丹尼斯·哈萨比斯在斯坦福谈人工智能、创造力与人类繁荣

非常高兴看到大家来参加与德米斯·哈萨比斯的对话。今天我们特别荣幸地邀请到校长约翰·莱文主持这场炉边谈话。斯坦福大学之所以独特，是因为许多最重要的想法并非诞生于单一学院或学科，而是出现在学院和单位的交叉点上。这种跨大学合作的精神在当前尤为重要，因为人工智能的进步开始重塑社会的几乎每一个领域。而其中影响最深远的是医学。我通过商学院与斯坦福医学院的紧密合作深有体会，医学院正在开展一项非凡的努力，通过汇聚社会科学家、科学家、临床医生、工程师和创新者，重新构想癌症创新和护理，从预防到生存期全程改变患者的旅程。这一愿景雄心勃勃，需要一所伟大大学的全方位能力协同合作。斯坦福的优势不仅在于各领域的卓越，更在于我们连接不同领域的能力。我们将人工智能研究人员与医生、组织领导者与科学家、企业家与致力于人类福祉的人们聚集在一起。这正是今天这场对话如此重要的原因之一。德米斯·哈萨比斯是一位人工智能研究员、企业家和诺贝尔奖得主，他的工作恰恰处于这些交叉点上。他是谷歌 DeepMind 的联合创始人兼 CEO，DeepMind 是全球领先的人工智能研究公司之一，成立于 2010 年，2014 年被谷歌收购。如今，该公司是谷歌人工智能努力的核心，并取得了该领域一些定义性的突破。这些突破包括 AlphaGo——第一个在围棋比赛中击败世界冠军的程序；以及 AlphaFold，它通过准确预测蛋白质的三维结构，解决了蛋白质结构预测这一长达 50 年的重大挑战。这一突破对疾病理解和药物发现具有巨大影响。凭借这项工作，德米斯与约翰·江珀和大卫·贝克共同获得了 2024 年诺贝尔化学奖。他还是英国皇家学会和皇家工程院院士。2024 年，他因对人工智能的贡献被授予爵士头衔。他还多次入选《时代》全球最具影响力 100 人榜单，包括 2017 年和 2025 年。但此刻在斯坦福尤为引人注目的是，这里关于人工智能的讨论从未仅仅局限于能力，也关乎人类繁荣。几年前，李飞飞教授和詹妮弗·奥尔开始教授一门斯坦福课程“人工智能促进人类繁荣”，围绕一系列深刻的问题展开：作为人类意味着什么？繁荣是什么样子？技术何时有助于推进这些目标，何时又可能削弱它们？这项工作中的一个见解让我深有感触：某些形式的摩擦实际上是承重的。寻找恰当词语的挣扎、艰难对话的不适、学习新事物的挑战，并非需要消除的低效。相反，它们正是成长、能动性、韧性和意义产生的体验。这也是为什么我们今天的讨论如此重要。在斯坦福，人工智能的进步并非抽象概念。它们已经在重塑我们对发现、诊断、领导力、学习和人类潜力本身的思考方式。人工智能的进步也迫使我们应对关于判断、伦理、制度以及我们最终希望技术帮助构建何种生活的更大问题。所以，感谢大家的光临，请和我一起欢迎校长约翰·莱文和德米斯·哈萨比斯上台。

And it's such a pleasure to see everybody here for the conversation with Demis Hassabis. We're especially honored today to have President John Levin leading this fireside chat. One of the things that makes Stanford University so distinctive is that some of the most important ideas emerge not within a single school or discipline, but at the intersections of schools and units. That spirit of cross-university collaboration is particularly important right now as advances in AI begin to reshape nearly every domain of society. And nowhere is this more consequential than in medicine. I feel that personally through the GSB's close partnership with the Stanford School of Medicine, which is undertaking an extraordinary effort to reimagine cancer innovation and care by bringing together social scientists, scientists, clinicians, engineers, and innovators to transform the patient journey from prevention through survivorship. That vision is ambitious and it will require the full capabilities of a great university working together. Stanford's strength lies not just in excellence within fields but in our ability to connect fields. We bring AI researchers together with physicians, organizational leaders together with scientists and entrepreneurs together with people deeply committed to human well-being. And that is one reason that today's conversation feels so important. Demis Hassabis is an artificial intelligence researcher, an entrepreneur, and a Nobel laureate whose work sits exactly at these intersections. He's the co-founder and CEO of Google DeepMind, one of the world's leading AI research companies. This was founded as DeepMind in 2010 and acquired by Google in 2014. And the company is now central to Google's AI efforts and has produced some of the defining breakthroughs in the field. Some of these breakthroughs include AlphaGo, the first program to defeat a world champion at the game of Go, and AlphaFold, which solved the 50-year grand challenge of protein structure prediction by accurately predicting the three-dimensional shapes of proteins. This was a breakthrough with enormous implications for disease understanding and drug discovery. For this work, Demis alongside John Jumper and David Baker was awarded the 2024 Nobel Prize in Chemistry. He's also a fellow of the Royal Society and the Royal Academy of Engineering. And in 2024, he was knighted for services to artificial intelligence. He has also been named the Time 100 list of the world's most influential people multiple times, including in 2017 and 2025. But what makes this moment especially compelling at Stanford is that the conversation around AI here has never been only about capability. It has also been about human flourishing. Several years ago, professors Fei-Fei Li and Jennifer Oer began teaching a Stanford course on AI for human flourishing built around a profound set of questions. What does it mean to be human? What does flourishing look like? And when does technology help advance those goals versus undermine them? One insight from this work has stayed really deeply with me and that is that some forms of friction are actually load-bearing. The struggle to find the right word, the discomfort of difficult conversations, the challenge of learning something new are not inefficiencies to eliminate. They are instead the very experiences through which growth, agency, resilience and meaning emerge. And that too is why our discussion today matters so much. At Stanford, advances in AI are not abstract. They are already reshaping how we think about discovery, diagnosis, leadership, learning, and human potential itself. Advances in AI are also forcing us to grapple with larger questions about judgment, ethics, institutions, and what kinds of lives we ultimately want technology to help us build. So, thank you all for being here and please join me in welcoming President John Levin and Demis Hassabis to the stage.

德米斯，很高兴你来到斯坦福。

Dennis, it's great to have you here at Stanford.

非常高兴来到这里。谢谢大家的光临。

Fantastic to be here. Thanks everyone for coming.

非常感谢你来做这次对话。我会问你一些问题，之后也会有学生提问，期待听到你的想法。我想，你最近被大量报道，有电影、有书，所以很多人已经了解你的经历。你的轨迹非常了不起：国际象棋神童、电子游戏开发者、科学家、科技企业家和领导者、诺贝尔奖得主——这只是你职业生涯的前半段。那么，如果你试图为你所做的所有这些不同的事情画一条主线，那会是什么？

Really appreciate you doing this. So we're going to I'm going to ask you some questions. We'll have some questions from students and looking forward to hearing your thoughts. Thought maybe we would you've been chronicled a lot recently a movie a a book. So some many people have heard about your trajectory. It is quite remarkable. I chess prodigy, video game developer, scientist, tech entrepreneur and leader, Nobel laureate, that's just the first half of your career. So if you were going to try to draw a through line through all those different things that you've done, what would it be?

嗯，我认为实际上有好几条主线，贯穿这些看似不太相关的领域。首先，我一直非常喜欢在创造力和技术的交叉点上工作，广义上来说。实际上，游戏行业，电子游戏行业，是我职业生涯早期在 90 年代的第一份工作，那是所有行业中最具创造力的空间之一，利用尖端技术与艺术和设计相结合，创造出一种全新的娱乐媒介。那真是一段奇妙的时光。事实上，我职业生涯中最快乐的时光就是 90 年代初期。国际象棋和神经科学，我做的所有这些事情，都源于我从很小就有的一个想法：研究人工智能和 AGI 是一个人职业生涯中最重要、最有趣的事情。所以，作为青少年，我可能读了太多科幻小说，比如《哥德尔、埃舍尔、巴赫》这类书，以及我一些科学英雄的传记，比如图灵、费曼等等。所有这些都激励我试图以非常深刻的方式理解我们周围的世界。而构建人工智能是我表达这一使命的方式，试图构建科学的终极工具。因为生命短暂，我试图重新利用和转化我的每一次经历，服务于那个我坚持了 30 多年的北极星使命。所以，我的国际象棋训练塑造了我思考商业、组织事物和规划的方式，以及我如何能够将非常宏大的计划分解成更小、更易于管理的步骤。

Well, I think there's several through lines actually, with what seems maybe somewhat unconnected subjects. First of all, I've always really enjoyed working at the intersection of creativity and technology, very broadly construed. So, actually the games industry, the video games industry, which was my first very early part of my career in the '90s, was one of the most creative spaces in any industry that was using cutting edge technology with art and design to sort of create an entirely new entertainment medium. So that was really an amazing time. In fact, some of the most fun times I've had in my career was early in the '90s. The chess and the neuroscience I did all of those things I've tried to have from this idea of working on AI and AGI being the most important thing one could and most interesting thing one could spend your career working on also from a very early age. So as a teenager probably I read too much science fiction, reading things like Gödel, Escher, Bach, these types of books and biographies of some of my scientific heroes, you know Turing and Feynman and so on. So all of these were serving to inspire me to try and understand the world around us in a really deep way. And then building AI was my expression of that mission to try and build the ultimate tool for science. And I've tried to, because life's short, I've tried to reuse and repurpose every experience I've had in service of that bigger northstar mission that I've had, you know, for more than 30 years. So, you know, my chess training is the way that I think about business and organizing things and planning and how I think I've been able to break down very ambitious plans into smaller more manageable steps.

