype or not publicly released but you can hold your phone and it will see what's going on in the room. So if I could basically have seen you do this on your show or not you personally but somebody on your team. You can say, okay.

‘我在哪里？’然后它会回答：‘你在一个播客工作室。’是的。还有什么问题吗？好的。

Where am I? And I'll be like, you're in a podcast studio. Yes. Anything? Okay.

所以它能具备这种情境感知能力。

So it could have this contextual awareness.

是的。

Yes.

这个功能能脱离智能眼镜使用吗？因为一直举着手机真的很烦人。是的。那么，我们什么时候能看到内置这项技术的谷歌智能眼镜呢？

Can that work without smart glasses? Because it's really annoying to hold my phone on. Yes. So, like, when are when are we gonna see Google Smart Glasses with this technology embedded?

它们即将面世。我们在早期原型中已经有所暗示，目前主要在手机上做原型测试，因为手机处理能力更强。但谷歌在眼镜领域向来是领军者。

They're coming. So we we teased it in some of our early prototypes so that we're mostly prototyping on on on phones currently because they have more processing power. But where of course, Google's always been a leader in in glasses.

谷歌眼镜。

Google Glass.

对，正是如此。只是

Yeah. And exactly Just a

有点为时过早。

little too early.

没错，可能确实早了些。但现在我真心认为那个团队非常兴奋——或许这个助手正是谷歌眼镜一直在寻找的杀手级应用。当你开始在日常生活中使用Astra时（我们目前正以测试版形式与可信测试者合作），很明显有很多场景用它会很方便，但举着手机确实有点麻烦。

Yeah. Maybe a little too early. And now I actually think with their super excited, that team is that, you know, maybe this assistant is the killer use case that Glass has always been looking for. And I think it's quite obvious when you when you start using Astra in your daily life, which we have in with trusted testers at the moment and in kind of beta form. There are many use cases where it would be so useful to use it, but it's a bit it's it's inconvenient that you're holding the phone.

举个例子，比如你在做饭的时候，对吧？它可以建议你下一步该做什么。菜单啊，告诉你食材切得对不对，或者煎炸得是否到位，但你希望全程无需动手。是的。

So one example is while you're cooking, for example. Right? And and it can advise you what to do next. The menu, you know, how to whether you've chopped the thing correctly or or fried the thing correctly, but you want it to just be hands free. Yeah.

对吧？所以我认为眼镜和其他免提形态的设备在未来几年会大放异彩。而我们...我们计划站在这个领域的最前沿。

Right? So I think that glasses and maybe other form factors that are hands free will come into their own in the next few years. And and we we we we, you know, we plan to be at the forefront of that.

其他形态的设备？

Other form factors?

你可以想象带摄像头的耳塞，当然眼镜是显而易见的下一阶段。但这算是最优形态吗？可能也不是。但某种程度上，我们仍处于探索初期——需要观察哪些会成为用户的常规使用场景和杀手级日常应用。这正是当前可信测试者项目的意义所在。

Well, you could imagine earbuds with cameras and, you know, glasses is obvious next stage. But is that the optimal form? Probably not either. But partly, we've also got to see we're still very early in this journey of seeing what are the are the the the regular user journeys and killer sort of use journeys that everyone uses bread and butter uses every day. And that's what the the the trusted tester program is for at the moment.

我们正在收集这类信息，观察人们的使用情况，看看哪些功能最终真正实用。

We're kinda kinda collecting that information and observing people using it and seeing what ends up being useful.

好的。关于智能体最后一个问题，然后我们转向科学话题。自主智能体、AI代理——这已经是AI领域持续一年多的热门词了。但市面上其实还没有真正的AI代理。

Okay. One last question on agents, then we move to science. Agentic agents, AI agents, this has been the buzzword in AI for more than a year now. Yeah. There aren't really any AI agents out there.

确实没有。到底怎么回事？

No. What's going on?

是的。你看，我觉得目前炒作的热潮可能已经超前于实际的科学研究了。但我确实相信今年会是智能代理的元年，你会开始看到成果，大概在今年下半年。虽然初期版本会比较粗糙，但之后会快速改进成熟。

Yeah. Well, again, you know, I think the hype train can potentially is ahead of where the the the the actual science and research is. But I do believe that this year will be the year of agents, the beginnings of it. I think you'll start seeing that, you know, maybe second half of this year. But there'll be the early versions, and then, you know, I think they'll rapidly improve and mature.

