# (77) A new era of discovery: AI and the frontiers of science with Demis Hassabis - YouTube
- Transcript ID: `dgblvm2l1p4`
- Source tier: `youtube_creator`
- Original language: `en_or_source_language_unlabeled`
- Translation status: `agent_translated_reviewed` (106/106 chunks)
- Original source: https://www.youtube.com/watch?v=dgBLVm2L1P4
- Raw evidence: `data/transcripts/youtube/demis-hassabis-google-io-2026-dgBLVm2L1P4.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。
## 00:00:00
**中文**
【音乐】【掌声】迈克·艾伦：我是 Axios 联合创始人迈克·艾伦。感谢 Google I/O 团队促成这些对话。德米斯·哈萨比斯最初是国际象棋神童和游戏玩家，如今是 Google DeepMind 联合创始人兼 CEO，身处 AI 科学竞赛与商业竞赛的最前线。
**English / Original**
[MUSIC PLAYING] [APPLAUSE] MIKE ALLEN: I'm Mike Allen, co-founder of Axios. Thank you to the Google I/O team for making these conversations possible. Demis Hassabis started as a chess savant and a gamer, now co-founder and CEO of Google DeepMind, where he's on the front lines of both the scientific race and the business race for AI.
## 00:00:33
**中文**
DeepMind 具有里程碑意义的 AI 突破包括 AlphaGo 和 AlphaFold；后者解决了蛋白质结构这一长达 50 年的重大难题。凭借破解这道难题，德米斯获得了诺贝尔化学奖。上个月，《经济学人》把德米斯装扮成身着长袍的希腊神祇放上封面，列入正在塑造我们所有人未来的“泰坦诸神殿”。
**English / Original**
DeepMind's landmark AI breakthroughs include AlphaGo and AlphaFold, which solved the 50-year grand challenge of protein structure. For cracking that code, Demis was awarded the Nobel Prize in chemistry. "The Economist" last month put Demis on the cover as a Greek god, complete with tunic, in a pantheon of the Titans, who are shaping all of our future.
## 00:01:00
**中文**
这位 DeepMind 背后的大脑甚至获封爵士。德米斯·哈萨比斯爵士，欢迎来到《Dialogues》。【欢呼】【音乐】德米斯：嗨，迈克，很高兴见到你。迈克：非常感谢你参加。德米斯：谢谢，也谢谢你来做这场对话。迈克：德米斯，昨天你在主题演讲中宣布了你很喜欢的 Gemini for Science，赢得了热烈掌声甚至欢呼，但你给我们留下了一个终极悬念。
**English / Original**
He's even been knighted the mind behind DeepMind. Sir Demis Hassabis, welcome to Dialogues. [CROWD CHEERING] [MUSIC PLAYING] DEMIS HASSABIS: Hey, Mike. Good to see you. MIKE ALLEN: Thank you so much for doing this. DEMIS HASSABIS: Thanks. Yeah, thanks for doing it. MIKE ALLEN: So, Demis, yesterday, at your keynote, where you got, by the way, big applause and even whoops for announcing that the Gemini for Science, which is one of your favorites-- but you left us with the ultimate cliffhanger.
## 00:01:35
**中文**
你说：“当我们回望这个时代时，我想我们会意识到，自己当时正站在奇点的山麓。”德米斯：嗯。迈克：再深入一层。你认为会发生什么？你希望发生什么？德米斯：是的，大家似乎特别注意到了那句话。
**English / Original**
You said, when we look back at this time, I think we will realize we were standing in the foothills of the singularity. DEMIS HASSABIS: Mm-hmm. MIKE ALLEN: Take us one layer deeper. What do you think is going to happen? What do you hope is going to happen? DEMIS HASSABIS: Yeah, I thought people seemed to pick up on that line specifically.
