# Demis Hassabis Thinks We’re in the ‘Foothills of the Singularity’
- Transcript ID: `543d63952a4a9ee0`
- Source tier: `official_authored_text`
- Original language: `en_or_source_language_unlabeled`
- Translation status: `agent_translated_reviewed` (103/103 chunks)
- Original source: https://www.gsb.stanford.edu/insights/demis-hassabis-thinks-were-foothills-singularity
- Raw evidence: `data/transcripts/web/demis-hassabis-stanford-gsb-2026-06-18.json`
> 中文是派生翻译，英文是留存的源文本。中文未复核时，以英文和原始音视频为准。
## paragraph 1
**中文**
Google DeepMind 首席执行官谈到破解智能、免费开放 AlphaFold，以及他为何相信 AGI 已近在眼前。
**English / Original**
The CEO of Google DeepMind discusses solving intelligence, giving away AlphaFold, and why he believes AGI is just around the corner.
## paragraph 2
**中文**
2026 年 6 月 18 日
**English / Original**
June 18, 2026
## paragraph 3
**中文**
收听：Apple / Spotify / 亚马逊
**English / Original**
Listen: Apple / Spotify / Amazon
## paragraph 4
**中文**
2010 年，当 Demis Hassabis 向几位风险投资人推介 DeepMind 时，其商业计划大胆得近乎荒诞。他在斯坦福大学商学院与斯坦福大学校长 Jonathan Levin 的对谈中回忆：“第一步：破解智能。第二步：用它解决其他一切问题。人们当时相当困惑，但我们是认真的。”
**English / Original**
When Demis Hassabis pitched DeepMind to a few venture capitalists back in 2010, the business plan was almost comically audacious. “Step one: Solve intelligence. Step two: Use it to solve everything else,” he recalls in a conversation at Stanford Graduate School of Business with Stanford University President Jonathan Levin. “And people were quite confused. But we really meant it.”
## paragraph 5
**中文**
十六年后，Hassabis 表示，这项计划的“总体轨迹”进展得“好得令人难以置信”。这位从国际象棋神童一路成为电子游戏开发者、神经科学家、诺贝尔奖得主和 AI 先驱的人，如今正致力于打造“科学的终极工具”；他此前决定免费开放能够预测蛋白质结构的突破性 AI 系统 AlphaFold。“这显然是正确的选择，”他说。
**English / Original**
Sixteen years later, the “broad arcs” of that plan have gone “unbelievably well,” says Hassabis, a chess prodigy turned video game developer turned neuroscientist turned Nobel Prize-winning AI pioneer. Today he’s on a mission to create “the ultimate tool for science,” building on his decision to give away AlphaFold, the groundbreaking AI system that predicts the structures of proteins. “It was obviously the right thing to do,” he says.
## paragraph 6
**中文**
人类可能创造的最强大的技术正在进入历史上地缘政治竞争之上的“最残酷的竞争环境”。哈萨比斯担心顶级人工智能实验室之间的“逐底竞争”动态，并呼吁“敏捷”地进行“智能监管”以跟上步伐。其中的利害关系是深远的：通用人工智能的影响力可能是“工业革命的十倍……速度快十倍”。
**English / Original**
The most powerful technology humanity may ever create is arriving in “the most ferocious competitive environment” in history layered atop geopolitical rivalry. Hassabis worries about a “race to the bottom” dynamic among top AI labs and calls for “smart regulation” that is “fleet-footed” enough to keep pace. The stakes are profound: Artificial general intelligence could have “10 times the impact of the Industrial Revolution… 10 times faster.”
## paragraph 7
**中文**
哈萨比斯说，这个未来就在眼前：“十年后，我想我们会意识到我们现在正站在奇点的山麓上。”
**English / Original**
That future, Hassabis says, is just around the corner: “Ten years from now, I think we’ll realize that we were standing in the foothills of the singularity now.”
## paragraph 8
**中文**
AI@GSB 是斯坦福大学商学院 (GSB) 院长的应用人工智能项目，它与斯坦福大学医学院共同主办了一场与 Google DeepMind 联合创始人兼首席执行官 Demis Hassabis 的对话，讨论了人工智能的前沿及其对我们生活、工作和繁荣的意义。
**English / Original**
AI@GSB, the Dean’s Applied AI initiative at the Stanford Graduate School of Business (GSB), and Stanford Medical School hosted a conversation with Demis Hassabis, Co-founder and CEO of Google DeepMind, on the frontier of artificial intelligence and what it means for how we live, work, and flourish.
## paragraph 9
**中文**
收听并订阅完整成绩单
**English / Original**
Listen & Subscribe Full Transcript
## paragraph 11
**中文**
注意：此文字记录由自动化系统生成，为了清晰起见，经过轻微编辑。它可能包含错误或遗漏。
**English / Original**
Note: This transcript was generated by an automated system and has been lightly edited for clarity. It may contain errors or omissions.
## paragraph 12
**中文**
詹妮弗·阿克：大家好。我是詹妮弗·阿克 (Jennifer Aaker)，斯坦福大学商学院泛大西洋投资集团教授。
**English / Original**
Jennifer Aaker: Hi everyone. I’m Jennifer Aaker, the General Atlantic Professor at Stanford Graduate School of Business.
## paragraph 13
**中文**
迈克尔·麦克道尔：我是迈克尔·麦克道尔，GSB 的一名制片人。今天我们由 GSB 进行现场直播。
**English / Original**
Michael McDowell: And I’m Michael McDowell, a producer here at the GSB. And today we’re live from the GSB.
## paragraph 14
**中文**
詹妮弗·阿克：没错。你将听到我的朋友兼同事、斯坦福大学校长 Jon Levin 和 Google DeepMind 首席执行官兼联合创始人 Demis Hassabis 之间的对话。
**English / Original**
Jennifer Aaker: That’s right. You’re about to hear a conversation between my friend and colleague Jon Levin, President of Stanford University and Demis Hassabis, CEO and Co-founder of Google DeepMind.
## paragraph 15
**中文**
迈克尔·麦克道尔：詹妮弗，我认为可以公平地说，是你促成了这次谈话。那么请告诉我你为什么邀请戴米斯加入 GSB？
**English / Original**
Michael McDowell: Jennifer, I think it’s fair to say that you made this conversation happen. So tell me why you invited Demis to the GSB?
## paragraph 16
**中文**
詹妮弗·阿克：原因有很多。但说实话，最吸引我的不是人工智能本身，而是 Demis。在 DeepMind 之前，在 AlphaGo 之前，他的论文探讨了记忆与想象力之间的神经学关系。他表明，海马体受损的患者不仅失去了回忆过去的能力，而且还失去了构建未来丰富的心理场景的能力。要想想象你必须活过。
**English / Original**
Jennifer Aaker: So many reasons. But honestly, the thing that drew me the most isn’t AI itself, it’s Demis. Before DeepMind, before AlphaGo, his dissertation explored the neurological relationship between memory and imagination. He showed that patients with hippocampal damage lost not only the ability to recall the past, but to construct any rich mental scene moving forward. To imagine you have to have lived.
## paragraph 17
**中文**
Michael McDowell：这在 2007 年是一个重大突破，考虑到 Demis 继续构建的内容，它提出了一个非常深刻的问题。
**English / Original**
Michael McDowell: This was a big breakthrough back in 2007 and it raises a pretty profound question given what Demis went on to build.
## paragraph 18
**中文**
詹妮弗·阿克：确实如此。如果人工智能越来越多地处理规划、记忆、场景构建，那么人类的想象力会发生什么？我们最想保留、深化和扩大哪些能力？
**English / Original**
Jennifer Aaker: It really does. If AI increasingly handles planning, remembering, scenario building, what happens to human imagination? What capacities do we most want to preserve, deepen and expand?
## paragraph 19
**中文**
Michael McDowell：是的，问题不只是我们可能失去什么，也在于由此会出现哪些新的可能。
**English / Original**
Michael McDowell: Yeah, it’s not just what we might lose, but what becomes possible.
## paragraph 20
**中文**
詹妮弗·阿克：当然。我思考的是科学进步、经济赋权、创造潜力、扩大的能动性以及最终更加充实的人类生活。在微观层面上，我认为这是蓬勃发展，这意味着感觉充满活力、富有创造力、相互联系和目标明确。如果我们能够实现这一目标，那么在组织、社区和社会的宏观层面上，真正令人兴奋的事情就会开始发生。
**English / Original**
Jennifer Aaker: Absolutely. I’m thinking about scientific advancement, economic empowerment, creative potential, expanded agency, and ultimately fuller human lives. At the micro level, I think of this as flourishing, which means feeling fully alive, creative, connected, and purposeful. And if we’re able to accomplish that, really exciting things start to happen at the macro level across organizations, communities and society.
## paragraph 21
**中文**
迈克尔·麦克道尔：我一直在思考戴米斯说过的话，你们都会听到，那就是未来仍然是不成文的。
**English / Original**
Michael McDowell: I keep thinking about something Demis said, which you’re all about to hear, which is that the future is still unwritten.