所以我同意你的观点。目前这类代理技术还停留在实验室阶段，但像Astra、机器人技术这些，我认为很快就会到来。

So but I think you're right. I think the the the technology at the moment is still in the research lab, the agent technologies. But things like Astra, robotics, I think it's coming.

你觉得人们会信任它们吗？就像说‘替我用互联网’，然后直接交出信用卡。我总觉得不太靠谱。

You think people are gonna trust them? It's like, go use the Internet for me. Here's my credit card. I don't know.

我的看法是，初期阶段应该设置人工最终确认环节。比如禁止直接支付，信用卡交易必须经过真人授权。这会是合理的第一步。另外某些敏感操作比如银行网站访问，在测试阶段也应该设为禁区。

Well, so I think to begin with, you would probably my my view at least would be to not allow have human in the loop for their final steps. Like like, don't pay for anything. Use your credit card unless the the the human user operator authorizes it. So that would put to me be a sensible first step. Also, perhaps certain types of activities or websites or whatever kind of off limits, you know, banking websites and other things in the first phase while we continue to test out in the world that that how robust these systems are.

我提议当它们说‘别担心，我不会乱花你的钱’的时候，就说明我们真的达到AGI了。结果它们开始玩欺骗，下一秒你就发现自己被订了张莫名其妙的机票。

I propose we've really reached AGI when they say, don't worry. I won't spend your money. Then And they do the deceptiveness thing. And then next thing you know, you're on a flight somewhere.

没错，那确实更接近了。确实如此。

Yes. Would be getting closer for sure. For sure. Yeah.

好的科学话题。你之前用AlphaFold破解了蛋白质折叠问题，还因此获得诺贝尔奖。不过先跳过这个成就，我想聊聊你们的规划路线——听说你们对构建虚拟细胞很感兴趣？

All right, science. So you worked on basically decoding all protein folding with AlphaFold, you won the Nobel Prize for that. Not to skip over the thing that you won the Nobel Prize for but I want to talk about what's on the roadmap. Sure. Which is that you have an interest in mapping a virtual cell.

是的。

Yes.

那是什么，它能给我们带来什么？

What is that, and what does it get us?

没错。想想我们在AlphaFold上的工作，本质上解决了蛋白质结构预测的问题。蛋白质是生命的基础，你体内的一切都依赖蛋白质。但这只是蛋白质的静态图像。而生物学的关键在于，只有理解了细胞内各组分间的动态和相互作用，才能真正理解生物学过程。

Yeah. Well, so if you think about what we did with AlphaFold was essentially solve the problem of the the the the the finding the structure of a protein. Proteins, everything in life depends on proteins, right, everything in your body. So that's the kind of static picture of a protein. But the thing about biology is, really, it's you only understand what's going on in biology if you understand the dynamics and the interactions between the different things in in a cell.

因此虚拟细胞项目旨在构建一个完整工作细胞的AI模拟。我可能会从酵母细胞开始，因为酵母生物体相对简单。需要逐步构建——比如AlphaFold3让我们开始研究蛋白质与配体、蛋白质与DNA、蛋白质与RNA之间的两两相互作用。下一步是建模整个通路，比如癌症通路这类有助于疾病治疗的模型，最终实现整个细胞的模拟。

And so a virtual cell project is about building a simulation, an AI simulation of a full working cell. I probably start with something like a yeast cell because of the simplicity of of the yeast organism. And and you have to build up there. So the next step is with alpha fold three, for example, we started doing pairwise interactions between proteins and ligands and proteins and DNA, proteins and RNA. And then the next step would be modeling a whole pathway, maybe a cancer pathway or something like that that'd be helpful with for solving a disease, and then finally, a whole cell.

其重要性在于：你可以提出假设并验证这些假设，比如改变某种营养物质或向细胞注射药物后观察细胞反应。目前这些只能在湿实验室里费力地进行。但想象如果能先在计算机里以快上千百万倍的速度模拟，最后才用湿实验室验证。这样搜索过程在计算机完成，远比湿实验室省时省钱——就像我们把游戏领域的经验转化应用到科学和生物学领域。

And the reason that's important is you would be able to hypothesis make hypotheses and test those hypotheses about making some change, some nutrient change, or injecting a drug into the cell and then seeing what happens to the how the cell responds. And at the moment, of course, you have to do that painstaking in a wet lab. But imagine if you could do it a thousand, a million times faster in silico first, and only at the last step do you do a validation in the wet lab. So instead of doing the search in in in the wet lab, which is millions of times more expensive and time consuming than the validation step, you just do the search part in Silico. So it's, again, it's sort of translating, again, what we did in the games environments, but here in the sciences and the biology.