## 00:01:52
**中文**
迈克：可不是吗？【笑声】德米斯：是的，这多少在意料之中，不过能稍微展开谈谈也很好。我显然经常谈到 AGI 已在地平线上。我认为距离完整形态的 AGI 现在只剩几年。“奇点”是一个很合适的词，用来描述 AGI 这类技术登场后所开启的时代。
**English / Original**
MIKE ALLEN: Ya think? [LAUGHTER] DEMIS HASSABIS: Yes, so it was kind of expected. But it's great to be able to expand on it a little bit. So I'm talking a lot about, obviously, AGI being on the horizon. I think we are only a few years away now from the full version of that. And the singularity is a kind of good word, I think, to describe the era that is precipitated by a technology like AGI arriving on the scene.
## 00:02:20
**中文**
部分原因是，它将带来如此深刻的变革——我认为可能是有史以来最重要的发明——以至于很难预测越过那条地平线之后的事情，因为改变会非常巨大。今年，随着我们都在看到和使用智能体及智能体系统，我认为现在已经开始能够感受到它了。
**English / Original**
And partly, it's because it's going to be so transformative, I think the most important invention ever, that it's hard to make many predictions beyond that horizon because it will change so much. And I think, this year, with the agents, agentic systems that we're all seeing and using, I think we can start feeling it now.
## 00:02:44
**中文**
这大致就是我的意思。我想，五年或十年后回望时，我们会说：“哦，是的，2026、2027 年就是一切开始的时候。”迈克：我很喜欢“我们开始感受到它”这个说法。再展开一点，你是怎么感受到的？德米斯：我想大家都能感受到。我自己仅仅使用我们的编程系统和编程智能体、做一些氛围编程就感受到了；凌晨仅有的一两个小时里，我甚至能做出小型游戏原型，而过去这可能需要六个月。
**English / Original**
And that's what I was sort of meaning by that. When we look back, I think, in five, 10 years' time, I think we'll see it as like, oh, yes, 2026, '27 is when it was starting. MIKE ALLEN: Yeah, and I love that we're starting to feel it. DEMIS HASSABIS: Yeah. MIKE ALLEN: Expand on that a little bit. How do you feel it? DEMIS HASSABIS: Well, I think all of you-- I mean, I've been feeling it just by using our own coding systems and coding agents and just vibe coding things, even little game prototypes in my spare one or two hours in the small hours of the morning, which would have taken six months before.
## 00:03:19
**中文**
我觉得这种加速非常值得关注。我们显然正在科学领域看到它：AlphaFold 已是几年前的成果，如今 AI 几乎被应用于科学的每一个分支。我也看到 Isomorphic Labs 等机构在药物发现方面取得进展的速度。
**English / Original**
And I just think that kind of acceleration is very interesting to see. I think we're seeing it in areas of science, obviously with AlphaFold, which was a few years ago now, but almost every branch of science AI being applied to. And I'm also seeing the speed at which we're making advances in things like Isomorphic Labs for drug discovery.
## 00:03:41
**中文**
因此，把这些因素综合起来，再看基础模型、聊天机器人系统以及 Omni 等技术当前的进展，我只能说整体进步令人惊叹。迈克：根据你多年来设定的里程碑，我觉得我们大致正按计划前进。
**English / Original**
So I think taking that all together and looking at the current progress of the foundation models and the chatbot systems and things like Omni, all of that, taken together, is just astounding progress, I would say. MIKE ALLEN: Yeah, and based on the markers you set over the years, I feel like we're kind of right on schedule.
## 00:04:00
**中文**
德米斯：是的。迈克：你说只剩几年。你最新的时间判断是什么？德米斯：根据我现在看到的情况，我最近一直说大约是 2030 年，前后浮动一年，这是合理估计。迈克：也就是 2029 至 2031 年。德米斯：嗯。
**English / Original**
DEMIS HASSABIS: Yeah. MIKE ALLEN: You say, a few years. DEMIS HASSABIS: Yeah. MIKE ALLEN: What is your latest marker? DEMIS HASSABIS: Well, I feel like it's-- I've been saying, recently, around 2030, plus or minus a year, I think is a reasonable estimate, from what I'm seeing now. MIKE ALLEN: So 2029 to 2031. DEMIS HASSABIS: Mm-hmm.