嗯，这些都来自国际象棋思维，我会这么说。然后利用游戏，首先是构建游戏，学习大规模工程项目、运营公司和初创企业。然后将创造力与工程融合。实际上，这就是我们今天用 AI 做的事情。这是一门工程科学，所以你是在将创造性工作、科学工作与非常硬核的前沿工程融合在一起。所以这些都结合在一起。最后，关于游戏，众所周知，我们在 DeepMind 早期将游戏作为测试算法想法的完美试验场，最著名的可能是 AlphaGo，我想我们刚刚庆祝了它的十周年，现在回想起来，它可能标志着现代 AI 时代的开始。

Um, that all comes from kind of chess thinking, I would say. And then using games, first of all building games, learning about engineering projects at scale, running companies, startups. And then fusing this creativity with engineering. Actually, it's what we do today with AI. It's an engineering science, so you're fusing creative work, scientific work with very hardcore cutting-edge engineering. So that all served together. And then finally, on games, as everyone knows, we used games in the early days of DeepMind as the perfect proving ground for testing out algorithmic ideas, probably most famously with AlphaGo, which I think we've just had the 10-year anniversary of, and was really, looking back now, maybe the start of the modern AI era.

当你在 2010 年左右进入 AI 领域时，你创立了 DeepMind。你有一个非常雄心勃勃的愿景：先解决智能，然后用它解决一切。进展如何？让我稍微展开一下。哪些是按计划进行的，哪些是偏离计划的？

When you went into AI professionally in 2010 or so, you started DeepMind. You had this very ambitious vision: you were going to solve intelligence and then solve everything else. How's it going? Let me expand a little bit. What has gone according to plan and what has been off plan?

嗯，总体轨迹走得非常好，也许好得令人难以置信。你知道，2010 年我们创立 DeepMind 时，你能想象吗？我们曾试图去找英国的 VC，但数量不多，而我们的商业计划就是：第一步，解决智能；第二步，用它解决一切。人们都很困惑。但我们是认真的。实际上，我们一直沿用那个使命宣言。因为，所谓“解决智能”，我们指的是构建 AGI，理想情况下在构建 AGI 的过程中理解智能的本质，或许还利用 AGI 帮助我们更好地理解自己的大脑和心智。比如意识本质、创造力、梦境，所有这些心智的深层奥秘。我学习神经科学的原因之一，就是试图从我们对大脑的理解中汲取灵感，用于算法思想。所以第一步是尝试构建 AGI，我们一直预见到后来发生的事情：它当然是一种通用技术，也许是终极通用技术。如果以正确的方式构建，它是一个非常通用的学习系统，那么它的应用极限是什么？梦想是它可以应用于几乎任何事情。我认为这已经得到了证实。我特别为第二步设想了推进科学和医学。这就是我所说的用 AI 解决一切。我指的是科学中的所有重大问题，全部。我对它们都很着迷：时间的本质、现实的本质，也许这是最根本的。我上学时喜欢物理，那是我最喜欢的科目。我认为当一个人对重大问题感兴趣时，最终可能会去研究物理。但我之所以决定有太多有趣的重大问题，一个人如何在有生之年解决所有这些问题？在我看来，这意味着要构建新工具来帮助我们，帮助最优秀的科学家、最优秀的专家，在他们所研究的领域以及他们正在解决的重大和重要问题上取得更快的进展。然后，当然，AI 本身也是一个迷人的产物，一个值得研究的科学对象。它几乎是一个新领域。所以对我来说，这感觉像是最迷人、最重要的事情，值得为之付出一生。即使没有成功，我也会去做。我会找到某种方式来做这件事，在学术界或其他地方。这是我一生都计划从事的工作。我早期做的所有事情都是不同的表达，积累经验，以及知识，以便在 2010 年我们觉得准备好快速推进时，能够尝试像 DeepMind 这样的项目。当然，第二部分“用它解决一切”现在已远不止科学和医学，尽管那是我个人努力的方向，同时我也在管理整个组织。但显然，它将对生产力以及科学和医学之外的许多其他领域产生巨大影响。

Well, the broad arcs of it have gone, I mean, unbelievably well, perhaps. You know, when we started DeepMind in 2010, can you imagine? We used to go to try to go to VCs in the UK, of which there weren't very many, and with that as the business plan: it was literally step one: solve intelligence; step two: use it to solve everything else. And people were quite confused. But we really meant it. And actually, we go back to exactly using that mission statement because, um, so by 'solve intelligence' we meant build AGI, ideally also understand the nature of intelligence on the way to building AGI, and perhaps using AGI to help us understand our own brains and minds better. You know, things like the nature of consciousness, what is creativity, dreaming, all of these deep mysteries of the mind. And one of the reasons I studied neuroscience was to try to learn from what we understood about the brain as inspiration for algorithmic ideas. So step one was to try and build AGI, and we always had in mind what sort of happened, which is that of course it's a general-purpose technology, maybe the general-purpose technology. And if it was built in the right way, so it was a learning system that was very general, what would be the limit of what it could be applied to? It could be applied to almost anything was the dream. And I think that's what's borne out. I had specifically in mind for that step two: advancing science and medicine. So that's what I meant by using it to solve everything else. I meant the big questions in science, all of them. I was fascinated by all of them: the nature of time, the nature of reality, maybe that's the most fundamental one. I loved physics when I was at school; that was my favorite subject. And I think when you're interested in the big questions, you end up doing physics probably. But the reason I decided that there were too many interesting big questions, so how was one to try and tackle all of that in a lifetime? That meant, in my view, building new tools to aid us, the best scientists, the best experts, to make much faster progress in the fields they were tackling and the big questions and important questions they were tackling. And then, of course, AI in itself is also a fascinating artifact, a scientific object one could call worthy of study itself. It's almost a new field. So this felt to me like the most fascinating and most important thing to spend one's life on. And I would have been doing it even if it hadn't worked out. I would have found some way to be doing this, you know, in academia or wherever. This is what I always planned to spend my life working on. And all those things I did earlier were different expressions, gathering the experience and, I suppose, the knowledge to be able to attempt something like DeepMind in 2010 when we felt we were ready to make fast progress. And of course, the second part of that, 'use it to solve everything else,' is now much broader than just science and medicine, although that's where I've tried to personally do my work in as well as running the overall organization. But obviously, it's going to be amazing for productivity and many other things in the world outside of just science and medicine.

在 DeepMind 构建这些不同模型的过程中，你从游戏开始，然后进入科学领域。有没有某些时刻你看到，“这真的会成功”？

As you've been building these different models at DeepMind, you started with games and then went into science. Were there particular moments where you saw, 'This is actually going to work'?

有很多时刻我都觉得它不会成功，这么说吧。所以我记得很清楚的一些时刻是：我们从游戏开始，因为它们是自包含的。它们显然是由其他人设计的，旨在让其他人类觉得有挑战性或有趣。我喜欢游戏；它们通常是许多现实世界场景的缩影。你知道，如果你想想围棋、扑克或国际象棋，我经常认为它们就像 MBA 课程中的一个游戏模块，用来研究那些类型的游戏。外交。它们都有现实生活中最佳游戏的有趣方面，而且你显然可以在安全场景中多次练习。这就是我认为游戏非常有用的原因。这也适用于正在学习的 AI 系统：它们是整洁的环境，具有挑战性，并且有明确的目标函数，这对我们早期的强化学习非常重要。几乎没有人将强化学习用于任何规模扩大的问题。它只是被使用，它是一门学术学科，但主要用于像小网格世界这样的玩具问题。不清楚它能否扩展到任何重大问题上。所以我们从最著名但最基础的游戏开始，这些游戏已经风靡全球，那就是 70 年代的雅达利游戏。我们从最简单的游戏开始，那就是 Pong。你知道，就是两个球拍和一个球。有一个内置的 AI 系统——其实不是 AI 系统，是一个内置系统，控制你的对手，并利用游戏拥有的关于球位置的所有信息来移动球拍。

There were many moments where I thought it wasn't going to work, put it that way. So some of the ones I remember really well are: we started with games because they're self-contained. They obviously were designed by other humans to be challenging or fun for other humans to play. I love games; they're often microcosms of a lot of real-world scenarios. You know, if you think of Go or poker or chess, I often thought of them as, you know, an MBA course module would be a games module to study those types of games. Diplomacy. They all have really interesting aspects of the best games of real life, and you can obviously practice many times in a kind of safe scenario. That's what I think games are really useful for. And that applies to AI systems that are learning too: they are neat environments, they are challenging, and they have clear objective functions, which was also very important for our early days of reinforcement learning. Almost no one had used reinforcement learning for any kind of scaled-up problem. It was just being used, it was very much an academic discipline, but it was used mostly for toy problems like little grid worlds. It wasn't clear it could scale up to anything major. So we started with the most famous set of but most basic games that had become world popular, which were Atari games from the '70s. And we started with the simplest game of all, which was Pong. You know, just the two bats and a ball. And there's an inbuilt AI system—it's not really an AI system, an inbuilt system that controls your opponent and uses all the information that the game has about where the ball is and so on to move the bat around.