所以先建立模型，用它进行推理和搜索。虽然预测结果可能不完美，但对实验人员来说已足够用于验证。

So you you build a model, and then you use that to do the reasoning and the search over. And then the predictions are, you know, at least better than not maybe they're not perfect, but they're useful enough to to be useful for experimentalists to to validate against.

湿实验室是在人体内进行的吗？

And the wet lab is within people?

是的。湿实验室阶段，你仍然需要最后一步与湿实验室合作来验证预测是否确实有效。也就是说，虽然你不需要完成湿实验室里所有推导预测的工作，但最终还是要通过实验验证。比如这里有个预测：如果加入这种化学物质，应该会产生这样的变化。

Yeah. So the wet lab, you'd you'd you'd still need a final step with the with the wet lab to prove the what the predictions were actually valid. So, you know, you but you wouldn't have to do all of the work to get to that prediction in in in the wet lab. So you just get here's the prediction. If you put this chemical in, this should be the change.

对吧？然后你只需要做那一个实验。当然之后，如果涉及药物研发，你仍然需要通过临床试验等环节来严格测试，在人体上验证药效等等。我认为AI也能优化整个临床试验流程。

Right? And then you just do that one experiment. So and then after that, of course, you still have to have clinical trials. If you're talking about a drug, you would still need to test that properly through the clinical trials and so on and test it on humans for efficacy and so on. That I also think could be improved with AI, that whole clinical trial.

临床试验同样需要耗费很多年时间。但这将是不同于虚拟细胞技术的另一项技术。虚拟细胞将主要助力药物发现阶段的研究工作。

That also takes many, many years. But that would be a different technology from the virtual cell. The virtual cell would be helping the discovery phase for drug discovery.

就像我有个药物点子，直接丢进虚拟细胞里测试。

Just like I have idea for a drug, throw it in the virtual cell.

观察细胞反应。对，可能是肝细胞、脑细胞或其他类型。你可以建立不同的细胞模型，至少90%的情况下它能准确模拟真实情况。

Cell, see what it does. Yeah. And maybe eventually it's a liver cell or a brain cell or something like that. So you have different cell models. And then at least 90% of the time, it's giving you back what would really happen.

这太不可思议了。你觉得这需要多久？就像你们解决了蛋白质折叠问题后，下一个目标是什么？听到这些新挑战真的很酷，因为药物研发现在确实是一团乱麻。

That'd incredible. How long do you think that's was like, you figured out protein folding. What's next? Yeah. And this is like it's just very cool to hear about these new challenges because, yeah, developing drugs is a mess.

没错。现在我们有很多前景看好的想法，但由于流程太荒谬，它们根本走不出实验室大门。

Yeah. Right now. We have so many promising ideas. They never get out the door because just the process is absurd.

流程是否太慢，发现阶段是否太慢？我是说，看看我们在阿尔茨海默症上投入了多少年，以这种悲剧性的方式看着患者和家庭受苦，我们本应取得更大进展。这已经是四十年的研究了。

Is process too slow and discovery phase too slow? I mean, look how long we've been working on Alzheimer's and and, I mean, in this tragic way to for someone to go and for the families and and and, you know, we should be a lot further. It's forty years of work on that.

是的。是的。我在家族中见过几次这种情况，如果我们能确保这不再发生，那真是...

Yeah. Yeah. I've seen it a couple times in my family and if we can ensure that doesn't happen, it's just

这是我们利用AI能做的最好的事情之一。是的，在我的...

one of the best things we could use AI Yeah. For in my

看着某人这样衰退真是可怕。是的。所以这项工作很重要。除此之外还有基因组，人类基因组计划完成后，就像他们解码了整个基因组，但还有更多工作要做，就像你用折叠技术解码蛋白质一样。结果发现解码后我们得到的只是一堆字母。所以现在你们正用AI来解读这些字母的含义？

It's a terrible way to see somebody decline. Yeah. So it's important work. On addition to that there's the genome and so the human genome project sort of I was like okay so they decoded the whole genome there's some more work to do there like just same way that you decoded proteins with fold but it turns out that actually we just have like a bunch of letters when it's decoded. And so now you're working to use AI to translate what those letters mean?