## paragraph 22
**中文**
詹妮弗·阿克：这一切都不是不可避免的。我认为戴米斯问了许多与我相同的问题，并且也许比任何人都更了解我们现在做出的选择不仅会改变我们的未来，还会改变人类存在的本质。这就是为什么我如此兴奋地把他带到斯坦福大学，在这里伟大的想法与实际应用相结合。
**English / Original**
Jennifer Aaker: None of this is inevitable. I think Demis is asking many of the same questions I am, and understands maybe better than anyone how the choices we make now may alter not only our future, but the nature of human existence. And that’s why I was so excited to bring him here to Stanford, the place where big ideas meet real application.
## paragraph 23
**中文**
迈克尔·麦克道尔：就此而言，非常感谢你，詹妮弗。
**English / Original**
Michael McDowell: On that note, thank you so much, Jennifer.
## paragraph 24
**中文**
詹妮弗·阿克：谢谢你，迈克尔。
**English / Original**
Jennifer Aaker: Thank you, Michael.
## paragraph 25
**中文**
迈克尔·麦克道尔：这是黛米斯·哈萨比斯与乔纳森·莱文的对话。
**English / Original**
Michael McDowell: Here’s Demis Hassabis in conversation with Jonathan Levin.
## paragraph 26
**中文**
乔纳森·莱文：戴米斯，很高兴你来到斯坦福大学。谢谢你——
**English / Original**
Jonathan Levin: Demis, it’s great to have you here at Stanford. Thank you for —
## paragraph 27
**中文**
Demis Hassabis：很高兴来到这里，感谢大家的到来。
**English / Original**
Demis Hassabis: It’s fantastic to be here, thanks everyone for coming.
## paragraph 28
**中文**
乔纳森·莱文：真的很感谢你这样做。所以我要问你一些问题。我们会收到学生的一些问题，并期待听到您的想法。最近你被记录了很多，一部电影，一本书，所以很多人都听说过你的轨迹。这是相当了不起的。国际象棋神童、视频游戏开发者、科学家、科技企业家和领导者、诺贝尔奖获得者。这只是你职业生涯的前半段。所以，如果你想尝试为你做过的所有这些不同的事情画一条贯穿线，那会是什么？
**English / Original**
Jonathan Levin: Really appreciate you doing this. So I’m going to ask you some questions. We’ll have some questions from students and looking forward to hearing your thoughts. You’ve been chronicled a lot recently, a movie, a book, so many people have heard about your trajectory. It is quite remarkable. 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?
## paragraph 29
**中文**
杰米斯·哈萨比斯：嗯，我认为实际上有几条主线与看似无关的主题。首先，我一直非常喜欢在创造力和技术的交叉点上工作，而且解释得非常广泛。所以实际上游戏行业，视频游戏行业，这是我在 90 年代职业生涯的第一个非常早期的部分，是任何行业中最具创意的空间之一，它利用尖端技术与艺术和设计来创造一种全新的娱乐媒体。那真是一段美妙的时光。事实上，我职业生涯中最有趣的时光是在 90 年代初。我做的国际象棋和神经科学，所有我尝试过的事情……我从这个想法中得到了这样的想法：研究人工智能和通用人工智能是人们从很小的时候就可以投入职业生涯的最重要和最有趣的事情。
**English / Original**
Demis Hassabis: 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 and very broadly construed. So actually the games industry, the video games industry, which is 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 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… I had from this idea of working on AI and AGI being the most important thing and most interesting one thing one could spend your career working on also from a very early age.
## paragraph 30
**中文**
所以，作为一个青少年，我可能读了太多的科幻小说，读了哥德尔、埃舍尔、巴赫之类的书，以及我的一些科学英雄图灵和费曼等的传记。因此，所有这些都激励我尝试深入了解我们周围的世界。然后构建人工智能是我对尝试构建终极科学工具这一使命的表达。我已经尝试过，因为生命短暂，我尝试重新利用和重新利用我所获得的每一次经验，以服务于我 30 多年来更大的北极星任务。因此，我的国际象棋训练是我思考商业、组织事物和规划的方式，以及我认为我如何能够将雄心勃勃的计划分解为更小、更易于管理的步骤。我想说，这一切都来自国际象棋思维。
**English / Original**
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, 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 North Star mission that I’ve had for more than 30 years. So 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 small and more manageable steps. That all comes from chess thinking, I would say.
## paragraph 31
**中文**
然后使用游戏，首先，构建游戏，学习大规模的工程项目，运营公司，初创公司，然后将这种创造力与工程融合。事实上，这就是我们今天使用人工智能所做的事情。这是一门工程科学。所以你正在将创造性工作、科学工作与非常核心的尖端工程融合在一起。这样大家就一起服务了。最后是游戏，众所周知，我们在 DeepMind 早期使用游戏作为测试算法思想的完美试验场，其中最著名的可能是 AlphaGo，我想我们刚刚度过了它的 10 周年纪念日，现在回过头来看，也许是现代人工智能时代的开始。
**English / Original**
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.
## paragraph 32
**中文**
Jonathan Levin：2010 年前后，你投身 AI、创立 DeepMind 时有一个非常宏大的愿景：先破解智能，再解决其他一切问题。现在进展如何？再具体一点：哪些按计划发生了，哪些偏离了计划？
**English / Original**
Jonathan Levin: 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?
## paragraph 33
**中文**
杰米斯·哈萨比斯：嗯，我的意思是，它的大致弧线已经消失了，也许好得令人难以置信。当我们在 2010 年创立 DeepMind 时，你能想象我们曾经尝试去英国寻找风险投资人吗？当时英国的风险投资人并不多。以此作为商业计划，实际上就是第一步，解决智能问题，第二步，用它来解决其他所有问题。人们很困惑。但我们确实是这么想的。实际上，我们可以回到准确使用该使命宣言，因为通过解决智能，我们的意思是构建 AGI。理想情况下，在构建 AGI 的过程中了解智能的本质，也许使用 AGI 来帮助我们更好地理解我们自己的大脑和思想，比如意识的本质，什么是创造力，梦想，所有这些心灵的深层奥秘。
**English / Original**
Demis Hassabis: Well, the broad arcs of it have gone, I mean, unbelievably well, perhaps. When we started DeepMind in 2010, can you imagine we used to try to go to VCs in the UK, 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 may could go back to exactly using that mission statement because 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, things like the nature of consciousness, what is creativity, dreaming, all of these deep mysteries of the mind.
## paragraph 34
**中文**
我研究神经科学的原因之一是尝试从我们对大脑的理解中学习，作为算法思想的灵感。因此，第一步是尝试构建 AGI，然后我们总是牢记发生了什么，这当然是一种通用技术，也许是通用技术。如果它以正确的方式构建，那么它是一个非常通用的学习系统，那么它的应用范围会有什么限制？它几乎可以应用于任何事物，这就是梦想，我认为这已经得到了证实。我特别想到了第二步，即推动科学和医学的发展。这就是我用它来解决其他所有问题的意思。我指的是科学中的所有重大问题。
**English / Original**
And one of the reasons I studied neuroscience was 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 then 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.
## paragraph 35
**中文**
我对所有这些都着迷，时间的本质，现实的本质，也许这是最基本的一个。我在学校时就喜欢物理。那是我最喜欢的科目。我认为当你对重大问题感兴趣时，你最终可能会研究物理。但我决定的原因是有太多有趣的大问题。那么，一个人如何在一生中尝试解决所有这些问题呢？在我看来，这意味着构建新工具并帮助我们的是最好的科学家、最好的专家，以便在他们正在解决的领域以及他们正在解决的重大问题和重要问题上取得更快的进展。当然，人工智能本身也是一件令人着迷的神器，可以称之为科学对象，本身就值得研究。这几乎是一个新领域。
**English / Original**
I was fascinated by all of them, the nature of time, the nature of reality, maybe that’s the most fundamental one. And 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? And that meant, in my view, building new tools and to aid us are the best scientists, the best experts to make much faster progress in the fields that they were tackling and the big questions and important questions that they were tackling. And then of course, AI in itself is also a fascinating artifact in itself, scientific object one could call, worthy of study itself. It’s almost a new field.
## paragraph 36
**中文**
对我来说，这感觉像是一个人一生中最迷人、最重要的事情，即使没有成功，我也会一直这样做，我会在学术界或任何地方找到一些方法来做到这一点。这就是我一直计划一生致力于的事情。我之前做的所有这些事情都是不同的表达方式，收集经验和我认为能够在 2010 年尝试像 DeepMind 这样的东西的知识，当时我们觉得我们已经准备好取得快速进展。当然，第二部分，用它来解决其他所有问题现在比科学和医学更广泛，尽管这是我试图亲自完成工作以及管理整个组织的领域。但显然，除了科学和医学之外，这对于生产力和世界上许多其他事物来说都将是惊人的。
**English / Original**
To me, it would felt 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 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 that 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.
## paragraph 37
**中文**
Jonathan Levin：当你在 DeepMind 构建这些不同的模型时，你从游戏开始，然后进入科学领域。是否有特定的时刻……我的意思是，你一开始就充满信心，但我很好奇是否有特定的时刻你看到这实际上可以与 AlphaGo 配合使用，并更多地与 - [听不清] 一起玩。
**English / Original**
Jonathan Levin: As you’ve been building these different models at DeepMind, you started with games and then you went into science. Were there particular moments where that… I mean, you started with a lot of conviction, but I’m curious if there were particular moments where you saw this is actually going to work with the AlphaGo and playing more with the — [inaudible].