我们当时想做的是，能否仅凭屏幕上的像素来玩《乓》？也就是原始数据，原始视觉输入，没有其他信息，没有特权信息，无法访问程序内部，比如球的位置或速度等，这些程序当然知道。但我们没有给那个被称为 DQN 的雅达利系统任何这些信息。它只得到屏幕上的两万个像素。两万个像素。现在看起来微不足道，但在 2010 年，那是海量的输入数据。从来没有人处理过这么复杂的东西，还要乘以所有帧。大约有六个月，也许只有两个月，我们在《乓》上连一分都赢不了。它胡乱地移动球拍，让人怀疑它到底能不能控制球拍。它对这些概念一无所知，只是以 21 比 0 输给内置 AI。我当时想，我们尝试了几种不同的方法，但几乎没钱了。我们仅有的几百万美元资金，现在连一个实习生都雇不起，那就是我们全部的资金。我们没拿薪水，钱也快花光了。我想，也许我们还是早了十年，或者二十年。然后神奇地，它得了一分。我想，也许只是运气。接着它开始赢很多分，然后开始赢比赛。然后我们就起飞了。现在，做机器学习的人都知道：一旦有了立足点，通常就能爬山式地解决问题。我认为这就是 AI 的历史。一旦有东西能工作，通常就有办法进一步优化。雅达利项目就是这样。那是我们的第一个重大成果，也是第一篇《自然》论文，确实是第一个深度强化学习模型，而且达到了规模，结合了深度学习来学习领域、处理感知输入和输入的复杂性、发现其中的模式，然后在上面构建强化学习来做决策和规划。

And what we wanted to do was could you play Pong just from the pixels on the screen. So the raw data, the raw visual input and no other information, no privileged information about no access to the insides of the program about you know where the ball is or the speed of it and so on which obviously the program knows. But we didn't give the DQN system as it was called our Atari system any of that information. It just got the 20,000 pixels on the screen. And 20,000 pixels. I mean, it seems trivial now, but back in 2010, that was an enormous amount of input data. No one had ever dealt with something that complex and then multiplied by all the frames that you were doing. And for about it felt like six months, maybe it was only two months, but we couldn't win a single point at Pong, right? So, it was, you know, jerking the bat around. It was like, oh, is it ever going to even be able to control the bat? And of course it had no notions of any of these things and it was just losing 21 nil to the inbuilt AI. And I did think and we had a couple of different ways of trying to attack this and we had almost no money. The runway, the bare couple of million dollars of funding that we had which wouldn't even cover an intern these days was our entire funding. And we were making no salary and we were running out of money. And I was like, oh well maybe it turns out maybe we are still 10 years too early, maybe we're 20 years too early. And then magically it got a point and it was like, oh maybe it was just luck. And then it started winning a lot of points and then it started winning the games. And then it was like, okay we have liftoff now. So now, those of you working in machine learning will know this: if you get a foothold, you can usually hill climb your way out of that. That's been the history of AI, I would say. Once you have something working, there's usually a way of optimizing it more. And that's what turned out with Atari. And so that was our first big result and our first Nature paper was really the first deep reinforcement learning model, certainly at scale, combining deep learning to learn the domain and deal with the perceptual inputs and the complexity of the inputs and find the patterns in it, and then reinforcement learning built on top of that to make the decisions and do the planning.

当然，这一切最终在 AlphaGo 上达到顶峰，那一直是我们的目标。Dave Silva 和我是那个项目的负责人，我们在剑桥读本科时就是朋友，从本科就开始讨论……我们在 90 年代中期上学。深蓝对卡斯帕罗夫的比赛发生在我们上大学时。当然，从国际象棋和 AI 的角度我都着迷。但比起深蓝，我更佩服卡斯帕罗夫的大脑，因为他凭借不可思议的头脑，仍然是有史以来最伟大的国际象棋天才之一，能够与旁边那台超级计算机的蛮力机器基本持平。但他还能用他的大脑做其他所有事情：说五种语言、搞政治、开车，以及人类能做的所有其他事情。对我来说，那太不可思议了，更令人印象深刻。所以深蓝系统缺少了某些东西。显然，那些专家系统技术——手动设计启发式规则，然后在上面使用蛮力搜索——今天许多传统国际象棋程序仍然这样工作，这对国际象棋有效，但对围棋从来不起作用，因为围棋太深奥了。它没有物质概念；每个棋子价值相同。全是关于模式和直觉。即使是顶尖围棋选手也是这样下的。所以我们想，如果有人能真正达到围棋世界冠军水平，那不仅仅是达到那个水平本身，更重要的是我们采用的方法可能是一种非常有趣的算法方法，并且希望它能推广到其他领域。AlphaGo 就是这样。它超出了我们最疯狂的梦想，因为它不仅在 2016 年击败了李世石，还创造了著名的、从未见过的新策略，尽管人类已经下了几千年围棋。围棋是人类发明的最古老的游戏，有 2000 多年历史，职业下棋也有几百年了。我们却没有发现那些策略。这对我来说是双重打击。我一直在等待 AI 能提出新颖东西的那一刻。当然，创造力还有更高的层次，但至少这是一个新颖的想法。那正是我等待的，然后开始将 AI 用于科学。所以我们从首尔一回来，就启动了 AlphaFold 项目。

And then of course that culminated in AlphaGo, which was always our aim. Dave Silva and I, who was head of that project, we were undergrad friends at Cambridge and we had been discussing it since our undergrad about... we were there in the mid '90s. The Deep Blue vs Kasparov match happened while we were at college. Of course I was fascinated by it both from the chess and the AI point of view. But I was more impressed with Kasparov's brain than with Deep Blue because Kasparov, with his incredible mind, still one of the biggest chess geniuses of all time, was able to basically compete on an equal footing with this supercomputer brute force machine next to him. But of course he could do all the other things with his mind: speak five languages, do his politics, drive cars, all the rest of the things humans can do. To me, that was incredible, much more impressive. So there was something missing from the Deep Blue system. And obviously those techniques, those expert system techniques where you hand-curate the heuristics and then use brute force search on top, which is still how a lot of traditional chess programs work today, that works for chess but it's never worked for Go because Go's too esoteric a game. It hasn't got material; every piece is worth the same. It's all about patterns and intuition. Even the top Go players, that's how they play it. So we thought, okay, if someone could actually get to world champion level at Go, it's not just about that, getting to that level was an aside. It was more about the approach we would have taken would probably be a really interesting algorithmic approach and maybe, hopefully, would generalize to other domains. And that's what turned out with AlphaGo. And then it went beyond our wildest dreams really because not only did it win the match against Lee Sedol in 2016, it also created new, famously new strategies that had never been seen before, even though we've played Go. Go is the oldest game humanity has invented, 2,000 years old, 2,000 plus years old, and been played professionally for hundreds of years. And we hadn't discovered those strategies. So that was a double whammy for me. I was waiting for that moment that AI was able to come up with something novel. And it's not that there are no more levels of creativity beyond that, but at least it was a novel idea. And that for me was what I was waiting for, to then start using AI for science. So the moment we got back from Seoul, we started the AlphaFold project.

当你进入蛋白质折叠问题时，你选择了一个有数据、有明确目标函数的问题。而且它成功了。你实际上解决了这个长期存在的预测蛋白质结构的问题。你在推出 AlphaFold 时做了一件非常有趣的事，这显然是一个巨大的科学突破，值得诺贝尔奖，可能也有商业价值，但你却免费提供了它。我很好奇你是如何做出这个决定的？你有没有考虑过其他方式？为什么要免费提供？

Now when you went into the protein folding problem, you picked a problem where there was data and where there was a clear objective function. And it worked. You actually managed to solve this long-standing problem of predicting protein structure. You did something very interesting when you came up with AlphaFold, which was obviously a huge scientific breakthrough, Nobel-worthy, probably also of commercial value, and you just gave it away for free. I'm curious how did you come to that decision? Did you think about other ways of going about it? Why give it away?