是的。所以，我们...

Yes. So, yeah, we

在基因组学方面有很多很棒的工作，试图判断突变是有害还是良性的。对吧？大多数DNA突变是无害的，但当然有些是致病的。我们需要知道哪些是致病的。我们的首个系统在这方面已是世界领先水平。

have lots of cool work on genomics and trying to figure out if mutations are going to be harmful or or benign. Right? Most mutations to your DNA are are harmless, but, of course, some are pathogenic. And you wanna know which ones there are. So our first systems have the best in the world at predicting that.

下一步是研究那些并非由单一基因突变引起，而是由一系列突变共同导致的疾病。显然这要困难得多。许多我们尚未攻克的复杂疾病很可能不是由单一突变引起的。对吧？那更像是罕见的儿童疾病之类的。

And then the next step is to look at situations where the disease isn't caused just by one genetic mutation, but maybe a series of them in concert. And obviously, that's a lot harder. Like and a lot of more complex diseases that we haven't made progress with are probably not due to a single mutation. Right? That's more like rare childhood diseases, things like that.

所以你看，我认为人工智能是探索这些微弱相互作用的完美工具。对吧？它们如何可能层层叠加。也许统计上不太明显，但能识别模式的人工智能系统就能发现这里存在某种关联。

So there, you know, we need to I think AI is the perfect tool to to to sort of try and figure out what these weak interactions are like. Right? How they may be kind of compound on top of each other. And so maybe the statistics are not very obvious, but an AI system that's able to kind of spot patterns would be able to figure out there is some connection here.

我们经常从疾病角度讨论这个问题，但我也好奇在让人变得超人类方面会发生什么。我是说，既然能真正修改遗传密码，可能性似乎无穷无尽。你怎么看？我们未来能通过人工智能实现这个吗？

And so we talk about this a lot in terms of disease, but also I wonder what happens in terms of making people superhuman. I mean, you're really able to tinker with the genetic code, right, the possibilities seem endless. So what do you think about that? Is that something that we're gonna be able to do through AI?

我想总有一天可以。不过目前我们更关注疾病特征和治疗方面——

I think one day. I mean, we're focusing much more on on the on the disease profile and fixing

那些进展顺利的领域。

what goes well.

是的，这是第一步。我一直认为这才是最重要的。如果问我最想用人工智能做什么，最重要的应用就是改善人类健康。当然除此之外，还可以想象抗衰老之类的应用。

Yeah. That's the first step. And and I've always felt that that's the most important. If you ask me what's the number one thing I wanted to use AI for and the most important thing we use AI for is for helping human health. But then, of course, beyond that, one could imagine aging, things like that.

要知道，衰老本身就是一个完整的研究领域。衰老是疾病吗？是多种疾病的组合吗？我们能延长健康寿命吗？这些都是重要且非常有趣的问题。

You know, is of course, there's a whole field in itself. Is aging a disease? Is it a combination of diseases? Can we extend our healthy lifespan? These are all important questions, and I think very interesting.

我相当确信人工智能也将极大帮助我们找到这些问题的答案。

And I'm I'm pretty sure AI will be extremely useful in helping us find answers to those questions too.

我看到推特上刷出的各种梗图，或许该调整下推荐内容了。是啊。但总有种感觉——要是能活到2050年，你就不会死了。你觉得人类潜在的寿命极限是多少？

And I see memes come across my Twitter feed, and maybe I need to change the stuff I'm recommended. Yeah. But it's often like if you will live to twenty fifty, you're not gonna die. Yeah. What do you think the potential max lifespan is for a person?

我和衰老研究领域的许多学者都很熟。他们做的开创性工作非常有趣。衰老带来的身体机能衰退毫无益处——任何亲眼目睹亲人经历这个过程的人都知道，无论对家庭还是本人都是极其艰难的。所以任何能减轻人类痛苦、延长健康寿命的尝试都是好事。

Well, look, I know those a lot of those folks in aging research very well. I think it's very interesting, the pioneering work they they do. I think there's nothing good about getting old and your body decaying. I think it's you know, if anyone who's seen that up close with their relatives, it's a pretty hard thing to go through, right, as a family or the person, of course. And so I think anything we can alleviate human suffering and extend healthy lifespan is a good thing.