## paragraph 38
**中文**
黛米斯·哈萨比斯：是的。有很多时候我认为这行不通，这么说吧。我记得很清楚的一些是，我们从游戏开始，因为它们是独立的。显然，它们是由其他人设计的，旨在为其他人带来挑战或乐趣。我喜欢游戏。它们通常是许多现实世界场景的缩影。如果你想到围棋、扑克或国际象棋，我经常认为我在 MBA 或商学院课程中的课程之一是学习这些类型的游戏和外交的游戏模块。它们都有现实生活中最好的游戏真正有趣的方面。而且显然你可以在安全的情况下练习很多次。这就是我认为游戏真正有用的地方。这也适用于正在学习的人工智能系统。它们是整洁的环境，具有挑战性，并且具有明确的目标函数，这对于我们早期的强化学习也非常重要。
**English / Original**
Demis Hassabis: Yeah. There were many moments where I thought it wasn’t going to work, put it that way. 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. If you think of Go or Poker or chess, I often thought as one of the think courses I’d have in an MBA or business school course 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 safe scenario. That’s what I think games are really useful for. And that applies to AI systems that are learning too. They’re neat environments, they’re challenging and they have clear objective functions, which also was very important for our early days of reinforcement learning.
## paragraph 39
**中文**
几乎没有人使用强化学习来解决任何类型的规模化问题。这显然是一门学科，但它主要用于解决诸如小网格世界之类的玩具问题。目前尚不清楚它能否扩展到任何重大领域。所以我想说，我们从世界流行的最著名但最基本的游戏开始，这些游戏是 20 世纪 70 年代的 Atari 游戏。我们从最简单的游戏开始，那就是乒乓球，只有球棒和球，只有两个球棒和一个球。还有一个内置的人工智能系统。它并不是真正的人工智能系统，而是一个控制对手并使用游戏中有关球所在位置等所有信息来移动球棒的内置系统。我们想要做的是你可以只通过屏幕上的像素来玩乒乓球吗？因此，原始数据，原始视觉输入，没有其他信息，没有特权信息，无法访问程序内部有关球的位置或速度等的信息，显然程序知道这些。
**English / Original**
Almost no one had used reinforcement learning for any kind of scaled up problem. It was obviously 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. And so we started with, I would say, the most famous set of but most basic games that had become world popular, which were Atari games from the 1970s. And we started with the simplest game of all, which was Pong, just the bat and the ball, just 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. 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 where the ball is or the speed of it and so on, which obviously the program knows.
## paragraph 40
**中文**
但我们没有把这些信息提供给 DQN，也就是我们的 Atari 系统；它只能看到屏幕上的 2 万个像素。如今这似乎微不足道，可在 2010 年却是巨量输入，更不用说还要乘以连续处理的所有画面帧。有一段时间感觉足足过了六个月——也许其实只有两个月——我们在 Pong 中一分都拿不到。系统只会胡乱移动球拍，我们甚至怀疑它究竟能否控制球拍；它一次次以 0 比 21 输给内置程序。我们尝试了几条技术路线，却几乎没有钱，几百万美元融资就是全部资金，现金余量不断耗尽；如今这点钱甚至可能不够支付一名实习生。
**English / Original**
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. So it was jerking the bat around. It was like, “Oh, is it ever going to even be able to control the bat?” And of course we 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 couple of million dollars of funding that we had, which wouldn’t even cover an intern these days, which is good for all of you, students, was our entire funding.
## paragraph 41
**中文**
我们不领薪水，资金却快耗尽了。我心想：也许我们还是早了 10 年，甚至早了 20 年。随后它奇迹般拿到一分；起初我们怀疑只是运气，接着它不断得分、开始赢下比赛，我们意识到：“好，终于起飞了。”做机器学习的人都知道，一旦找到立足点，通常就能沿着改进路径不断攀升。我认为 AI 的历史正是如此：只要先让系统跑起来，通常就能继续优化。Atari 因而成为我们的首项重大成果；我们发表在《Nature》的首篇论文，确实是首个达到规模化水平的深度强化学习模型。
**English / Original**
And we were taking no salary and it was running out, the 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, and those of you working in machine learning will know this, if you get a foothold, you can usually heel-climb your way out of that. That’s been the history of AI, I would say, right? Once you have something working, there’s usually a way of optimizing it more. And that’s what turned out with Atari, so that was our first big result. And our first nature paper was really the first deep reinforcement learning model, certainly at scale.
## paragraph 42
**中文**
结合深度学习来学习领域并处理感知输入和输入的复杂性，找到其中的模式，然后在此基础上建立强化学习来做出决策和进行规划。当然，最终的结果是 AlphaGo，这一直是我们的目标。戴夫·西尔弗（Dave Silver）和我是该项目的负责人，我们是剑桥大学的本科生朋友，我们从本科生起就开始讨论这个问题，大约在 90 年代中期，深蓝与卡斯帕罗夫的比赛发生在我们上大学的时候。当然，我对国际象棋和人工智能的角度都着迷，但我对卡斯帕罗夫的大脑印象比深蓝更深刻，因为卡斯帕罗夫凭借他令人难以置信的头脑，仍然是有史以来最伟大的国际象棋天才之一，他基本上能够与他旁边的超级计算机强力机器进行平等竞争。
**English / Original**
Combining deep learning to learn the domain and deal with the perceptual inputs and the complexity of the input, find the patterns in it and then reinforcement learning built on top of that to kind of make the decisions and do the planning. And then of course that culminated in AlphaGo, which was always our aim. Dave Silver and I who was head of that project, we were undergrad friends as undergrads at Cambridge and we were just discussing it since our undergrad, about we were there in the mid ’90s, the Deep Blue-Kasparov match happened while we were at college. Of course, I was fascinated by both from the chess and the AI point of view, but I was more impressed with Kasparov’s brain than I was with Deep Blue because Kasparov with his incredible mind, still one of the biggest chess geniuses that there’s been of all time, he was able to basically compete on an equal footing with this supercomputer brute force machine next to him.
## paragraph 43
**中文**
当然，他还能用同一个大脑做其他一切：说五种语言、参与政治、开车，以及人类能够做的种种事情。对我而言，这才真正不可思议，也更加令人钦佩。因此，深蓝系统显然缺少某种东西。那类专家系统依靠人工编写启发式规则，再叠加强力搜索；今天许多传统国际象棋程序仍这样工作。它适用于国际象棋，却一直不适用于围棋：围棋更玄妙，没有不同价值的子力，每颗棋子价值相同，关键全在模式和直觉，顶尖棋手也是凭此下棋。因此我们认为，若有人能让系统达到围棋世界冠军水平，重点并不只是达到这个等级本身。
**English / Original**
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. And to me, that was incredible. That’s 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 you 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…” Really that was an aside getting to that level.
## paragraph 44
**中文**
更重要的是，实现它所需的算法路径很可能非常有趣，并有望推广到其他领域。AlphaGo 的成果远超我们最狂野的设想：它不仅在 2016 年击败李世石，还创造了此前从未出现过的新策略。围棋已有两千多年历史，职业对弈也延续数百年，人类却仍未发现这些策略。这对我有双重意义：我一直在等待 AI 提出新颖想法的时刻；创造力当然还有更高层次，但这至少是真正的新想法。那正是我等待的信号，之后我们便开始将 AI 用于科学；从首尔回来后，我们立即启动了 AlphaFold 项目。
**English / Original**
It was more about the approach we would have taken would probably be a really interesting algorithmic approach and maybe and 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 famously new strategies that had never been seen before, even though we’ve played Go. Go is the oldest game humanity has invented, it’s 2000 plus years old, and been played professionally for hundreds of years, and we hadn’t discovered those strategies. So that was double whammy for me. I was waiting for that moment that AI was able to come up with something novel and there’s more levels of creativity than that, but beyond that, but at least it was a novel idea. And then that was for me was what I was waiting for to then start using AI for science. So the moment we got back from SOL, we started the AlphaFold project.
## paragraph 45
**中文**
乔纳森·莱文（Jonathan Levin）：所以让我们谈谈科学，因为然后你进入了蛋白质折叠问题。再一次，你选择了一个有数据的问题，并且在思考蛋白质折叠方面有一个明确的目标函数，并且它起作用了。我的意思是，你实际上成功解决了预测蛋白质结构这个长期存在的问题。当你想出 AlphaFold 时，你做了一些非常有趣的事情，这显然是一项巨大的科学，值得诺贝尔奖的科学突破。可能也有商业价值，而你只是免费赠送了它。我很好奇，你是怎么做出这个决定的？是不是有什么问题，你有没有想过其他的解决办法？为什么要放弃它？
**English / Original**
Jonathan Levin: So let’s talk about the science a bit, because then you went into the protein folding problem. And again, you picked a problem where there was data and where there was a clear objective function in terms of thinking about protein folding and it worked. I mean, you actually managed to solve this longstanding problem of predicting protein structure. You did something very interesting when you came up with AlphaFold, which was, it was obviously a huge science, Nobel worthy scientific breakthrough. Probably also of commercial value and you just gave it away for free. I’m curious, how did you come to that decision? Was it something, did you think about other ways of going about it? Why give it away?