是的。所以我们选择了蛋白质折叠问题。我从剑桥本科时就一直关注它。那是我第一次遇到它。我有几个生物学家朋友，他们对蛋白质折叠问题非常着迷，后来他们当然成为了结构生物学家。我特别记得一个，每次我们在酒吧玩桌上足球之类的，他都会痴迷地谈论这是生物学中最重要的一个问题。更重要的是，我认为它是一个根节点问题。如果你能解开它，找到蛋白质的结构，那将开启全新的研究途径，比如药物发现，我们显然在推动这一点，还有基础生物学和疾病理解。

Yeah. So we picked the protein folding problem. I had my eye on that since my undergrad days at Cambridge. That's when I first came across it. I had a few biologist friends who were obsessed with the protein folding problem and actually they ended up becoming structural biologists of course in their career. And one specifically I remember, he was, every time we were in the pub playing table football or something, he would be talking about obsessively how this was the most important problem in biology. And more importantly, I think of it as a root node problem. Like if you could unlock that and find the structures of proteins, that would unlock whole new avenues of research, things like drug discovery obviously we're trying to push that, but also fundamental biology and disease understanding.

所以这是一个值得投入大量精力和时间的问题，因为它会带来深远的影响。而且我觉得它本身也是一个迷人的问题。对我来说，它就像终极谜题，一个三维拼图：氨基酸序列——你可以把它看作基因序列——是如何折叠成三维结构的。这非常有趣，非常精妙。我越研究蛋白质，就越对生物学感到敬畏和惊奇，它们就像不可思议的生物纳米机器。生命中的一切都依赖于蛋白质。当你开始观察它们的结构，你就能理解它们的功能。所以这对我来说是一个迷人的科学问题。

So this was a problem worth really spending a lot of attention and time on because of the downstream effects that it would have. It also felt to me it was a fascinating problem. It felt to me like the ultimate puzzle, you know, 3D puzzle of how does this amino acid sequence, you think of it as genetic sequence, fold up into this 3D structure. It's amazingly interesting, intricate thing. And the more I looked into proteins, the more incredible my respect and wonder is for biology, like these unbelievable little bio-nano machines. Everything in life obviously depends on proteins. And as you start looking at their structure, you start understanding their function. So this was fascinating to me as a science question.

然后，是的，明确的目标是类似最小化系统的自由能。大概物理学就是这样做的。这就是为什么蛋白质在体内能在毫秒内折叠，每秒数十亿次。所以物理学已经解决了这个问题。而且，一定存在某种拓扑结构，你可以通过深度学习系统学习，来引导搜索，就像我们用 AlphaGo 在围棋中找到妙招一样，从比宇宙中原子还多的可能性中找出最佳策略。蛋白质折叠的搜索空间甚至更大，但总有办法合理地缩小范围。你通过深度学习模型学习一种启发式方法，来引导搜索，使其变得可行。这感觉就像我们在围棋中解决的科学问题，将同样的方法、同样的理论应用到这一领域。

And then yes, there was the clear objective is sort of like minimizing the free energy in the system. Presumably this is how physics is doing it. It's why the body, you know, these proteins fold in milliseconds in your body, billions of times a second. So somehow physics has solved this. And it can't be, there must be some topology, let's say, that you could learn maybe with a deep learning system that would guide the search, just like we've done with AlphaGo to find a great move in Go, a great strategy out of more possibilities than there are atoms in the universe. And protein folds are an even larger search space than that, but there's some way to narrow that down in a sensible way. You learn a kind of heuristic using the deep learning models to then guide your search for that to become tractable. And it felt like a really analogous problem in science to what we'd solved in Go, sort of applying some of those same approaches, those same theories to this domain.

另一件事是，显然有 50 年艰苦的晶体学结构生物学工作，由许多伟大的实验室和人员完成。经过所有这些努力，PDB（主要数据库）中大约有 15 万个结构。这实际上并不多。显然，这背后付出了巨大的努力，但有两亿种蛋白质，而 15 万个对于机器学习系统来说是非常少量的数据。所以大多数人认为至少还需要 10 到 20 年，我们才能有足够的数据和正确的算法来解决这个问题。但我们觉得，利用我们所知的所有技术，最终能够取得进展。结果也确实如此。

And then the other thing was, there was obviously 50 years worth of painstaking crystallography structural biology work by many great labs and people. And after all of that effort, there was about 150,000 structures in the PDB, the main database. Which isn't a lot actually. Obviously it's a huge amount of effort that's gone into that, but there's 200 million proteins and 150,000 also for machine learning systems is a very small amount of data. So most people thought it was at least 10, 20 years away before we would have enough data and the right types of algorithms to tackle that. But we felt that using every technique we knew, in the end we could make progress with that. And it turned out to be the case.

当我们决定如何最大化这一成果的影响力时，对我来说很明显，我们应该折叠所有蛋白质，因为 AlphaFold 不仅准确，而且速度极快。它能在几秒钟内折叠一个蛋白质。然后与剑桥的欧洲生物信息学研究所合作，该研究所托管着科学家使用的许多最大生物学数据库，将全部两亿个蛋白质结构托管在他们的数据库上，使其像谷歌搜索一样简单，只需找到你的蛋白质结构，同时附上机器学习系统对蛋白质结构哪些部分有信心的置信区间，这对生物学家来说非常重要。所以我们把这一切整合在一起。

And then when we decided how we would make the maximum impact with this, it was obvious to me that we should fold all the proteins, because not only was AlphaFold accurate, it was extremely fast. It could fold a protein in a matter of seconds. And then collaborate with the European Bioinformatics Institute in Cambridge, which hosts many of the biggest databases biology databases scientists use, and just host the entire 200 million protein structures on their database and allow it to be as simple as a kind of Google search to just find your protein structure, along with the confidence intervals the machine learning system had about which parts of the protein structure it was confident on, which is very important for biologists to know. So we put that all together.

它本来可以非常有价值。我不知道值多少亿美元。我的意思是，这取决于你如何计算。通过实验做到这一点成本无法估量，但如果保持专有，会非常有价值。但对我们来说，如果只靠我们自己，我们只能触及将这些结构公之于众所能带来的下游影响的皮毛，因为全世界有 300 万研究人员几乎每天都在使用 AlphaFold。几乎每一位生物学家和医学研究人员。没有一个组织能够独自做到这一点。所以这显然是正确的事情。

It could have been very valuable. I don't know how many billions of dollars or whatever. I mean it depends how you calculate it. To do that experimentally would be incalculable cost, but it would have been hugely valuable to keep proprietary. But for us, it felt like we would only be able to scratch the surface of the downstream impact that putting all those structures out in the world could have on our own, because there's three million researchers around the world that use AlphaFold pretty much every day. Almost every biologist medical researcher in the world. There's no way one organization could have done that. So it was obviously the right thing to do.

我们也依赖公共数据来训练 AlphaFold 的早期版本。所以回馈给结构生物学界这个惊人的资源，放大他们已经出色建立的资源，是理所当然的。所以对我来说这甚至不是一个问题。而且谷歌的高管们也热爱科学，完全理解这一点，这很棒。我不认为所有公司都会做出这个决定，所以我也要给他们很多赞扬。那是一次轻松的讨论。

We also had depended on public data to train the first versions of AlphaFold. So it only felt right to give back to that community, the structural biology community, this amazing resource that was amplifying the resource they had spectacularly built. And so it was just not even a question for me. And it was great that also the executives at Google also love science and totally got that. I don't think all companies would have made that decision, so I give them a lot of kudos on that too. That was an easy discussion.

然后我们自己也尝试通过 Isomorphic Labs（Alphabet 的衍生公司）推动下游应用，该公司正在构建更多类似 AlphaFold 级别的突破，将它们整合在一起，有望加速药物发现，将时间从几年缩短到几个月，甚至有一天可能缩短到几周，就像我们对蛋白质结构所做的那样，过去一个结构需要数年，而现在我们可以在几秒钟内完成。

And then we've tried to ourselves push that downstream with Isomorphic Labs, an Alphabet spinout that is building, you can think, several more type of AlphaFold level breakthroughs, putting them together into a way that will accelerate hopefully drug discovery, take it down from years to months, maybe even one day weeks, just like we did with protein structures which used to take years for a single one and then we could do it in seconds.

这是人工智能未来真正令人兴奋的领域之一。我想稍微谈谈你这周早些时候说的话。你这周上了新闻，因为在一次大型谷歌活动上，你说我们正处于奇点的山麓。这句话被大量报道。我理解也许谷歌的公关团队对此并不那么兴奋，但既然你公开说了，你是什么意思？

That's one of the really exciting areas of the future with AI. I want to turn for a minute to something you said earlier this week. You were in the news this week because at a big Google event you said that we're in the foothills of the singularity. It got quite a lot of pickup that line. I understand that maybe the Google press team might not have been so thrilled about it, but since you're out there saying that, what did you mean by that?