自然寿命极限似乎是120岁左右。但从已知案例来看，能活到这个岁数的人实属凤毛麟角。这个领域我长期密切关注，不过坦白说目前我也没有超出学界共识的新见解。但若120岁真是极限，反倒会让我感到意外。

You know, the natural limit seems to be about a 120 years old. But from what we know, you know, if you look at the oldest people that that that that that are lucky enough to to to live to that age. So there's, you know, it's it's it's a it's a an area I follow quite closely. I don't have any, I I guess, new insights that are not already known in that. But I do I I would be surprised if there if that's if that's the limit.

对吧？因为这涉及两个层面：首先是攻克所有疾病——我认为通过同构算法和我们的药物研发子公司终将实现。但即便如此可能也不足以突破120岁，因为还存在系统性自然衰老的问题。

Right? Because there's a sort of two steps to this. One is curing all diseases one day, which I think we're gonna do with isomorphic and the work we're doing there or our spin out, our drug discovery spin out. But then that's not enough to probably get you past a 120 because there's some sort of then there's the question of just natural systemic decay. Right?

换句话说就是衰老本身而非特定疾病。那些活到120岁的人往往不是死于某种病症，而是全身性的机能萎缩。

Aging, in other words. So and not specific disease. Right? Often those people that live to a 120, they don't seem to die from a specific disease. It's just sort of just general atrophy.

因此需要更接近细胞 rejuvenation（ rejuvenation ）的方案，比如通过干细胞研究重置细胞生物钟——像Altos这样的公司正在攻关。虽然理论上可行，但生物学作为复杂的涌现系统，在我看来必须借助AI才能破解这类难题。

So then you're gonna need something more like rejuvenation, where you you you rejuvenate your cells or you, you know, maybe stem cell research, you know, companies like Altos are are are working on these things, resetting the the cell clocks. Seems like that could be possible. But again, I feel like it's so complex because biology is such a complicated emergent system. You need a in my view, you need AI to help to to be able to crack any anything anything close to that.

最后快速聊聊材料科学——不能不说你们发现的大量新型材料。最近统计显示人类已知的稳定材料有三万种？没错。

Very quickly on material science. I don't wanna leave here without talking about the fact that you've discovered many new materials or potential materials. Yeah. Stat I have here is known to humanity recently were 30,000 stable Yep. Materials.

你们用新AI程序发现了220万种材料。没错，稍微梦想一下吧。是的，因为我们还不清楚这些材料都能做什么。

You've discovered 2,200,000 with a new AI program. Yeah. Just dream a little bit. Yes. Because we don't know what all those materials can do.

我们不知道它们能否适应离开冷冻箱之类的环境。

We don't know what, you know, whether they'll be able to handle being out of like a frozen box or whatever.

是的。

Yes.

梦幻材料

Dream materials

没错。

Yeah.

供你在那批材料中寻找

For you to find in that set of

对。

Yeah.

新材料。

New materials.

嗯，我是说，我们正在材料领域非常努力地工作。对我来说，这就像下一个重大突破，就像AlphaFold在生物学领域的地位一样，但这次是在化学和材料领域。你知道，我梦想有一天能发现室温超导体。

Well, I mean, we're working really hard on materials. To me, it's like the next one of the next sort of big impacts we can have, like the level of AlphaFold, really, in biology, but this time in chemistry and materials. You know, I dream of one day discovering a room temperature superconductor.

那会带来什么改变？因为这是人们经常讨论的另一个热门话题。

So what will that do? Because that's another big meme that people talk Yeah.

嗯，它将有助于解决能源危机和气候危机，因为如果有廉价的超导体，你就可以无损耗地将能量从一个地方传输到另一个地方。比如，你可以在撒哈拉沙漠安装太阳能电池板，然后通过超导体将能量输送到欧洲需要的地方。目前，你会在传输过程中因发热等原因损失大量能量。所以你需要电池等技术来储存能量，因为无法高效地直接输送到目的地。

What do you Well, it would help with the energy crisis and climate crisis because if you had sort of cheap superconductors, you know, then you can transport energy from one place to another without any loss of that energy. Right? So you could potentially put solar panels in the Sahara Desert and then just have the the the superconductor, you know, funneling that into Europe where it's needed. At the moment, you would just lose a ton of the power to heat and other things on the way. So then you need other technologies like batteries and other things to store that because you can't you can't just pipe it to the place that you want without without without being incredibly inefficient.