## paragraph 46
**中文**
黛米斯·哈萨比斯：是的。所以我们选择了蛋白质折叠问题。从我在剑桥读本科时起，当我第一次遇到它时，我就一直关注着它。我有一些生物学家朋友，他们痴迷于蛋白质折叠问题，实际上他们最终成为了结构生物学家，当然，在他们的职业生涯中。具体来说，我记得每次我们在酒吧玩桌上足球或其他什么东西时，他都会着迷地谈论这是生物学中最重要的问题。更重要的是，我认为这是一个根本问题，就像如果你能解开它并找到蛋白质的结构，这将开启全新的研究途径，比如药物发现，显然我们正在努力推动这一点，但也包括基础生物学和疾病理解。因此，这是一个值得花费大量紧张和时间来解决的问题，因为它会产生下游影响。
**English / Original**
Demis Hassabis: Yeah. So we picked the protein folding problem. I had my eye on that since also my undergrad days at Cambridge, just 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 every time we were in the pub playing table football or something, he would be talking obsessively about how this was the most important problem in biology. And more importantly, I think of it as a root note 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. So this was a problem worth really spending a lot of tension and time on because of the downstream effects that it would have.
## paragraph 47
**中文**
这是一个令人着迷的问题。对我来说，这感觉就像终极谜题，3D 谜题，即这个氨基酸序列如何思考其基因序列，折叠成这个 3D 结构。这是非常有趣、复杂的事情。我对蛋白质研究得越多，我对生物学的尊重和好奇就越令人难以置信。这些令人难以置信的小型生物纳米机器，生命中的一切显然都取决于蛋白质，当您开始观察它们的结构时，您就会开始了解它们的功能。所以作为一个科学问题，这对我来说很有吸引力。然后是的，有明确的目标，比如最小化系统中的自由能。想必这就是物理学的原理。这就是为什么在你的身体里，这些蛋白质会在几毫秒内折叠，每秒数十亿次。所以物理学以某种方式解决了这个问题。比方说，一定有一些拓扑结构，你可以通过深度学习系统来学习，从而指导搜索。
**English / Original**
It was a fascinating problem. It felt to me like the ultimate puzzle, 3D puzzle of how does this amino acid sequence, think of its 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. These unbelievable little bio nano machines, everything on 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. And then yes, there was the clear objective, like minimizing the free energy in the system. Presumably this is how physics is doing it. It’s why the body, these proteins fold in milliseconds in your body, billions of times a second. So somehow physics has solved this. There must be some topology, let’s say, that you could learn maybe with a deep learning system that would guide the search.
## paragraph 48
**中文**
就像我们用 AlphaGo 来寻找围棋中的伟大棋步一样，这是一个伟大的策略，它的可能性比围棋宇宙中的原子还要多。蛋白质折叠的搜索空间甚至比这更大，但有一些方法可以以合理的方式缩小搜索范围。您使用深度学习模型学习一种启发式方法，然后指导您的搜索变得易于处理。这感觉就像一个与我们在 Go 中解决的科学问题非常相似的问题，将一些相同的方法、相同的理论应用到这个领域。另一件事是，许多伟大的实验室和人们显然花费了 50 年的时间进行晶体学、结构生物学方面的艰苦工作。经过所有这些努力，主数据库 PDB 中大约有 150,000 个结构，实际上并不多。显然，这需要付出巨大的努力，但蛋白质有 2 亿个，而 15 万个对于机器学习系统来说只是很小的数据量。
**English / Original**
Just like we’d done with AlphaGo to find a great move in Go, a great strategy out of the more possibilities than there are atoms in the universe in Go. And protein folds are 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, applying some of those same approaches, those same theories to this domain. 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.
## paragraph 49
**中文**
因此，大多数人认为至少还需要 10 年、20 年我们才能拥有足够的数据和正确类型的算法来解决这个问题。但我们觉得，使用我们所知道的每一种技术，最终我们可以取得进展，事实证明确实如此。然后，当我们决定时，我们如何才能发挥最大的影响力呢？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，而且速度非常快。它可以在几秒钟内折叠蛋白质。然后最终与剑桥的欧洲生物信息学研究所合作，该研究所拥有科学家使用的许多最大的生物数据库，并且在他们的数据库中托管了全部 2 亿个蛋白质结构，并使其像 Google 搜索一样简单，即可找到您的蛋白质结构。
**English / Original**
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 that we could make progress with that and it turned out to be the case. And then when we decided to, well, how would we 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, in the end European Bioinformatics Institute in Cambridge which hosts many of the biggest biology databases scientists use, and just host the entire 200 million protein structures on their database and just allow it to be as simple as a Google search to just find your protein structure.
## paragraph 50
**中文**
除了置信区间之外，机器学习系统还知道它对蛋白质结构的哪些部分有信心，这对于生物学家来说非常重要。所以我们把所有这些放在一起，当然，它可能非常有价值。我不知道有多少亿美元或其他什么。我的意思是，这取决于你如何计算，通过实验来做到这一点将是难以估量的成本。但保持专有性是非常有价值的，但对我们来说，感觉我们只能触及下游影响的表面，而我们自己将所有这些结构放到世界上可能会产生这样的影响。因为全世界有 300 万研究人员几乎每天都在使用 AlphaFold，几乎全世界的每一位生物学家、医学研究人员都在使用 AlphaFold。一个组织不可能做到这一点。所以这显然是正确的做法。
**English / Original**
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 and it was, of course, could have been very valuable. I don’t know how many billions of dollars or whatever. I mean, it depends on 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.
## paragraph 51
**中文**
我们还依赖公共数据来训练 AlphaFold 的第一个版本，因此回馈该社区（结构生物学社区）是正确的选择，这个令人惊叹的资源正在放大他们精心构建的资源。所以这对我来说根本不是问题，而且谷歌的高管也热爱科学并且完全理解这一点，这真是太好了。我不认为所有公司都会做出这样的决定，所以我也对此给予他们很多荣誉。这是一次轻松的讨论。然后我们尝试通过 Isomorphic Labs（一家正在建设的 Alphabet 分拆公司）将其推向下游，你可以想到更多类型的 AlphaFold 级别的突破，将它们组合成一种有望加速药物发现的方式，将其从几年缩短到几个月，甚至可能一天、几周，就像我们对蛋白质结构所做的那样，过去需要数年时间才能完成一个单一的结构，然后我们可以在几秒钟内完成。
**English / Original**
We also had depended on public data to train the first version 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 painstakingly built. And so it wasn’t even a question for me and it was great that also the executives at Google also loved 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. 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.
## paragraph 52
**中文**
詹妮弗·阿克：此后我们会带着更多黛米斯和乔恩回来。
**English / Original**
Jennifer Aaker: We’ll be back with more of Demis and Jon after this.
## paragraph 53
**中文**
乔纳森·莱文（Jonathan Levin）：我想花一点时间谈谈你本周早些时候所说的事情。你本周出现在新闻中是因为在谷歌的一次大型活动中你说“我们正处于奇点的山麓”。
**English / Original**
Jonathan Levin: 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.”
## paragraph 54
**中文**
Demis Hassabis：是的，这条线得到了很多人的关注。
**English / Original**
Demis Hassabis: Yes, it got quite a lot of pickup, that line.
## paragraph 55
**中文**
乔纳森·莱文（Jonathan Levin）：它得到了很多关注，我知道谷歌新闻团队可能对此并不那么兴奋，但既然你在那里这么说，那么你的意思是什么？
**English / Original**
Jonathan Levin: It got a lot of pickup and 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?
## paragraph 56
**中文**
黛米斯·哈萨比斯：是的。所以我在会议结束时所说的全部内容是，当我们回顾这个时候时，我想也许我在想十年后，我想我们会意识到我们现在正站在奇点的山脚下。我的意思是，我选择这个词的原因是，AGI 是一项技术。我们一直将 AGI 称为真正通用人工智能的下一个版本。我相信我们距离这个目标只有几年的时间，也许每年 20、30 上下，这真的是令人震惊的。然后这个时代，我认为这将是一个巨大的变革性技术，它将有效地成为一个新的人类时代。
**English / Original**
Demis Hassabis: Yeah. So the full thing I said that closed the conference with was, when we look back at this time, I think that maybe I’m thinking 10 years from now, I think we’ll 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, so 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.
## paragraph 57
**中文**
这就是我所说的奇点的意思，许多科幻小说作家都写过这一点，它描述的是当通用人工智能出现时，我们将所处的时代。我认为今年我们可以感觉到，尽管我已经为此努力了 30 年，但我认为今年随着代理的工作方式和工具使用，它开始变得真正有用，你知道，虽然还处于早期阶段，但在人们的工作流程中真正有用。我们可以看到需要做哪些额外的事情，我们所有人，领先的实验室都在努力解决这个问题。我认为这只是开始，但在山麓，我仍然认为还有很多工作要做，而这只是开始。
**English / Original**
And that’s what I’m meaning by the singularity is that, and what many science fiction writers have written about that, is that it’s describing the era that we will be in and around when the advent of AGI happens. 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 the 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. And we can see what extra things are needed to be done and all of us, the leading labs are working on that. I think this is the beginnings of that, but the foothills, I still think there’s a lot more work and it’s just the beginnings.