是的。所以我在会议结束时说的完整内容是：当我们回顾这个时期时，我想也许从现在起 10 年后，我们会意识到我们正站在奇点的山麓。我选择这个词的意思是，有一种技术叫做 AGI，我们一直称 AGI 为下一代真正的通用人工智能。我相信我们离它只有几年之遥，也许 20 到 30 年，上下浮动一年，这想想都令人震惊。然后我认为这个时代将是一项如此巨大的变革性技术，它实际上将开启一个新的人类纪元。这就是我所说的奇点：它描述了 AGI 出现时我们将进入的时代。所以，我认为今年我们就能感受到，尽管我为此努力了 30 年，但今年随着智能体和工具使用的方式，它开始变得真正有用，虽然仍处于早期阶段，但在人们的工作流程中已经真正有用了。

Yeah. So the full thing I said to sort of close the conference was: when we look back at this time, I think that maybe I'm thinking sort of 10 years from now, I think we will realize that we were standing in the foothills of the singularity. Now what I mean by that and the reason I chose that word is that there's the technology which is AGI, we've been calling AGI this next version of really general artificial intelligence. I believe that we're only a few years away from that, maybe like 20, 30 plus or minus a year, which is astounding to think really. And then the era I think that will be such an enormous transformative technology, it's going to effectively be a new human era. And that's what I mean by the singularity: it's describing the era that we will be in around when the advent of AGI happens. So, and I think we can feel this year, I would say, even though I've been working towards this for 30 years, I think this year with the way that agents are working and tool use, it started to become really useful for, you know, still early days of it, but genuinely useful in people's workflows.

我们可以看到还需要做哪些额外的事情，我们所有领先的实验室都在为此努力。我认为这只是开始，我们还在山脚下。还有很多工作要做。这不是单一的事情，而是多种不同的技术和用例。一些我以为还很遥远的事情现在已经汇聚在一起，让我觉得，总的来说，社会需要听到这个，因为我们没有太多时间准备了。这将产生极其深远的影响。未来仍有待书写，但接下来的几年对于决定其走向以及我们共同希望它成为什么样至关重要。

We can see what extra things need to be done, and all of us, the leading labs, are working on that. I think this is the beginning, but we're still in the foothills. There's a lot more work to do. It's not any one thing; it's several different technologies and use cases. Several things I thought were further out have now come together, making me feel that, in aggregate, society needs to hear this because we don't have long to prepare. It's going to be enormously profound. The future is still to be written, but the next few years will be critical in determining which way it goes and how we collectively want it to look.

如果你看关于人们如何看待 AI 的调查，尤其是在这个国家，现在非常负面。可能比其他国家的负面情绪更重。这背后可能有很多原因：对隐私、国家控制、科技公司规模或就业的担忧。你如何看待这种公众担忧？

If you look at surveys of how people perceive AI in this country in particular, it's very negative right now. It's maybe more negative here than in other countries. There are probably lots of things driving that: concerns about privacy, state control, the size of tech companies, or jobs. How do you think about that public concern?

我认为公众有理由担忧。我对这项技术的几个方面也感到担忧。这是一项双重用途的技术，影响深远。我有时将其描述为工业革命影响的 10 倍，速度快 10 倍——在十年内发生而不是一个世纪。这相当于工业革命的 100 倍，而且可能还是低估了。所以当然，有非常令人兴奋的事情，比如解决所有疾病。当今社会面临的许多挑战——从气候到能源再到疾病——都将得到 AI 的帮助。如果我不认为 AI 即将到来，我会对这些挑战更加担忧。但它会带来很多变化和颠覆，无论是技术、经济还是哲学层面。我们需要深思熟虑，汇集社会各界的意见，而不仅仅是技术专家。技术及其安全性只是其中一部分。我们需要经济学家、社会科学家和人文专家来规划接下来会发生什么。在这里负面情绪的一个原因是其他国家情况不同。例如，在印度，AI 在年轻人中非常受欢迎，因为他们看到了它将带来的民主化机会——获得以前需要去硅谷才能使用的工具。我们正处于一个惊人的时刻，每个人都可以访问前沿实验室正在发生的事情，只延迟几个月。这前所未有。但我认为部分负面情绪也来自我的一些同行表达方式。他们在声明中过于确定，而实际上存在巨大的不确定性。在我看来，什么都没有决定。这是未知的。方向性上我可以告诉你一些事情，但很大程度上取决于未来几年采取的行动以及今天的年轻人——第一代 AI 原生代——会做什么。至少在未来 10 年内，如果你正确使用这些工具，你将拥有超能力。个人可以完成的创造力和项目数量将改变工作的性质——更多的小型创业公司而不是大公司。社会需要团结起来，认真对待这种指数级增长。不仅仅是技术专家，经济学家和其他人需要开始规划这看起来像什么。如果我们处于后稀缺世界，每个人如何受益？只有少数人、公司或国家受益是不对的。它需要广泛地惠及全人类。我们现在就需要答案和具体行动。我打算尽自己的一份力。多年来我一直在思考这个问题，并建立影响力。我们是一个重要的参与者，但只是其中之一。好消息是，所有领先的实验室及其领导者，尽管存在分歧，都担心这些问题。但我们需要更多的论坛来坦诚讨论。公众可能察觉到了一些有偏见的讨论，也许背后有筹集资金等动机。我们需要运用科学方法，对这个历史关键时刻保持严谨和深思熟虑。

I think the public is right to be concerned. I'm concerned about several aspects of the technology. It's a dual-purpose technology, something this profound. I sometimes describe it as 10 times the impact of the industrial revolution, 10 times faster—taking place over a decade instead of a century. That's like a 100x of the industrial revolution, and it's probably an underestimate. So of course, there are super exciting things, like solving all disease. Many challenges facing society today—from climate to energy to disease—will be helped by AI. I'd be much more worried about those challenges if I didn't think something like AI was coming. But it will cause a lot of change and disruption, both technical, economic, and philosophical. We need to think very thoughtfully and bring together all parts of society, not just technologists. The technology and its safety are just one piece. We need economists, social scientists, and humanity experts to chart out what happens next. One reason it's negative here is that it's different in other countries. For example, in India, AI is hugely popular with youth because they see the opportunities it will democratize—access to tools that previously required going to Silicon Valley. We're in an amazing moment where everyone can access what's happening in frontier labs with only a few months' delay. That's unheard of. But I think part of the negativity also comes from how some of my peers articulate things. They're being way too certain in their pronouncements, when there's huge uncertainty. Nothing is decided in my opinion. It's unknown. Directionally, I can tell you some things, but a lot depends on actions taken in the next few years and what the youth of today—the first generation to grow up AI-native—will do. Over the next 10 years, at least, you'll be superpowered if you use these tools right. The amount of creativity and projects an individual can do will change the nature of jobs—more entrepreneurial small things rather than big companies. Society needs to come together, take this exponential seriously. Not just technologists, but economists and others need to start charting out what that looks like. If we're in a post-scarcity world, how does everyone benefit? It's not correct for just a few people, companies, or nations to benefit. It needs to be broad, affecting all of humanity. We need answers now and concrete actions. I plan to do my bit. I've been thinking about this for years, building influence. We're an important actor, but only one. The good news is that all leading labs and their leaders, despite disagreements, worry about these issues. But we need more forums to discuss candidly. The public is detecting slightly skewed discussions, maybe with ulterior motives like raising money. We need to use the scientific method, be rigorous and thoughtful about this critical moment in history.

最后我想说的是，我认为行业和领域有责任更明确地展示这些好处，不只是谈论，而是实际证明。比如在健康、医学、科学领域，这些东西在我看来都是明确的好事，对吧？就像 AlphaFold，但这样的例子还不够多。应该有 20 个 AlphaFold 才对。我们得停止空谈治愈癌症，而是真正去治愈它。所以我认为，这些是向公众展示我们所兴奋的事情所必需的——为什么我们这些对此兴奋的人，包括在座的许多人，为什么我们如此兴奋，为什么我们一生都在为此努力，以及我们如何具体地降低风险，同时实现我们希望看到且社会需要的所有惊人成果。

And then maybe the final thing I would say is I think it's incumbent on the industry and the field to show more unequivocally what the benefits are, and not just talk about them but demonstrate them. So in health, in medicine, in science, these things are all in my view sort of unequivocal goods, right? Like AlphaFold, but there aren't enough examples. They should be 20 AlphaFolds, right? And we've got to stop talking hypothetically about curing cancer and actually cure cancer. So these are the things that I think are going to be needed to demonstrate to the public why those of us who are excited about it, and many of us are in this room, why we are excited about this, why we have spent our whole lives building towards this, and also how we are going to concretely mitigate the risks while enabling all the amazing things we would like to see and I think society needs.