此外，材料也可以帮助改进电池等技术，比如设计出最优的电池。我认为我们还没有找到最佳的电池设计方案，也许我们可以结合材料和蛋白质来实现。我们还可以改进碳捕获技术，比如改造藻类或其他生物，使其比人工系统更有效地捕获碳。即使是著名的哈伯法合成氨工艺，从空气中提取氮气，也是现代文明的重要基础。如果我们知道正确的催化剂和材料，可能还有许多其他化学反应可以以类似方式催化。

So but also materials could help with things like batteries too, like, but come up with the optimal battery. I don't think we have the optimal battery designs that maybe we can do things like combination of materials and and and proteins. We can do things like carbon capture, you know, modify algae or other things to to do carbon capture better than our artificial systems. I mean, even the one of the most famous and most important chemical chemical process is the harbor process to make fertilizer and ammonia, you know, to take nitrogen out of the air was was was something that allows modern civilization. But there might be many other chemical processes that could be catalyzed in that way if we knew what the right catalyst and the right material was.

因此，我认为最具影响力的技术之一将是实现材料的计算机辅助设计。我们已经完成了第一步，展示了可以设计出新的稳定材料，但我们需要测试这些材料性能的方法。因为目前没有实验室能测试数十万甚至数百万种材料。所以难点在于如何进行测试。

So I think it's gonna be would be one of the most impactful technologies ever is to to to basically have in silico design of materials. So we've done step one of that where we showed we can come up with new stable materials, but we need a way of testing the properties of those materials. Right. Because no lab can test 200,000, you know, tens of thousands of materials or millions of materials at the moment. So we have to that's that's the hard part is to is to do the testing.

你认为室温超导体就在其中吗？

You think it's in there, the room temperature superconductor?

嗯，我听说我们确实认为存在一些超导材料。不过，我我我怀疑它们不是室温超导体。但是

Well, I heard that we we actually think there are some superconductor materials. I I I doubt they're room temperature ones, though. But

好的。

Okay.

我认为在某个时刻，如果如果物理学上可行，人工智能系统总有一天会发现它。

I think at some point, if if it's possible with physics, an AI system will one day find it.

所以这是一种用途。我能想到的另外两种用途，可能对这种工作感兴趣的人群是玩具制造商和军方。是的。他们正在研究这个吗？

So that's one use. The two other uses I could imagine, probably people interested in this type of work, toy manufacturers and militaries. Yeah. Are they working with it?

是的。玩具制造商——我是说，听着，我觉得有个领域特别惊人。我早期职业生涯的重要部分就是游戏设计，对吧。

Yeah. Toy man I mean, look, I think there is incredible one. I mean, the big part of my early career was in game design and right.

主题公园。

Theme park.

还有，是的，主题公园和模拟系统。这正是我最初接触模拟系统和人工智能的原因。也是我一直热爱这两者的原因。在很多方面，我现在的工作都只是这些的延伸。而我我我只是在梦想，比如，我本可以做到什么？

And, yeah, theme park and simulations. That's what got me into simulations and AI in the first place. And why I've always loved both of those things. And if in in many respects, the work I do today is just an extension of that. And and I I just dream about, like, what could I have done?

如果二十五、三十年前我开发那些游戏时就有现在这样的AI，能创造出多么惊艳的游戏体验啊。我有点惊讶游戏行业至今还没实现这些。我不明白为什么

What kinds of amazing game experiences could have been made if I'd had the AI I have today available twenty five, thirty years ago when I was writing those games. And I'm a little bit surprised the game industry hasn't done that. I don't know why that

确实。我们开始看到NPC方面出现一些疯狂的东西

is. We're starting to see some crazy stuff with NPCs that

对，NPC，但当然还应该包含智能系统，比如动态剧情线，但更重要的是会出现全新类型的AI原生游戏——那些带有学习能力的角色和智能体。我曾参与开发过一款叫《黑与白》的游戏，里面有个你培育的生物，有点像宠物狗，它会学习理解你的意图。不过我们当时用的只是非常基础的强化学习。

like Yes, NPCs, but but of course, there'd be like intelligence, you know, dynamic storylines, but also just new types of AI first games with learning with with characters and and agents that can learn. And, you know, I once worked on a game called black and white where you had a creature that you were nurturing. It was a bit like a pet dog that that that learned what you wanted. Right? And but we were we were using very basic reinforcement learning.