## 00:04:16
**中文**
迈克：DeepMind 最初的使命宣言是“解决智能”。进展如何？德米斯：我认为相当不错。我们最初的说法是：第一步，解决智能；第二步，用智能解决其他一切。我觉得这很有先见之明。我们当时认为，AGI 会成为一种能够提供帮助的通用技术。
**English / Original**
MIKE ALLEN: So DeepMind's original mission statement was solve intelligence. How are we doing? DEMIS HASSABIS: Well, I think we're doing pretty well. So our original statement was, step one, solve intelligence. Step two, use it to solve everything else. And I think that was pretty prescient. That's what we felt AGI would be is a general purpose technology that could help.
## 00:04:40
**中文**
我当时更多想到的是科学与医学，尤其是增进我们对周围世界运行方式的理解。你知道，这一直是我的热情所在。我认为我们现在正处于这一时刻的前夜。迈克：在现场以及全球观看我们的建设者和探索者有生之年，你认为“智能”会被解决吗？
**English / Original**
I was thinking more about science and medicine, specifically, in increasing our understanding of how the world works around us. That's always been my passion, as you know. And I think we are on the cusp of that now. MIKE ALLEN: In the lifetime of the builders and searchers in this room and watching us around the world, do you think intelligence will be solved?
## 00:05:01
**中文**
德米斯：会。不过，“解决”只是个简洁的说法，它究竟意味着什么？我的意思是建成 AGI，而我认为距离它只剩几年。对我而言，这将是第一步。迈克：不同的人以不同方式谈论 AGI，但我以为你认为会有一个明确时刻，而且我们届时会知道。
**English / Original**
DEMIS HASSABIS: Yes, well, I mean, look, solved, it's just a neat term. What does it actually mean? But what I meant by that was building AGI. And I think we're a few years away. So that would be the first step to me. MIKE ALLEN: And with AGI, different people talk about it different ways. DEMIS HASSABIS: Yeah. MIKE ALLEN: But I think you're one of the people that thinks that there will be a moment and that we will know.
## 00:05:25
**中文**
德米斯：不，我认为并非如此。我认为它会逐步提升，这也是我们现在看到的情况——速度可能会相当快。不过，我心中有一项特殊测试，姑且称为“爱因斯坦测试”。想象一下，某个领先前沿模型的知识截止于 1901 年。
**English / Original**
DEMIS HASSABIS: Well, no, I think that's not the case. I think it will be-- it's going to be a gradual improvement. I think that's what we're seeing. Probably a pretty fast improvement. But I have a particular test in mind, which I call kind of the Einstein test. So imagine you had a knowledge cutoff of one of these leading frontier models of 1901.
## 00:05:47
**中文**
它能否独立得出爱因斯坦在 1905 年取得的非凡洞见和发现，包括狭义相对论？一个系统能否产生这样的洞见和突破？今天的系统显然做不到，但我看不出未来它们为何不能做到。这种创造力或洞见上的跃迁，正是我仍在等待系统展现、从而称其为完整 AGI 的能力。
**English / Original**
Would it be able to come up with his incredible sort of insights and discoveries that he did in 1905, including special relativity? Could a system come up with those insights and breakthroughs? And I think today systems clearly can't. But I think I don't see why, in the future, they won't be able to. So that's the creativity or leap of insight that I'm still looking for from these systems for it to be full AGI.
## 00:06:14
**中文**
迈克：Google I/O 的 Press Park 里，科技记者热议 Google“找回了状态”。《纽约时报》的陈布赖恩昨天发文称，Google 正开始赢得 AI 竞赛。如果你们获胜，原因会是什么？Google 的优势在哪里？
**English / Original**
MIKE ALLEN: Lots of buzz among tech writers in Press Park, right here at Google I/O, about Google having its groove back. Brian Chen of "The New York Times" posted yesterday how Google is starting to win the AI race. DEMIS HASSABIS: Mm-hmm. MIKE ALLEN: If you win, it will be, why? What is the Google edge? DEMIS HASSABIS: Mm-hmm.