## paragraph 58
**中文**
这不是一件事，而是几种不同的技术，我看到的几种用例，我认为可能有点遥远的几件事，结果现在聚集在一起，让我感觉到总体上以及我想说的程度。因为我认为社会需要听到这一点，因为我们没有很长时间来准备这意味着什么，而且我认为这将是非常深远的。在我看来，未来仍有待书写，但接下来的几年对于未来的发展方向以及我们共同希望的结果将非常关键。
**English / Original**
And it’s not any one thing, it’s several different technologies, several use cases that I see, several things that I thought were maybe a bit further out, turned out to be now that are coming together that make me feel that in aggregate and to the extent that I wanted to say that. Because I think society needs to hear that because we don’t have long to prepare for what that means and it’s going to be enormously profound, I think. And the future in my view is still to be written, but these next few years are going to be very critical as to which way that will go and how we collectively want that to look like.
## paragraph 59
**中文**
乔纳森·莱文（Jonathan Levin）：如果你看一下关于人们如何看待人工智能的调查，特别是在这个国家，现在的情况非常负面，甚至可能比其他国家更负面。可能有很多因素推动了这一点，比如对隐私或国家控制或科技公司或就业规模的担忧。我的意思是，您正在运营一个领先的实验室，您如何看待公众对该技术的担忧？
**English / Original**
Jonathan Levin: If you look at surveys of how people perceive AI in this country in particular, it’s very negative right now, and maybe more negative here than even in other countries. And there’s probably lots of things driving that, concerns about privacy or state control or the size of the tech companies or jobs. I mean, you’re running one of the leading labs and how do you think about that public concern about the technology?
## paragraph 60
**中文**
Demis Hassabis：我认为公众有权感到担忧。我认为有些事情，并且我担心该技术作为双重用途技术的几个方面。这是这么深刻的事情。我有时会描述它，将其量化为工业革命影响力的 10 倍，速度快 10 倍。因此，发生的时间是十年而不是一个世纪。所以这就像工业革命的一百倍，说实话可能是低估的，但这可能足以让我们尝试、理解和处理。当然，我认为还有超级令人兴奋的事情。将会发生令人惊奇的事情。我们正在努力解决所有疾病。我认为当今社会面临的许多其他挑战，从气候到能源再到疾病，都将得到人工智能的帮助，我确信这一点。
**English / Original**
Demis Hassabis: I think the public’s right to be concerned. I think that there are things that, and I’m concerned about several aspects of what the technology is as a dual purpose technology. It’s something this profound. I sometimes describe it, quantify it as 10 times the impact the Industrial Revolution was, 10 times faster. And so taking into place over a decade instead of a century. So that’s like a hundred X of the Industrial Revolution and it’s probably an underestimate to be honest, but that’s probably enough for us to try and comprehend and deal with. And so of course I think there are super exciting. There’s going to be amazing things that are going to happen. We’re trying to do that with solving all disease. I think a lot of the other challenges facing society today from climate to energy to disease will be helped by AI, I’m sure of it.
## paragraph 61
**中文**
事实上，如果我不认为像人工智能这样的东西即将出现，我会更担心这些挑战，但它会带来很多变化和破坏，实际上是在技术、经济和哲学方面。我认为我们必须经过深思熟虑，并召集社会各阶层来讨论这个问题，而不仅仅是技术专家。技术和技术的安全性只是其中的一部分。它需要经济学家、社会科学家、人类和人类专家来规划接下来会发生什么。我认为这里的负面原因之一是具体而言，因为它在其他国家是不同的。
**English / Original**
And in fact, I’d be much more worried about those challenges if I didn’t think something like AI was coming down the line, but it’s going to cause a lot of change and disruptions and actually both on the technical side, economic and philosophical. And I think we’ve got to think through very thoughtfully and bring together all parts of society to discuss this, not just the technologists. The technology and the safety of the technology is just one piece of this. It needs economists, social scientists, human and humanity experts to kind of chart out what is going to happen next. And I think one of the reasons it’s negative here is that specifically, because it’s different in other countries.
## paragraph 62
**中文**
例如，我们从印度峰会回来，它在印度年轻人中非常受欢迎，因为他们看到了民主化的机会，让他们能够获得与去硅谷所需的基本相同的工具。我们正处于一个令人惊奇的时刻，每个人都可以访问前沿实验室中发生的大部分事情，但只会延迟几个月。如果你仔细想想，这确实是闻所未闻的。这些令人难以置信的事情，但我认为这也是我的一些同行表达这一点的部分方式。我认为他们在沟通和理解方面非常谨慎，我想说，他们的一些声明过于确定。我认为实际上存在巨大的不确定性，这本身就令人担忧，但在我看来，这也意味着什么都没有决定。
**English / Original**
For example, we came back from the summit in India, it’s hugely popular with the youth of India because they see the opportunities that it’s going to democratize for them having access to basically the same tools that you would have needed to go to Silicon Valley for. We’re in an amazing moment in that everyone can access pretty much what’s going on in the frontier labs, but only with a delay of just a few months. That’s unheard of really if you think about that. So these incredible things, but I think it’s also partly the way some of my peers are articulating that. I think they’re being very careful with their communication and understanding they’re being way too certain, I would say, with some of their pronouncements. Where I think actually there’s just huge uncertainty and that is worrying in itself, but it’s also means that nothing is decided, in my opinion.
## paragraph 63
**中文**
我认为这是未知的，我认为任何这么说的人……我认为我可以直接告诉你一些事情，但我认为其中很多事情实际上取决于未来几年采取的行动。今天的年轻人，今天在座的许多学生，你们将要形成什么，你们是第一代在人工智能原生环境中长大的人，我们可以说，就像我一样，在计算机原生环境中长大。就像每一代人一样，您将掌握这些技术，并利用它们变得超级高效。事实上，我认为至少在接下来的 10 年里，很难预测除此之外，如果你以正确的方式使用它们，你几乎会获得超级力量。您可以完成的创造力和项目的数量以及个人能够完成的数量。但这也许会改变工作的性质。将会有更多的创业型、小型创业型企业，而不是大公司。我不知道。它将会改变很多。
**English / Original**
I think it’s unknown and I think anyone who says that… I think directionally I could tell you some things, but I think a lot of it really depends on the actions taken in the next few years. And also what the youth of today, the many students in the room today, you’re going to be forming, you’re the first generation to grow up in AI native, shall we say, like I did, computer native. And just like every generation, you’ll master these technologies, become super productive with them. And I actually think over the next 10 years, at least, hard to predict beyond that, you’ll almost be super powered with those if you use them in the right way. The amount of creativity and projects you can do and the amount an individual will be able to do. But maybe that will change the nature of jobs. There’ll be more entrepreneurial, small entrepreneurial things, rather than big companies. I don’t know. It’s going to change a lot.
## paragraph 64
**中文**
我认为其中一部分是社会团结起来并真正认真对待这个指数。不仅技术专家、经济学家和其他人现在需要认真对待这一问题，并开始规划它的样子。例如，如果我们处于后稀缺世界，那么每个人如何从中受益？仅少数人或少数公司甚至少数公司甚至少数国家从这项技术中受益显然是不正确的。它需要广泛。它将影响全人类。必须让每个人都广泛受益，但这要怎么做呢？我们很多人已经讨论这个问题有一段时间了，但我们现在确实需要答案，需要采取具体的措施和行动。我计划为此尽自己的一份力量。
**English / Original**
And I think part of that is for society to come together and really take this exponential seriously. Not just the technologists, economists and others need to take this seriously right now and start charting out what does that look like. If we’re in a post-scarcity world, for example, how does everyone benefit from that? It’s obviously not correct for just a few people or a few companies or even a few companies or even a few nations to be benefiting from this technology. It needs to be broad. It’s going to affect all of humanity. Has to broadly accrue the benefits to everyone, but how’s that going to be done? A lot of us have been talking about this for a while, but we really need answers now and concrete things and actions to be taken. And I plan to do my bit on that.
## paragraph 65
**中文**
多年来我一直在思考这个问题，并对此进行规划和建立影响力，我会尽我所能。显然我们是一个重要的演员，但我们只是这个领域的一个演员。好消息是，我知道所有领先的实验室和这些实验室的领导者，尽管他们在很多事情上存在分歧，但他们确实担心这些问题的出现。但我们需要更多的论坛，让我们能够聚在一起更坦诚地讨论这些事情。我认为这可能是公众正在察觉的，关于将要发生的事情的讨论有点扭曲，也许在一些信息、筹集资金或其他事情的背后存在一些不可告人的动机。但我认为我们需要使用科学方法，对这个历史关键时刻进行真正严谨和深思熟虑。
**English / Original**
I’ve been thinking a lot about this over the years and planning and building influence on this and I will do what I can. Obviously we’re an important actor, but we’re only one actor in this space. The good news is, I know that all of the leading labs and the leaders of those labs, although they disagree on a lot of things, they do worry about these sorts of issues coming down the line. But we need more forums to allow them for us to come together to discuss these things more candidly. I think that’s what probably the public is detecting, is this slightly skewed discussions about what’s going to happen and maybe there’s some ulterior motives behind some of that messaging, raising money, other things. But I think we need to use the scientific method, be really rigorous and thoughtful about this critical moment in history.