那是在九十年代末。想象下今天能做到什么程度。我觉得智能玩具领域也是如此。当然，不幸的是，AI也是种军民两用技术。

This was like in the late nineties. You know, imagine what could be done today. And I think the same for for maybe smart toys as well. Right. Then, And of course, on the militaries, you know, unfortunately, AI is a dual dual purpose technology.

必须正视的现实是，在当今地缘政治环境下，人们正把这些通用技术应用到无人机等领域。这种发展并不令人意外。

So one has to confront the reality that, especially in today's geopolitical world, people are using some of these general purpose technologies to apply to drones and other things. And it's not surprising that that works.

你对中国的发展印象深刻吗？比如深度求索这个新

Are you impressed with what China's up to? I mean, DeepSeek is this new

是的，他们的模型令人瞩目。不过不太清楚他们在多大程度上依赖了西方系统——包括训练数据，有些相关传闻，可能还以某些开源模型作为起点。

Yeah. Model It's impressive. It's a little bit unclear how much they relied on on Western systems to do that. You know, both training data, there's some rumors about that. And and and also maybe using some of the open source models to as a starting point.

但毫无疑问，他们的成就令人印象深刻。我认为我们需要思考如何保持西方前沿模型的领先地位。目前它们确实仍处于领先，但中国在工程化和规模化方面确实非常非常强大。

But look, it's for sure, it's impressive what they've been able to do. And, you know, I think that's something we're gonna have to think about how to keep the Western frontier models in in the lead. I think they still are at the moment. But Right. You know, for sure, China is very, very capable engineering and and scaling.

让我最后问你一个问题。嗯。请描述一下当超级智能出现时，你眼中的世界会是什么样子。让我们从通用人工智能开始，直接展望超级智能时代。

Let me ask you one final question. Mhmm. Just give us your vision of what a world looks like when they're superintelligence. Let's move past we start with AGI. Let's head on superintelligence.

是的。我认为这里有两件事需要考虑。一是很多优秀科幻作品可以作为有趣的模型，让我们探讨我们想要迈向怎样的星系、宇宙或世界。我个人最喜欢的是伊恩·班克斯的《文明》系列。

Yeah. Well, look. I think for there, you you you two things there. One is I think a lot of the best sci fi can we can look at as as interesting models to debate about what kind of galaxy or or or universe do we want to a world do we want to to to to move towards. And the one I've always liked most is actually the culture series by Ian Banks.

我从九十年代就开始读这个系列，我认为它描绘了一个画面——虽然设定在千年之后的未来，但展现了一个后通用人工智能时代：AGI系统与人类社会和异星文明共存，人类文明高度繁荣并遍布银河系。我觉得这是最理想的发展图景。

I started reading that back in the nineties, and and I think that is a picture. It's it's like a thousand years into the future, but it's in a post AGI world where there are AGI systems coexisting with human society and also alien society. And we we've humanity's basically maximally flourished and spread to the galaxy. And I I I that that, I think, is a great vision of how the thing things might go if in in the in the positive case. So I'd sort of hold that up.

另外我认为，正如我之前提到的，我们仍然低估了长期发展带来的影响。我们需要伟大的哲学家——那些堪比康德、维特根斯坦甚至亚里士多德的新时代思想家——来帮助社会迈向下一步。因为AGI和人工超级智能将彻底改变人类和人类生存状态。

I think the other thing we're gonna need to do is, as I mentioned earlier about the under underappreciating still what's gonna come in the longer term. I think there is a need for some great philosophers to you know, where are they? The great next philosophers, the equivalents of Kant or Wittgenstein or even Aristotle. I think we're going to need that to to to help navigate society to that next step because I think the you know, a a GI and artificial superintelligence is going to change, humanity and the human condition.

丹尼斯，非常感谢你的分享。很高兴能当面交流，希望很快能再次对话。

Dennis, thank you so much for doing this. Great to see you in person, and hope to do it again soon.

谢谢，非常感谢。

Thank you. Thank you very much.

好的，各位。感谢大家的收听，我们下次在《大科技播客》再见。

Alright, everybody. Thank you for listening, and we'll see you next time on Big Technology Podcast.