## 00:06:34
**中文**
迈克：又是什么发生了变化？德米斯：我觉得“我们回来了、我们在赢、然后又没回来”这种说法有点奇怪。至少三家前沿实验室乃至整个前沿领域，正在经历科技史上、或许也是企业史上最激烈的竞争。
**English / Original**
MIKE ALLEN: And what changed? DEMIS HASSABIS: Look, I think it's sort of weird, this sort of idea of, we're back, and then we're winning, and then we're not back. And the thing is that, at least the three frontier labs and actually the whole of the frontier, it's the most ferocious competition there's ever been, I would say, in tech history, maybe in corporate history.
## 00:06:55
**中文**
各机构都有能力极强、才华横溢的人，竭尽全力推动研究和突破。就我们需要完成的事情而言，我认为我们做得很好，底层技术方面也是如此。我们的研究人才梯队比其他实验室更宽广。
**English / Original**
So there's that going on. Very capable, brilliant people in all the organizations, each trying to push, as hard as they can, on the research and the breakthroughs. I think we're doing really well for what we need to do, both in terms of the underlying technologies. We have a kind of broader research bench than I think the other labs have.
## 00:07:17
**中文**
我们一直如此。我认为这赋予了我们创造下一项突破的创新能力。现在可以看到新模型即将推出——我们的 3.5 Flash Pro 很快就会发布。我们对底层模型的进展非常满意。我们还有全部生成式媒体模型，最新的是 Omni。
**English / Original**
We've always had that. I think that's what gives us our innovative capacity to invent the next breakthroughs. You see, now, new models that are coming out-- our 3.5 Flash Pro coming soon. We're very happy with the progress that we're making on the underlying models. We also have all our generative media models, Omni being the latest.
## 00:07:38
**中文**
这些方面进展顺利。我认为表现尤其出色的是消费端。过去一年我们真正的大跨越，是精简技术栈，几乎从头重写，使其从“AI 优先”进一步变成“智能体优先”。对 Google 这样依靠强大工程栈、每天在多个产品界面服务数十亿用户的卓越工程组织而言，这是一次重大改变。
**English / Original**
So that's going well. Where I think things are going extremely well is on the consumer side. So I think that's what the really big leap has been, for us, in the last year, is streamlining our tech stack, actually almost rewriting it from the ground up, to be AI first and now agent first. That's a big change for a incredible engineering organization, like Google, that serves billions of users every day on multiple product surfaces on our incredible engineering stack.
## 00:08:08
**中文**
但技术栈必须适应，才能以极其庞大的规模把最新模型的能力直接交付到所有产品中。我认为我们现在已经做得非常好，胜过其他所谓大型科技公司。Gemini 应用的采用进展已经体现这一点——目前月活用户达 9 亿，增长惊人；搜索中的 AI Mode 也极受欢迎，我认为它是用于信息查找的非常实用的 AI 形态。
**English / Original**
But that has to adapt so that you can ship the model, the goodness of the latest models, directly into all of those products at massive, massive scale. And I think we've got that down really well now, better than any of the other, shall we say, big tech companies. And I think you're starting to see that in the progress we're making with the adoption of Gemini app-- 900 million monthly users now, incredible growth there that we're seeing-- and also things like AI Mode in Search, an unbelievably popular and incredibly useful, I think, version of AI for information seeking.
## 00:08:46
**中文**
如果观察我们的各个产品界面及模型工作，尤其是仍然非常强大的小型高效模型，就会看到这一点。它们对内部场景极其重要，因为我们必须服务数十亿用户；对一些大型企业客户同样非常重要。
**English / Original**
So I think if you look across our surfaces and what we're doing with our models, especially the small, efficient models that are still very powerful-- so that's really important for our internal use cases because we've got to serve billions of users. But it's also very important for some of our big enterprise customers as well.
## 00:09:02
**中文**
综合来看，我们还有全栈优势：从芯片、数据中心、云、前沿实验室，一直到嵌入 AI、拥有十亿级用户的产品。我认为我们是全球唯一拥有这套完整全栈的组织。
**English / Original**
So I think when you take a look at that, at aggregate, and then you also can see the advantage we have being full stack, all the way from the chips, to the data centers, to cloud, to frontier lab, to billion-user products that have AI embedded in them, I think we're the only organization in the world that has that full stack.