## paragraph 66
**中文**
也许我要说的最后一件事是，我很乐意看到，我认为行业和领域有责任更明确地展示其好处是什么。不仅仅是谈论它们，而是展示它们。所以在健康、医学、科学方面，在我看来，这些东西都是像 AlphaFold 这样明确的商品。但例子还不够多。应该有 20 个 AlphaFold，应该有……我们必须停止谈论治愈癌症的假设，而应该真正治愈癌症。因此，我认为需要这些事情来向公众展示为什么我们这些人对此感到兴奋以及我们中的许多人都在这个房间里。为什么我们对此感到兴奋？为什么我们要花一生的时间来实现这个目标？此外，我们如何具体地降低风险，同时实现我们希望看到的、我认为社会需要的所有令人惊奇的事情。
**English / Original**
And then maybe the final thing I would say is, I would love to see, 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, are unequivocal goods like AlphaFold. But there aren’t enough examples. There should be 20 AlphaFolds and there should be… We got to stop talking in the hypothetical about curing cancer and actually cure cancer. And so these are the things that I think are going to be needed to demonstrate to the public why are those of us who are excited about it and many of us are in this room. Why are we excited about this? Why have we spent our whole life building towards this? And also, how are we going to concretely mitigate the risks while enabling all of the amazing things that we would like to see and I think society needs.
## paragraph 67
**中文**
乔纳森·莱文（Jonathan Levin）：我认为其中有很多要点，如果人工智能的突破带来了一些切实的好处，比如人类健康或药物发现，那可能会在某些方面改变人们的看法。我喜欢这个建议，即尝试更长远地思考一个在生产力等方面可能看起来非常不同的世界。实际上很难做到这一点。在社会科学领域，人们很少能够摆脱当前的框架并真正规划前进的道路。我想起了凯恩斯在大萧条时期写的一篇伟大的文章，当时他审视了我们子孙后代的经济生活。您说我们现在需要另一个凯恩斯的情况很少见。
**English / Original**
Jonathan Levin: I think a lot of great points there that if there were some tangible benefits that were 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’ great article during the Depression when he looks out the economic lives of our grandchildren. It’s a rare case that you were saying we need another Keynes right now.
## paragraph 68
**中文**
Demis Hassabis：也许是观众中的某个人。
**English / Original**
Demis Hassabis: Maybe it’s someone in the audience.
## paragraph 69
**中文**
乔纳森·莱文：也许观众中有人会这么做。让我问一下，您多年来一直谈论的一件事是，前沿实验室需要在某种意义上进行自我监管。也就是说，有时不发布某些可能对安全等构成威胁的技术。现在很明显，实验室正处于一场激烈的竞争中。他们正在投资一切。他们正在全力以赴。您仍然认为实验室应该自我监管吗？您认为政府应该以某种方式介入并监管人工智能吗？相对于您过去谈论的方式，您如何看待当前的动态？
**English / Original**
Jonathan Levin: 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. That is to sometimes not release certain kinds of technologies that might be threats to safety and so forth. Right now it’s pretty clear that the labs are just at 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 talked about it in the past?
## paragraph 70
**中文**
黛米斯·哈萨比斯：是的。嗯，看，首先，只是为了提供一些历史背景，这不是……就我们之前讨论的技术如何发展而言，我认为技术已经取得了惊人的发展，甚至可能比我 20 年前想象的更好。但它诞生的环境并不理想。远非如此。 15 年前、10 年前，我非常担心这种竞争动态的发生，因为越来越多的人、越来越多的公司、越来越多雄心勃勃的技术领导者意识到我 20 多年来所知道的这项技术将有多么重要。我们在房间里讨论了这种种族动态的危险。不幸的是，由于技术的发展，我们已经结束了。因此，如果我可以挥舞魔杖，我会做的就是在研究设施（也许像欧洲核子研究中心）中构建通用人工智能（AGI），更多的通用技术。
**English / Original**
Demis Hassabis: Yeah. Well, look, first of all, just to give some historical context to this, this is not what… In terms of we talked earlier about how the technology’s gone, I think the technology has gone amazingly and 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. And 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’s gone. So if I could have waved a magic wand, what I would have done was build AGI, the general technology more in a research facility, perhaps like a CERN.
## paragraph 71
**中文**
也许所有最优秀的人才能共同检验彼此的想法，确保严格遵循科学方法、充分测试并理解迈出的每一步。当然，这会让 AGI 晚到，也许晚十年；但社会不必等到 AGI 才获得益处，因为我们可以不断拆出其中的能力，打造更多类似 AlphaFold 的专用系统来治疗疾病。AlphaFold 就是专用混合系统：它采用通用系统的许多思想，但专攻蛋白质折叠。这正是我原先的愿景，也是我们当时的做法。然而聊天机器人改变了局面：过去 15 年里，科学层面唯一真正令我意外的，或许就是 Transformer 对语言竟然如此有效。
**English / Original**
Maybe all the best minds helping critique each other’s ideas and making sure we were rigorous with the scientific method and the testing of it and understanding each step that 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 of it 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 because those are, AlphaFold is a specialized hybrid system, uses a lot of the ideas that general purpose systems use, but it’s specialized to protein folding. And you can see that was 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 over the last 15 years on the science side is how effective transformers ended up being for language.
## paragraph 72
**中文**
事实上，你可以分离语言并仅从互联网上学习它，而不必在世界中行动，无论是机器人还是模拟。这很有趣，但那将是另一个话题。我对此有一些理论。语言比语言学家想象的更有基础。人类测试人员正在进行的强化学习反馈提供了一些基础，因为显然我们是扎根于现实世界的。因此，当我们对某些事情说“是”、“不是”时，我们的基础就会以非常低的带宽方式结束，但最终仍然会修改基础模型所理解的内容。所以我想说，这些意想不到的事情发生了。这使得它成为一种非常重要的商业技术，可以通过工程和资金等来扩展，这就是你今天看到的。这改变了动态。
**English / Original**
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 kind of very interesting and that would be a whole nother topic why that was. And 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, no, to certain things, our grounding is then ending up in a very low bandwidth way, but still ending up modifying what the foundation model understands. So there was these unexpected things, I would say, that happened. And then that made it a very important commercial technology that could be scaled with engineering and money and so on, which is what you see today. And that changed the dynamic.
## paragraph 73
**中文**
这造就了我们今天所见的、或许有史以来最激烈的竞争环境——至少在科技产业史上如此，也可能不限于此。身处其中会感到强度高得难以置信，所有参与者都有同感。其上又叠加地缘政治复杂性，于是形成双重竞赛：公司之间进行近乎生死攸关的竞争，同时还有美中等地缘政治竞争。因此局面有两层，非常棘手。我仍希望各方能够合作协调；作为实验室负责人，我们确实会讨论安全性与信息安全问题，所有人都希望避免灾难。
**English / Original**
And then has created what we see today, which is probably the most ferocious competitive environment, I would say there’s ever been. I mean, certainly in the tech industry, tech era, 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 of the participants. And then on top of that, you layer the geopolitical complexities. So there’s a double race going on. There’s the race between the companies and it’s pretty life or death for them. And then there’s the U.S.-China dynamic and others, the geopolitical dynamic, and there’s a race there. 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 discuss this as lab leaders on the safety elements and the security elements. Everybody wants that.
## paragraph 74
**中文**
没有人希望出现灾难性的错误。问题是，我们陷入了囚徒困境，任何人……根据定义，如果你花更多时间来释放某些东西或使某些东西变得更安全，那就比仅仅将其放在那里并让它看看会发生什么更困难。因此，叛逃者有一定的优势，这是逐底竞争的经典问题。我们必须以某种方式改变这一点，我认为这很紧迫。我认为其中一部分是政府的某种形式的参与。当然，最困难的部分是，任何与监管有关的事情都太慢了。每周都有新东西。如果我们在两年前对某些事情进行监管，那么现在就像古老的历史一样。所以这几乎肯定是错误的事情。因此，无论设计什么，我对此有一些想法，我可能会在今年晚些时候讨论这一点，它需要是动态的，通常这个词与监管不相符。
**English / Original**
Nobody wants something catastrophic to go wrong. The problem is, we’re in a prisoners’ dilemma where anyone who… By definition, if you take 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 and this is 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, it’s too slow. Every week there’s something new. If we were to regulate something two years ago, it’s like ancient history now. So it would almost be almost certainly the wrong thing. So whatever is designed, and I have some ideas in this and I’ll probably be talking about this later this year is, it needs to be dynamic, which usually that word doesn’t go with regulation.
## paragraph 75
**中文**
因此，监管必须轻量、敏捷，并能吸收最新进展，从而随真实风险调整，而不是围绕多年前设想、后来被证明并不存在或并非关键的风险运转；那套方式不适用于 AI。即使今天，顶尖科学家也未必能就必要制衡措施的短清单达成一致——事实上，我确信他们不会。这是因为科学尚无定论，也因为进展速度已跑在理解之前，是竞赛态势的直接结果。我们必须重新平衡。我认为需要某种真正“聪明”的监管：动态、能迅速顺应变化，并可能需要由前沿实验室提供信息，因为它们最接近技术一线。
**English / Original**
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 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 and that’s partly the speed, but also the pace of the progress is running ahead of the understanding of it. That’s just how it is as part of the race dynamic. But we need to somehow rebalance that. And I think some form of really almost a 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.
## paragraph 76
**中文**
乔纳森·莱文（Jonathan Levin）：我认为，关于如何建立人工智能监管体系的前景，以及如何以不妨碍地缘政治所谈论的一些突破和积极突破的方式进行监管，还有很多值得讨论的地方。
**English / Original**
Jonathan Levin: I think 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 breaks you’re talking about to the geopolitics.