我觉得有很多很好的观点。如果因为 AI 突破而实现了切实的好处，比如对人类健康或药物发现，那可能会在某种程度上改变人们的看法。我喜欢那个建议，即尝试更长远地思考一个在生产力等方面可能截然不同的世界。实际上这很难做到。在社会科学中，很少有人能跳出当前的框架，真正向前展望。我想起凯恩斯在大萧条时期写的那篇关于我们子孙后代经济生活的伟大文章，那是一个罕见的例子。你昨晚说我们现在需要另一个凯恩斯，也许在座的有人能做到。让我问一个你多年来一直在谈论的问题：前沿实验室需要在某种意义上自我监管，有时不发布某些可能对安全构成威胁的技术。现在很明显，实验室正处于激烈的竞争中，他们投入一切，全力以赴。你仍然认为实验室应该自我监管吗？你认为政府应该介入并以某种方式监管 AI 吗？你如何看待当前的动态与你过去思考和谈论的方式相比？

I think a lot of great points there. If there were some tangible benefits realized because of AI breakthroughs, say to human health or drug discovery, that might change people's perception in some ways. And I love the suggestion of trying to think farther out about a world that might look very different in terms of productivity and so forth. It's hard actually to do that. Rarely in social science can people get out of the current frame they're in and actually project way forward. I think of Keynes's great article during the depression when he looks out at the economic lives of our grandchildren. It's a rare case. You were saying last night you thought we need another Keynes right now. Maybe there's someone in the audience who will do that. Let me ask one of the things you've talked about for a lot of years is the need for the frontier labs to in a sense regulate themselves, to sometimes not release certain kinds of technologies that might be threats to safety. Right now it's pretty clear that the labs are in a breakneck competition. They're investing everything, they're going all out. Do you still feel the labs ought to be self-regulating? Do you think the government ought to step in and regulate AI in some way? How do you see the current dynamic relative to the way you've thought about and talked about it in the past?

首先，给一些历史背景。就我们之前讨论的技术发展而言，我认为技术发展得非常惊人，甚至可能比我 20 年前想象的要好。但技术诞生的环境并不理想，远非如此。15 年前、10 年前，我就非常担心这种竞争动态，因为越来越多的人、公司、雄心勃勃的科技领袖意识到我 20 多年来所知道的技术的重要性。我们在房间里讨论过这种竞争动态的危险，不幸的是，由于技术的发展，我们最终陷入了这种局面。所以，如果我能挥动魔杖，我会把 AGI 建在一个像 CERN 这样的研究机构里，让所有最聪明的人互相批评彼此的想法，确保我们严格遵循科学方法，测试并理解每一步。但那样我们就不必等待了。当然，这意味着 AGI 会晚些到来，可能晚 10 年，但我们不需要等到那时才能获得社会效益，因为同时我们可以分离出部分技术，用于专门的系统，比如更多的 AlphaFold 来治疗疾病。这是可行的，因为 AlphaFold 是一个专门的混合系统，它使用了许多通用系统使用的想法，但专门用于蛋白质折叠。这实际上是我的愿景，因为我们当时就是这么做的。但后来聊天机器人改变了这一点，因为——这可能是过去 15 年科学方面唯一让我惊讶的事情——Transformer 在语言方面如此有效，而且你可以将语言分离出来，仅从互联网学习，而不必在现实世界中行动，无论是机器人还是模拟。这非常有趣，这将是另一个话题。我有一些理论：语言比语言学家可能认为的更接地气；人类测试者所做的强化学习反馈带来了一些接地性，因为我们显然扎根于现实世界。所以当我们对某些事情说“是”或“否”时，我们的接地性以非常低带宽的方式最终修改了基础模型的理解。所以发生了一些意想不到的事情，然后这使它成为了一项商业——非常重要的商业技术，可以通过工程和资金进行扩展，这就是你今天看到的。这改变了动态，并创造了我们今天看到的局面，这可能是历史上最激烈的竞争环境。我的意思是，至少在科技行业，也许是有史以来。也许商学院的其他历史学家会告诉我不是这样，但身处其中感觉难以置信地激烈，所有参与者都有同感。然后在此基础上，再加上地缘政治的复杂性。所以还有一场双重竞赛：公司之间的竞赛，对他们来说生死攸关，然后是美中动态和其他地缘政治竞赛。所以这是一个双层竞赛，现在非常棘手。我仍然希望实验室之间能在安全和安保方面进行合作与协调。我们作为实验室负责人确实讨论过这一点。每个人都希望这样，没有人希望发生灾难性的事情。问题是我们处于一种囚徒困境中，任何花更多时间发布或让东西更安全的人——这比直接发布并观察结果更难。所以背叛者有一些优势。

Well, first of all, to give some historical context to this. This is not what—in terms of we talked earlier about how the technology has gone. And I think the technology has gone amazingly, maybe even on the better side of what I imagined 20 years ago. But the environment it's been birthing in is not the ideal, far from it. I was very worried about 15 years ago, 10 years ago about this race dynamic happening as more and more people, more and more companies, more and more ambitious tech leaders realized what I had known for 20 plus years of how important this technology was going to be. We talked about some of this in the room about the dangers of this kind of race dynamic, and unfortunately we've ended up because of the way the technology has gone. So my—if I could have waved a magic wand, what I would have done was build AGI more in a research facility perhaps like CERN, all the best minds helping critique each other's ideas and making sure we were rigorous with the scientific method and testing and understanding each step we took. But then we wouldn't have to wait for that. Of course that means AGI would arrive later, maybe 10 years later, but we wouldn't need to wait for that to get the societal benefits because at the same time we would break off bits of that and use it for specialized systems like more AlphaFolds curing diseases. That can be done right because AlphaFold is a specialized hybrid system that uses a lot of the ideas the general purpose systems use but is specialized to protein folding. That was actually my vision for it because that's what we were doing. But then chatbots changed that because effectively—and that was probably the only surprise to me of the last sort of 15 years on the science side—is how effective transformers ended up being for language and the fact that you could separate language and learn it just from the internet without having to act in the world, either robotics or simulations. It's very interesting, and that would be a whole other topic why that was. I have some theories on that: language is more grounded than linguists probably thought; there's some grounding coming from the reinforcement learning feedback that the human testers are doing because obviously we're grounded in the real world. So when we say yes or no to certain things, our grounding ends up in a very low bandwidth way but still modifies what the foundation model understands. So there were these unexpected things that happened, and then that made it a commercial—very important commercial technology that could be scaled with engineering and money, which is what you see today. That changed the dynamic and created what we see today, which is probably the most ferocious competitive environment there's ever been. I mean certainly in the tech industry, maybe ever. Maybe other historians here from the business school will tell me otherwise, but it feels unbelievably intense being in the middle of it, and it feels like that for all participants. And then on top of that, you layer the geopolitical complexities. So there's also a double race going on: there's the race between the companies, pretty life or death for them, and then there's the US-China dynamic and others, a geopolitical race. So it's a double-layered one, very tricky now. I still have hope that there can be some cooperation and coordination between—we certainly discussed this as lab leaders on the safety elements and the security elements. Everybody wants that, nobody wants something catastrophic to go wrong. The problem is we're in a kind of prisoner's dilemma where anyone who by definition takes more time to release something or make something safer—that's harder than just putting it out there and letting it see what happens. So a defector has some advantage.

这就是经典的逐底竞争动态问题，我们必须以某种方式改变它，而且我认为很紧迫。我认为部分解决方案是某种形式的政府介入。当然，困难在于任何与监管相关的事情都太慢了。每周都有新东西出现。如果我们两年前就某件事进行监管，那现在就像远古历史一样。所以几乎肯定是错的。因此，无论我们设计什么——我对此有一些想法，今年晚些时候可能会谈到——它都需要是动态的，这通常与监管不搭边，对吧？所以它必须轻量、灵活，并能根据最新发展进行调整，从而适应实际风险，而不是某种多年后才发现并非关键问题的感知风险。这对 AI 根本行不通。即使在今天，顶尖科学家也不一定能在所需制衡措施的短名单上达成一致——事实上，我知道他们肯定无法达成一致。这是因为科学尚未定论。部分原因是速度，但也是因为进步的步伐超越了对其的理解。情况就是这样。这是竞争动态的一部分。但我们需要以某种方式重新平衡，我认为需要某种真正智能的监管，它必须是动态的，能快速适应时代，并且可能由领先实验室提供信息，因为他们看到了实际的前线情况。

And that's the classic problem with the race to the bottom dynamic, and we've got to change that somehow, and I think urgently. And I think part of that is some form of government involvement. The hard part there, of course, is that anything to do with regulation is too slow. Like, every week there's something new. If we were to regulate something two years ago, it would be like ancient history now. So it would almost certainly be the wrong thing. So whatever we design—and I have some ideas on this, and I'll probably be talking about this later this year—it needs to be dynamic, which doesn't usually go with regulation, right? So it's got to be light, fleet-footed, and able to be informed by the latest developments, so that it can adapt to where the actual risk is, rather than some kind of perceived risk that turns out not to be the case or not the critical thing, you know, many years before. It's just not going to work for AI. And even today, the leading scientists wouldn't necessarily agree on a short list—in fact, I know they definitely wouldn't agree on a short list of what checks and balances are needed. And that's because the science isn't settled. We're just—it's partly the speed, but also the pace of progress is running ahead of the understanding of it. That's just how it is. It's part of the race dynamic. But we need to somehow rebalance that, and I think some form of really almost smart regulation is required that is dynamic and can adapt with the times very quickly, and probably informed by the leading labs, because they're seeing what's actually at the coalface.