## 00:09:25
**中文**
我们不断优化这个反馈回路，让各环节相互促进。迈克：你认为你们会赢吗？德米斯：我认为我们会成为这个时代的大赢家之一。迈克：你觉得最终会有多少赢家？德米斯：我不知道。我并不真的以“输赢”来看这件事——究竟要赢到什么呢？
**English / Original**
And we're continually trying to optimize that feedback loop so that they all feed into each other. MIKE ALLEN: Do you think you will win? DEMIS HASSABIS: Well, I think we're going to be one of the big winners of the era. MIKE ALLEN: How many do you think there will be? DEMIS HASSABIS: Well, I don't know. I don't really see it like that, in terms of win-- win to what, exactly?
## 00:09:44
**中文**
AI 时代将无比广阔，会创造难以置信的大量新机会和新的经济进步。我们必须确保自己成为后 AGI 世界的关键参与者之一，而我认为我们的定位极其有利。至于参与者会有多少，考虑到这项技术的强大程度，我猜只有少数几家。
**English / Original**
So I think the AI era is going to be immense. It's going to be unbelievable amount of new opportunities created, new economic progress made. And we've got to make sure that we're one of the key players in that new post-AGI world. And I think we're incredibly well positioned for that. And as to how many players there'll be, my guess is a handful, given how powerful the technology is.
## 00:10:12
**中文**
但退一步说，我始终强调：尽管我们在这场竞赛中激烈竞争，所有前沿实验室的领导者都必须思考——而且他们确实正在思考——对世界所负责任的大局。我们把这项不可思议的技术带入世界，希望它造福每一个人、造福全人类。
**English / Original**
But actually, stepping back from that, what I always say is, although we're ferociously competing in this kind of race, all the leaders of the frontier labs need to think about-- and they are thinking about-- the bigger picture of the responsibility to the world. As we bring this unbelievable technology into the world, we want it to be for the benefit of everyone and the benefit of humanity.
## 00:10:35
**中文**
这件事比我们所有人以及竞争本身都更重大。我们要记住，最后几步必须谨慎、深思熟虑地迈出，确保系统在设计时兼顾安全与安保，同时也促成我们希望看到的所有出色善用场景。迈克：接着你说的“机会”——园区内 5,000 名学生和专业开发者就是鲜活证明。
**English / Original**
And it's something bigger than all of us and the competition is remembering we've got to take these final steps in a careful, thoughtful way and make sure that we're building it with safety and security in mind, as well as enabling all of the amazing, good use cases that we hope to see. MIKE ALLEN: Demis, picking up on your word "opportunity"-- and the 5,000 students and professional developers here on campus are living proof of that.
## 00:11:01
**中文**
德米斯：是的。迈克：但许多美国人既没看到也没感受到。今天《华尔街日报》头版写的是 AI 反弹正在形成，对吧？美国人的反抗，埃里克·施密特遭到嘘声。为何会这样？你们能做什么？德米斯：我不太认同一些同行谈论这件事的方式。
**English / Original**
DEMIS HASSABIS: Yeah. MIKE ALLEN: And yet a lot of America doesn't see it, feel it. Front page of today's "Wall Street Journal," the AI lash building. Right? American rebellion. Eric Schmidt getting booed. Why is that happening, and what can you do about it? DEMIS HASSABIS: Well, I think a lot of the ways that some of, let's say, my peers are talking about this I don't really agree with.
## 00:11:22
**中文**
我既不认同其沟通风格，也不认同其中一些实质内容。我的看法是，毫无疑问会发生巨大的变化。我曾公开表示，如果一定要量化，这项变革的影响可能是工业革命的 10 倍，而速度可能快 10 倍——也就是用十年而非一个世纪完成。
**English / Original**
I don't really agree with the style of communication but also some of the substance too. So what I would say, the way I'm looking at it is, there's going to be no doubt huge amount of change. So something this transformative, I've been on record saying, if you want to try and quantify it, it's going to be 10 times the impact of the Industrial Revolution, maybe 10 times faster-- so over a decade, instead of a century.