## paragraph 77
**中文**
黛米斯·哈萨比斯：没错。我们想要所有这些创新，对吗？我们想要解决这个疾病。那么，确切地说，如何启用好的用例并减少坏的用例？
**English / Original**
Demis Hassabis: Exactly. We want all that innovation, right? We want to solve the disease. So exactly, how do you enable the good use cases and mitigate the bad?
## paragraph 78
**中文**
乔纳森·莱文：我很期待你今年提出你的计划。我认为这太棒了，这会让我们在校园和任何地方有很多话题可以讨论。
**English / Original**
Jonathan Levin: 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.
## paragraph 79
**中文**
黛米斯·哈萨比斯：是的，下次。
**English / Original**
Demis Hassabis: Yes, next time.
## paragraph 80
**中文**
乔纳森·莱文：我们有几个学生问题。我想给一些学生提问的机会。
**English / Original**
Jonathan Levin: We’ve got a couple of student questions. I want to give a chance for some student questions.
## paragraph 81
**中文**
学生：嗨，黛米斯。我是阿林泽，商学院二年级学生。我的问题是，如何在推动人工智能前沿与确保健康和科学红利在非洲和南半球等地区均匀分配之间取得平衡，这些地方的需求最大，但部署和研究的基础设施却最有限。
**English / Original**
Student: Hi, Demis. I’m Arinze, 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 is 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.
## paragraph 82
**中文**
黛米斯·哈萨比斯：是的。实际上，我们对此进行了很多思考，这可以追溯到我可以给出的一些例子之一，即我们折叠所有蛋白质的 AlphaFold 问题，我们将其放在您可以从世界任何地方访问的数据库中。需要明确的是，这 300 万研究人员来自 190 个国家。几乎每个国家、每个研究人员都是如此。很棒的是，我们在早期开展一些合作时实际上做了什么，你可以用 AlphaFold 做什么，我们与 DNDi（被忽视疾病药物组织）合作，该组织隶属于瑞士世界卫生组织，致力于治疗世界上没有良好医疗保健系统的贫困地区的疾病。如您所知，其中一些疾病被忽视了，因为大型制药公司无法在这些市场上赚钱。因此，主要影响世界这些地区的疾病并没有得到那么多的研究资源。
**English / Original**
Demis Hassabis: Yeah. We think about that a lot actually and that goes back to one of some examples of that I can give is back to the AlphaFold question where we folded all the proteins, we 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 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 Disease, part of the WHO in Switzerland, which work on diseases in the poorer places of the world that don’t have good healthcare systems. And some of those diseases are neglected, as you know, because big pharma can’t make money in those markets. So then the diseases that affect primarily those areas of the regions of the world don’t get as much research resources behind them.
## paragraph 83
**中文**
因此，我们能够与该研究所和许多实际大学合作，直接让他们不需要尝试找出疟疾病毒或寨卡病毒的结构或类似的东西，而他们必须做所有艰苦的结构生物学工作。他们可以立即开始使用药物。这样就可以大大加快整个过程。他们可以采用感兴趣的结构并从那里继续前进。与受气候变化影响的作物恢复能力相同。我们与詹妮弗·杜德纳的研究所和许多其他研究所合作研究这些问题，因为很多植物蛋白，我们不知道它们的结构是什么，因为显然大部分结构工作都涉及人类蛋白质。因此，如果是动物或植物，那么数据就会少得多。因此，它在这些类型的领域的影响力甚至更大。
**English / Original**
So what we were able to do in collaboration with this institute and many actual universities on the ground is jump them straight to not needing to try to figure out malaria virus or the structures of Zika virus or something like that, which they would’ve 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 to speed up massively the whole process. They can take the structure of interest and move forwards 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 of them because obviously most of the structural work has gone into human proteins. So if it’s animals or plants, there’s a lot less data out there. So it’s even more differentially impactful in those types of areas.
## paragraph 84
**中文**
最后，我认为资本主义引擎在这里其实可以发挥善的作用。假如我们能让 Isomorphic 开发的药物发现平台像我所说的那样高效，把周期从数年缩短至数月，把成本从数十亿美元降至数千万、甚至数百万美元，那么 Isomorphic 可以先治愈富裕地区也深受其害的可怕疾病，以商业回报驱动引擎；继而再以公益方式寻找那些无须财务回报的疾病疗法。因为过程足够快、成本足够低，公司可以在很短时间内直接去做。这就是我希望 Isomorphic 能够造福全世界的方式。
**English / Original**
And then the final thing I would say is, and I think this is where the capitalist engine can actually work for good here is, 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 it costing billions of dollars, it costs tens of millions of dollars, maybe single millions of dollars. 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 that fuels the engine, but then we can do philanthropically, the company could find cures to diseases where we don’t need to make any return because it’s fast enough and it’s cheap enough that it can just be done and in a short amount of time. So I think that’s my dream for how I can make Isomorphic help the whole world.
## paragraph 85
**中文**
学生：嗨，黛米斯。非常感谢您抽出时间与我们交谈。我叫 Miki，是杜尔可持续发展学院的一名大四学生。您广泛地描述了 AGI 如何成为人类最具变革性的技术，我只是很好奇您的责任，或者您如何看待 AGI 所带来的智力开拓和生产力带来的社会影响，特别是在思考这将如何重新定义和重塑人类、我们今天试图解决的挑战以及可能带来的下游影响时。谢谢。
**English / Original**
Student: 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. And you’ve described extensively how AGI could be humanity’s most transformative technologies 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 is this going to redefine and reshape people, the challenges that we’re trying to solve today, but the downstream effects that that could bring forward. Thank you.
## paragraph 86
**中文**
黛米斯·哈萨比斯：是的，谢谢你的问题。我一直在思考这个问题，并且从一开始就这样做，因为我们正在为成功做好计划。尽管这在 15、20 年前看起来非常不可能。我认为这就是为什么我喜欢做这样的演讲并在这些地方与人们会面，因为现在这有点像战斗的号召。我们必须真正考虑二阶后果，这一点非常紧迫。我认为在座的许多人和人文学科的许多人，我认为现在是你们的时间了，因为，好吧，我们必须获得正确的技术。但如果我们这样做，就会出现经济问题。如果我们做对了，就会出现关于人类状况的哲学问题。我非常兴奋，也非常乐观。显然，我是一个谨慎的乐观主义者，正如我所说的那样。
**English / Original**
Demis Hassabis: Yeah, thanks for your question. 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. And I think this is why I like doing talks like this and meeting folks in these kinds of places is it is a bit of a call to arms now. It’s very urgent that we really think about the second order consequences. And I think many of you in the room and many of you in the humanity subjects, it now is your time in my opinion because, okay, we got to get the technology right. But then if we do that, then there’s the economics question. And if we get that right, there’s the philosophical questions about the human condition. And I’m very excited about, and I’m very optimistic. Obviously, I’m a cautious optimist is the way I would say it is.
## paragraph 87
**中文**
我非常乐观，相信我们能够把这件事做好，也深信人类的聪明才智，尤其是在压力最大的时刻。每当真正陷入困境，人类总能找到办法——而现在已到了这样的关头。技术人员正认真对待此事，社会其他部分也必须如此。我与经济学家谈起正在发生的变化时，常惊讶于他们仍很怀疑：“它体现到 GDP 里了吗？”但请看，这可能是工业革命影响的十倍，我们能否现在就开始规划？我们将需要这个时代自己的凯恩斯式巨匠。既有规律为何一定适用于后稀缺世界？如果技术发展正确，人类将第一次进入一个非零和世界。
**English / Original**
I’m very optimistic that we’re going to get this right and I’m a big believer in human ingenuity, especially when the pressure’s on. I think humanity’s always figured it out when the chips are down and they are now, but we do really need to start taking that… I think the technologists are taking it seriously, but the other parts of society need to as well. Economists, I’m always a little bit astounded when I talk to economists about what’s happening and 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? And we’ll be in a world where we do need some giants of these fields, like Keyne’s was, for now. Why would that hold in a post scarcity world? We’re going to be in a world for the first time if we get the technology right, where we’re a non-zero sum world for the first time in humanity’s existence.
## paragraph 88
**中文**
这怎么可能不需要新型经济体系呢？必须如此。我不认为这是我们尝试过的任何一个，因为它们都是在零和和有限、稀缺世界的幌子下完成的。我说的是前往星星并利用太阳系中的所有资源，而不仅仅是地球上有限的资源。我认为，如果我们在未来 10 年、20 年、30 年掌握正确的技术，这确实会发生。在这一切之后，还有一个更难的问题：我们想要如何发展我们的社会，什么是美德，什么是意义，什么是目的？我认为这需要很多伟大的哲学家。因此，这将是我对这些领域的人们的吸引力，在我看来，如果你正在从事这些类型的项目，那么现在是一个非常激动人心的时刻，只要你理解并真正发自内心地理解并倾向于这里实际发生的事情。
**English / Original**
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 zero-sum and a limited, a scarce world. And I’m talking about traveling to the stars and utilizing all the resources that are out in the solar system, not just the limited ones on earth. And I think that really is going to happen if we get the technology right in the next 10, 20, 30 years. And 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? And I think that’s going to need lots of great philosophers. So that would be my appeal to people in those fields is now, in my view, could not be more of an exciting time if you’re working on those types of projects, as long as you understand and really viscerally understand and lean into what’s actually happening here.