好的，关于如何建立 AI 监管体系，以及如何在不阻碍你提到的那些积极突破、地缘政治、所有创新的前提下做到这一点，还有很多可以讨论的。我们想治愈疾病，那么如何实现好的用例并减少坏的？我期待你今年推出相关计划。那会很棒，也会让我们在校园和各地有很多话题可聊。我想给一些学生提问的机会。

All right, that's—there's so much more to discuss there in terms of the prospects of how you set up a regulatory system for AI and do it in a way that didn't prevent some of the breakthroughs, positive breakthroughs you're talking about, to the geopolitics, all that innovation right? We want to solve the disease, so exactly how do you enable the good use cases and mitigate the bad? I'm looking forward to when you bring out your plan for that this year. I think that'll be fantastic. And that'll give us a lot to talk about here on campus and everywhere. I want to give a chance for some student questions.

你好，Demis。我是 Arinda，商学院二年级学生。我的问题是：你如何平衡推动 AI 前沿与确保健康和科学红利在非洲和全球南方等地公平分配？这些地方需求最大，但部署和研究基础设施最有限。

Hi Demis. I'm Arinda, a second year at the business school. My question is: how do you balance pushing the frontier of AI with ensuring that the health and scientific dividends are evenly distributed in places like Africa and the global south, where the need is the greatest but the infrastructure for deployment and research is most limited?

是的，我们其实经常思考这个问题。这可以回到我举的一个例子：AlphaFold 的问题。我们折叠了所有蛋白质，并将其放在数据库中，世界各地都可以访问。所以这三百万研究人员来自 190 个国家——明确地说，几乎每个国家、每个研究人员。这意味着他们——而且很棒的是，我们在早期播种合作时所做的，就是利用 AlphaFold。我们与 WHO 下属的瑞士 DNDi（被忽视疾病药物倡议）合作，该组织致力于世界上贫困地区的疾病，这些地区没有良好的医疗体系。其中一些疾病被忽视，因为大药厂无法在这些市场赚钱。因此，主要影响这些地区的疾病得不到那么多研究资源。通过与这个研究所和许多当地大学合作，我们能够让他们直接跳过尝试弄清疟疾病毒或寨卡病毒等结构的步骤，而这些原本需要他们进行艰苦的结构生物学研究。他们可以直接以此为起点，立即着手药物研发。这大大加速了整个流程。他们可以获取感兴趣的结构，然后从那里推进。同样，受气候变化影响的作物抗逆性也是如此。我们与 Jennifer Doudna 的研究所和其他许多机构合作，因为很多植物蛋白——我们不知道它们的结构，因为大部分结构工作都集中在人类蛋白质上。所以对于动物或植物，数据要少得多。因此，在这些领域，影响甚至更具差异性。最后我想说的是：如果我们能——我认为这是资本主义引擎可以发挥积极作用的地方——如果我们能让 Isomorphic 正在开发的药物发现平台像我说的那样高效，从几年缩短到几个月，成本从数十亿美元降到数千万甚至数百万美元。那么突然之间，我希望 Isomorphic 能够做到的是：我们治愈那些可能影响世界较富裕地区的可怕疾病，这能赚钱并驱动引擎。但然后我们可以以慈善的方式：公司可以找到治疗那些我们不需要任何回报的疾病的方法，因为它足够快、足够便宜，可以在短时间内完成。所以我认为这就是我梦想如何让 Isomorphic 帮助整个世界的方式。

Yeah, we think about that a lot actually. And that goes back to one of the examples I can give: back to the AlphaFold question, where we folded all the proteins and put that out on databases you could access from anywhere around the world. So these three million researchers come from 190 countries—just to be clear, it's pretty much every country, every researcher. And it means that they—and what was great, what we did actually in the early days of seeding some collaborations, what you could do with AlphaFold. We worked with the DNDi (Drugs for Neglected Diseases initiative), part of the WHO in Switzerland, which works on diseases in the poor places of the world that don't have good health care systems. Some of those diseases are neglected because the big pharma can't make money in those markets. So the diseases that affect primarily those regions don't get as much research resources behind them. What we were able to do, in collaboration with this institute and many universities on the ground, is jump them straight to not needing to try to figure out the structures of malaria virus or Zika virus or something like that, which they would have had to do all the painstaking structural biology. They can just start that as a given and work straight away on the drugs. So that allows them to speed up massively the whole process. They can take the structure of interest and move forward from there. Same with crop resilience affected by climate change. We work with Jennifer Doudna's Institute and many others on these things, because lots of plant proteins—we didn't know what the structures were, since most structural work has gone into human proteins. So for animals or plants, there's a lot less data out there. So it's even more differentially impactful in those types of areas. And then the final thing I would say is: if we can—and I think this is where the capitalist engine can actually work for good—if we can make the drug discovery platform that we're working on at Isomorphic as efficient as I'm talking about, down from years to months, so instead of costing billions of dollars it costs tens of millions, maybe single millions. Then suddenly, what I'm hoping we'll be able to do with Isomorphic is we cure these terrible diseases that maybe affect the richer parts of the world, that makes money and fuels the engine. But then we can do sort of philanthropically: the company could find cures to diseases where we don't need to make any return, because it's fast enough and cheap enough that it can just be done in a short amount of time. So I think that's sort of my dream for how I can make Isomorphic help the whole world.

你好，Demis，非常感谢你抽出时间与我们交谈。我叫 Miki，是多尔可持续发展学院的大四学生。你广泛描述了 AGI 如何成为人类最具变革性的技术，我很好奇：责任或你如何看待 AGI 带来的社会影响，以及这种智力开拓和生产力，特别是考虑到这将如何重新定义和重塑我们今天试图解决的挑战，以及可能带来的下游效应。谢谢。

Hi Demis, thank you so much for taking the time to talk to us. My name is Miki. I'm a senior in the Doerr School of Sustainability. You've described extensively how AGI could be humanity's most transformative technology, and I'm just curious: the responsibility or how you think about the societal impacts alongside this intellectual pioneering and productivity that AGI presents, particularly when thinking about how this is going to redefine and reshape the challenges that we're trying to solve today, but also the downstream effects that that could bring forward. Thank you.

谢谢你的问题。

Yeah, thanks for your question.

我一直在思考这个问题，从一开始就在想，因为我们是在为成功做规划。尽管十五二十年前这看起来非常不可能，但这就是为什么我喜欢做这样的演讲，在这样的场合与人交流。现在这有点像是一种号召，非常紧迫，我们必须真正思考二阶后果。在座的许多人，尤其是人文学科的各位，我认为现在是你们大显身手的时候了。我们必须先把技术做好，但接下来是经济学问题，如果那也解决了，还有关于人类境况的哲学问题。我非常乐观，是一个谨慎的乐观主义者。我坚信人类的聪明才智，尤其是在压力之下。人类总是在关键时刻找到解决办法，而现在就是关键时刻。但我们真的需要开始认真对待这件事。技术人员已经在认真对待，但社会的其他部分也需要跟上。经济学家，我每次和他们谈论正在发生的事情时总是有点惊讶，他们相当怀疑。‘这体现在 GDP 的哪里？’而我说，看，这是工业革命的十倍。我们现在能开始规划了吗？如果我们把技术做好，我们将首次进入一个非零和的世界，这是人类历史上第一次。这怎么可能不需要一种新的经济体系呢？必须要有。而且我不认为是我们尝试过的任何一种，因为它们都是在零和、资源稀缺的世界里产生的。我指的是星际旅行，利用太阳系中的所有资源，而不仅仅是地球上有限的那些。如果我们能在未来十、二十、三十年内把技术做好，这一切真的会发生。在那之后，还有更难的问题：我们想如何发展我们的社会？什么是美德？什么是意义？什么是目的？这需要很多伟大的哲学家。所以我对这些领域的人们的呼吁是，现在正是最激动人心的时刻，只要你们理解并真正投入到正在发生的事情中。