## paragraph 89
**中文**
Jonathan Levin：这是对大学提出的一项很好的使命。好，再听最后一个学生问题。
**English / Original**
Jonathan Levin: It’s a good charge to university. Okay, but one more student question.
## paragraph 90
**中文**
学生：嗨，丹尼斯。我是贾奈。我是 MBA 二年级学生。我问你的问题是，你这一生中不希望人工智能触及什么？从你的角度来看，什么是神圣的？谢谢。
**English / Original**
Student: Hi, Dennis. 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 sacred from your perspective? Thanks.
## paragraph 91
**中文**
杰米斯·哈萨比斯：是的，这是一个很好的问题。看，就科学世界而言，人工智能将是一项完全通用的技术。您可以将其视为图灵机。我就是这么想的。这是我在大学最喜欢的课程，我认为我们的思想实际上是完全通用的。所以我们是近似图灵机。正如图灵所展示的，任何可计算的东西，图灵机都可以计算。我们所知道的宇宙中的大多数事物，非量子事物都是可计算的。所以这是我们可以思考的一大堆一般性的事情。因此，我们建立了现代文明，如果我们停下来思考的话，这就是奇迹，而且我认为我们的好奇还不够。我们对此感到好奇的时间不够长。
**English / Original**
Demis Hassabis: Yeah, that’s a great question. Look, AI is going to be, in terms of the scientific world of things, it’s a fully general technology. You can think of it as a Turing machine. That’s the way I think about it. It was my favorite course at college and I think our minds are actually fully general. So we’re approximate Turing machine. So as Turing showed, anything that’s computable, a Turing machine can compute. And most things we know about in the universe, non-quantum things are computable. So that’s a pretty large set of general things that we can turn our minds to. Hence, we built modern civilization, which is miraculous if we stop to think about it and I don’t think we wonder enough. We don’t keep our sense of wonder for long enough about that.
## paragraph 92
**中文**
但这也意味着我们构建的系统同样会具备图灵完备的计算能力。未来有一些重大问题，我认为最好放慢脚步。当前很受关注的意识问题就是一例：哲学和神经科学至今仍未把它清晰定义，尽管我们对其重要方面都有直觉。我的判断是当前系统并未表现出意识，不过有人不同意。至于 AI 不应触及什么，我建议第一代系统先被构建成工具——智能工具。在我看来这已经足够艰巨，因为那本身就是 AGI。
**English / Original**
But it also means these systems that we’re building, they’re also going to be Turing powerful as well. One thing I would say is that there are very big questions to come that I think it would be better if we took more time over. So one example that’s pretty topical right now is consciousness. And it’s not a very well posed problem from philosophy and neuroscience still, although I think we all have intuitions as to what are the important aspects of that. My feeling is the current systems don’t exhibit any, are not, but others disagree. What I would recommend though in terms of like what area should AI not touch is that we build our first systems as tools, intelligent tools. That’s enough of a challenge already, in my opinion, because that’s already AGI.
## paragraph 93
**中文**
然后使用这些工具，我认为我们应该研究神经科学和其他类似的事物以及哲学，并实际上对意识等事物提出更严格的定义。我认为这是可能的。然后对此进行测试，然后也许社会会决定我们是否想要跨越第二个卢比孔河，尝试制造至少对我们来说似乎有意识的实体。所以我们可能不想做出这个决定。我认为智力和意识是分离的。我认为你不必这样做才能拥有一个智能系统。我认为这是一个选择。因此，您可能会感觉到，当您使用一些领先的聊天机器人时，会出现意见分歧。我的观点是最好分两步进行。它们对人类来说都是巨大的，而不是把两者混为一谈。
**English / Original**
And then using those tools, I think we should study neuroscience and other things like that and philosophy and actually come up with a more rigorous definition of things like consciousness. I think that is possible. And then test things against that and then maybe society decide if we want to cross the second Rubicon of trying to make entities that at least seem like conscious to us. So we may not want to make that decision. I think that intelligence and consciousness are dissociable. I don’t think you have to do that to have an intelligent system. I think it’s a choice. And so you can probably feel that when you use some of the leading chatbots, there’s differences in opinion that come through. And my view is it’d be better to take that as two steps. They’re both enormous for humanity, rather than conflate the two.
## paragraph 94
**中文**
乔纳森·莱文：戴米斯，我们有一个礼堂，里面有很多学生。如果你回到学校，你会学习什么？您会如何考虑……对于他们应该如何考虑在职业生涯中学习，您有何建议？
**English / Original**
Jonathan Levin: Demis, we’ve got an auditorium with many students in it. If you were back in school, what would you be studying? How would you be thinking about what… What would be your advice on how they should be thinking about to study in their careers?
## paragraph 95
**中文**
Demis Hassabis：如果现在重返大学，我会非常兴奋。我的建议是：学习科学、STEM、数学和计算机科学的人仍应继续深耕；理解这些工具如何构成、具备什么能力，会让你更好地利用它们，至少未来十年仍会如此。同时也要主动拥抱它们，而不是希望它们消失——精灵不会回到瓶子里。前沿实验室忙于打造工具，我们对其实际用途只触及皮毛；即便今天的工具也存在所谓的“能力余量”（capability overhang）。
**English / Original**
Demis Hassabis: Well, look, I would be really excited if I was back at college now. My recommendation would be those of you doing science and STEM subjects and 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. I think that’s going to be true for the next period, the next 10 years at least. And I would also lean in though to not wish it away. The genie’s not going back in the bottle. Lean into what these tools can do. I can tell you that the leading labs are so busy making the tools that we probably only scratch the surface of, not even probably, of what they can actually do. Even today’s tools. I have this, sometimes people call it capability overhang.
## paragraph 96
**中文**
如果您弄清楚如何将它们与其他事物配对或将其与您擅长的另一个领域配对，并以有趣的方式将其构建到您的工作流程中，那么这些东西可以发挥巨大的潜力。你有这些工具。它们是任何人都拥有的最强大的工具。它们就在你的手掌之中。作为个人，您可以做的还有很多。我认为它应该释放创造力，就像你们学习人文或产品或商业的人一样，也许你以前没有编码技能，但你现在可以使用这些工具产生很多你想到的东西。但我认为，如果你是编码专家，那么编码员、专家们可以在项目规模方面多做一百倍的工作。因此，我认为它既可以实现民主化，也可以使专门从事这些领域的人员发挥作用。所以我认为这是一个美妙的时刻，但我也感到担忧，因为一切都会改变。
**English / Original**
There’s so much potential these things can do if you figure out how to pair them with other things or pair it with another domain you’re an 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 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 a hundred X more in the terms of the size of a project you can do if you’re a 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 I get it’s also worrying because everything’s going to change.
## paragraph 97
**中文**
有一点我可以肯定：未来十年一切都会改变，程度可能超出多数人的设想。但凡出现如此巨大的变化，也必然伴随巨大机会，天地大有可为。我甚至羡慕你们中的一些人：你们将是第一代 AI 原住民，就像我这一代是计算机与互联网原住民。未来世界最终如何建成，将掌握在你们这些学生手中。只要从合适角度看待它，并投入充分的想象力和创造力，这会是一个极其激动人心的时代。巨变时期一向如此，而今天尤甚。
**English / Original**
So 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 that also, in any time there’s enormous change like that, there’s enormous opportunities. There has to be. And the world’s your oyster really. And I 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.
## paragraph 98
**中文**
乔纳森·莱文：我们说过，在一个充满变革的时期，你不太知道未来会怎样，但你必须能够适应并拥有广泛的知识领域。这将是自由教育的黄金时代。
**English / Original**
Jonathan Levin: We were saying 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.
## paragraph 99
**中文**
Demis Hassabis：最重要的是进一步强化你自己的能动性。未来仍有待书写；不要听信任何声称未来已经注定的人。
**English / Original**
Demis Hassabis: I mean, the main thing is to just make sure you double down your own agency. The future’s still to be written, I would say. So don’t listen to anyone who says it’s not.
## paragraph 100
**中文**
乔纳森·莱文：戴米斯，感谢您加入我们。这太棒了。
**English / Original**
Jonathan Levin: Demis, thank you for joining us. This was amazing.
## paragraph 101
**中文**
黛米斯·哈萨比斯：当然。
**English / Original**
Demis Hassabis: Sure.
## paragraph 102
**中文**
Jennifer Aaker：该播客由斯坦福大学商学院制作。非常感谢您的聆听。
**English / Original**
Jennifer Aaker: This podcast is a production of Stanford Graduate School of Business. Thanks so much for listening.
## paragraph 103
**中文**
如需媒体垂询，请访问新闻编辑室。
**English / Original**
For media inquiries, visit the Newsroom.
## paragraph 104
**中文**
编辑精选 乔纳森·莱文 (Jonathan Levin) 詹妮弗·阿克 (Jennifer Aaker) 探索更多人类学习的神秘过程 机器无法复制人类“才华”的秘密 人工智能无法匹敌 技术如何在不压倒我们脑力的情况下发展
**English / Original**
Editor’s Picks Jonathan Levin Jennifer Aaker Explore More The Mysterious Process of Human Learning That Machines Can’t Copy The Secret to Human ‘Brilliance’ That AI Just Can’t Match How Technology Can Evolve Without Overwhelming Our Brainpower