I think about this all the time and have done from the beginning because we were planning for success. Even though it seemed very improbable back 15-20 years ago, this is why I like doing talks like this and meeting folks in these kinds of places. It is a bit of a call to arms now; it's very urgent that we really think about the second-order consequences. Many of you in the room and many of you in the humanities subjects, now is your time in my opinion. We got to get the technology right, but then there's the economics question, and if we get that right, there's the philosophical questions about the human condition. I'm very optimistic, a cautious optimist. I'm a big believer in human ingenuity, especially when the pressure's on. Humanity has always figured it out when the chips are down, and they are now. But we really need to start taking that seriously. The technologists are taking it seriously, but the other parts of society need to as well. Economists, I'm always a little astounded when I talk to them about what's happening; they're pretty skeptical. 'Where's it coming in the GDP?' And it's like, look, it's 10 times the industrial revolution. Can we start planning for that now? We'll be in a world, for the first time if we get the technology right, where we're nonzero sum for the first time in humanity's existence. How can that not need a new type of economic system? It has to. And I don't think it's any of the ones we've tried because they were all done under the guise of a zero-sum, scarce world. I'm talking about traveling to the stars and utilizing all the resources out in the solar system, not just the limited ones on Earth. That really is going to happen if we get the technology right in the next 10, 20, 30 years. Then after all of that, there's the even harder question of how do we want to evolve our society and what is virtuous, what is meaning, what is purpose. That's going to need lots of great philosophers. So my appeal to people in those fields is now could not be more of an exciting time if you're working on those types of projects, as long as you understand and lean into what's actually happening.

这是对大学的一个很好的呼吁。好的，再来一个学生问题。

It's a good charge to university. Okay, one more student question.

你好，Demis，我是 Janai，MBA 二年级学生。我的问题是，你希望 AI 在这一生中不要触碰什么？从你的角度来看，你保守着什么秘密？谢谢。

Hi Demis, I'm Janai. I'm a second year MBA student. My question to you is what do you not want AI to touch in this lifetime and what do you hold secret from your perspective? Thanks.

这是个好问题。AI 将是一种完全通用的技术。你可以把它看作图灵机。我们的思维是完全通用的，正如图灵所示，任何可计算的东西，图灵机都能计算。宇宙中大多数非量子的事物都是可计算的。这是一个很大的通用集合，我们的思维可以转向它。因此我们建立了现代文明。但我们正在构建的这些系统也将具有图灵完备的能力。我想说的是，未来会有一些非常大的问题，我认为我们最好花更多时间来思考。一个例子是意识。从哲学和神经科学的角度来看，这还不是一个明确的问题，尽管我们都有直觉。我的感觉是当前的系统没有表现出意识，但其他人不同意。我的建议是，我们首先把系统构建成工具，智能工具。这已经是一个足够的挑战，因为这已经是 AGI 了。然后利用这些工具，我们应该研究神经科学和哲学，对意识之类的东西给出更严格的定义。然后根据这个定义进行测试，也许由社会决定是否要跨越第二条卢比孔河，即制造出看起来有意识的实体。我们可能不想做这个决定。我认为智能和意识是可分离的。你不需要为了拥有智能系统而这样做。这是一个选择。我的观点是，最好分两步走，这两步对人类来说都是巨大的，而不是将两者混为一谈。

That's a great question. AI is going to be a fully general technology. You can think of it as a Turing machine. Our minds are fully general, so as Turing showed, anything computable, a Turing machine can compute. Most things in the universe, non-quantum things, are computable. That's a large set of general things we can turn our minds to. Hence we built modern civilization. But these systems we're building are also going to be Turing powerful. One thing I would say is there are very big questions to come that I think it would be better if we took more time over. One example is consciousness. It's not a well-posed problem from philosophy and neuroscience, although we all have intuitions. My feeling is current systems don't exhibit consciousness, but others disagree. What I would recommend is that we build our first systems as tools, intelligent tools. That's enough of a challenge already because that's already AGI. Then using those tools, we should study neuroscience and philosophy and come up with a more rigorous definition of things like consciousness. Then test things against that and maybe as society decide if we want to cross the second Rubicon of trying to make entities that seem conscious to us. We may not want to make that decision. I think intelligence and consciousness are dissociable. You don't have to do that to have an intelligent system. It's a choice. My view is it'd be better to take that as two steps, both enormous for humanity, rather than conflate the two.

Demis，我们礼堂里有很多学生。如果你回到学校，你会怎么考虑学什么？你对他们的职业规划有什么建议？

Demis, we've got an auditorium with many students in it. If you were back in school, how would you be thinking about what you would be studying? What would be your advice on how they should think about their careers?

如果我现在回到大学，我会非常兴奋。我的建议是：你们中那些学习科学、STEM 学科、数学和计算机科学的人，继续学这些。我认为如果你理解这些工具是如何构建的以及它们的能力，你就能更好地利用它们。至少在未来十年内都是如此。同时，我也要拥抱它，而不是希望它消失。精灵不会回到瓶子里。拥抱这些工具能做什么。领先的实验室忙于制造工具，我们可能只触及了它们实际能力的皮毛。即使是今天的工具，也存在能力过剩。

I would be really excited if I was back at college now. My recommendation would be: those of you doing science and STEM subjects, mathematics and computer science, still do those things. I think you'll be able to take better advantage of these tools if you understand how they are put together and what they're capable of. That's going to be true for the next 10 years at least. I would also lean in, not wish it away. The genie is not going back in the bottle. Lean into what these tools can do. The leading labs are so busy making the tools that we have probably only scratched the surface of what they can actually do. Even today's tools have a capability overhang.

如果你能弄清楚如何将它们与其他事物结合，或者与你擅长的另一个领域配对，这些东西能发挥的潜力是巨大的。以有趣的方式将它们融入你的工作流程。你拥有这些工具，它们是任何人能得到的最强大的工具，就掌握在你手中。作为个人，你能做的事情要多得多。我认为它应该释放创造力。比如你们中那些学习人文学科、产品设计或商业的人，以前可能没有编程技能，但现在你不需要了；你可以利用这些工具将脑海中的许多想法实现出来。但我认为，对于程序员，那些擅长编程的人，如果你精通编码，你能做的项目规模可以提升 100 倍。所以我认为它既实现了民主化，也让那些在专业领域的人受益。因此，我认为这是一个了不起的时代，但同时也令人担忧，因为一切都会改变。这是我唯一能确定告诉你的。未来十年一切都会改变，可能比人们想象的还要多。但每当有如此巨大的变化时，也伴随着巨大的机遇。必然如此，对吧？世界真的尽在你的掌握之中。我有点羡慕你们中的一些人，因为你们是第一代 AI 原住民，就像我这一代是计算机和互联网原住民一样。最终，未来世界如何构建掌握在你们手中，在座的各位学生。我认为这是一个非常激动人心的时代，如果你以正确的方式、从正确的角度、带着丰富的想象力和创造力去思考的话。但我认为这一点始终成立，也许在像这样巨变的时期更是如此，它放大了这一点。

There's so much potential these things can do if you figure out how to pair them with other things or compare it with another domain you're expert in. Build it into your workflow in an interesting way. You have those tools. They're the most powerful tools anyone's got. You have them in the palm of your hand. There's so much more you can do as an individual. I think it should unleash creativity. Like those of you studying humanities or product or business maybe you didn't have coding skills before but you don't need them; you can produce a lot of what's in your mind now using these tools. But I think also the coders, the people who are expert at that, could do 100x more in terms of the size of project you can do if you're expert at coding. So I think it enables both the democratization and the people that are specialized in those areas. So I think it's an amazing time, but it's also worrying because everything's going to change. That's the only thing I can tell you for sure. Everything is going to change in the next 10 years, probably more than people assume. But anytime there's enormous change like that, there's enormous opportunities. There has to be, right? And the world's sort of your oyster really. And I kind of envy some of you now because you're the first generation that will be AI native, just like my generation was computer and internet native. And it's going to be in your hands in the end, the students in the room, how that future world gets built. And I think it's a very exciting time if you think about it in the right way from the right angle and with a lot of imagination and creativity. But I think that's always been true, and maybe it's more so now in periods of enormous change like this that accentuates it.

我们昨晚谈到，在一个充满变化、你不太清楚未来会怎样的时期，你必须能够适应并拥有广泛的知识领域。这将是博雅教育的黄金时代。

We were saying last night that in a period of a lot of change where you don't quite know what the future holds, but you have to be able to be adaptable and have broad domain of knowledge. It's going to be a golden era for liberal education.

我认为最重要的是确保你加倍发挥自己的主观能动性。未来仍待书写。我会这么说，所以不要听信任何说未来已定的人。

I think the main thing is to just make sure you double down on your own agency. The future's still to be written. I would say that, so don't listen to anyone who says it's not.

是的。

Yeah.

Demis，感谢你参加我们的节目。这太棒了。

Demis, thank you for joining us. This is amazing.