## 00:11:44
**中文**
这可能还是低估，或许确实是。迈克：等等，影响是 10 倍，速度也快 10 倍？德米斯：是的，可以把它看作工业革命的 100 倍，而且可能仍是低估。这非常巨大。但我要说，未来尚未写定，充满不确定性。
**English / Original**
And that might be an underestimate. Probably is. MIKE ALLEN: Wait. 10 times the impact and 10 times faster? DEMIS HASSABIS: Yeah, so you can think of it as 100x the Industrial Revolution. MIKE ALLEN: Yes, yes. DEMIS HASSABIS: Maybe an underestimate. So that's huge. But what I say about that, the future is not written. It's very uncertain.
## 00:11:59
**中文**
会创造更多工作吗？会出现不同工作吗？我们不知道，没有人知道，那些所谓专家也不知道。未来其实将由下一代书写。每当我与学生交流或到访一些地方，我都很兴奋——例如今年早些时候我们刚参加了印度 AI 峰会。
**English / Original**
Are there more jobs going to be created? Are there different ones? We don't know. Nobody knows. None of these experts, so-called experts, know. And the future is to be written, actually, by the next generation. I'm actually very excited when I talk to students and when I go to places-- like, we just came back earlier this year from the India AI Summit.
## 00:12:18
**中文**
印度年轻人对自己能做什么、这些工具将使他们能做什么无比兴奋：创办公司、开发应用、在全球范围竞争。这项技术实际上正在让创造机会民主化。过去需要 10 名或 50 名专家组成团队才能完成的东西，也许一个聪明孩子在自家车库里就能独立完成。
**English / Original**
And the youth of India are so excited about what they can do, what these tools will enable them to do-- build companies, build applications, compete at the global scale. So this technology is actually democratizing opportunities for what you can build. What would have taken a team of 10, 50 experts to build, maybe a smart kid in their garage can build it themselves.
## 00:12:41
**中文**
回想我成长的年代，先有计算机和家用电脑，之后出现互联网；它们为当时伴随这些技术长大的孩子和学生创造了大量机会，使他们得以建设我们今天看到的未来。我认为，同样的事情会再次发生在伴随 AI 长大的下一代身上。
**English / Original**
So when I think back to the era I grew up in, with computers and home computers-- and then there was the internet and the amount of opportunities that then created for kids of the day, students of the day, who grew up native with that technology, to actually go on and build the future that we see today. And I think that will happen again with the next generation that's going to grow up AI native.
## 00:13:08
**中文**
至少未来十年——再往后很难看清——他们会获得超级能力。只要接受足够的技术训练，并带来创造力、品味和判断力等技能，我认为这就会成为下一个时代。因此，我非常乐观地相信，人类的聪明才智会找到使用这些新工具的非凡方式，而我们一直都在看到这样的例子。
**English / Original**
And I think, for the next 10 years at least-- and it's hard to see beyond that-- they're going to be super powered. As long as they're technically trained enough and then they bring creativity and taste and judgment, these kinds of skills, to the table, I think that's going to be the next era. So I'm actually very optimistic about human ingenuity to find amazing ways to use-- and we're seeing that all the time-- to use these new tools.
## 00:13:32
**中文**
迈克：你的直觉是就业净增加，还是连这也无法判断？德米斯：和任何新革命一样，就业市场会发生变化。但我看不出机会为何不会增加。也许会出现更多一两个人创办的初创企业。因为拥有这些非凡的创作工具，人们会更需要创业能力。
**English / Original**
MIKE ALLEN: Your hunch would be net more jobs, or you can't even say that? DEMIS HASSABIS: I think there will be changes to the job market, as always there is, with any new revolution. But I don't see why there wouldn't be more opportunities. Maybe it will be more like creating startups, one- or two-person startups. More entrepreneurial skills will be needed because you have these incredible creative tools.
## 00:13:56
**中文**
变化一定会发生，但我认为总体会变得更好。关于反弹，我还想说，AI 领域和行业有责任更清楚、更具体地展示无可争议的益处。这正是我们通过“AI for Science”工作以及用 AI 推进科学和医学所努力做的。
**English / Original**
So there's going to be changes. But I think, overall, it will be better. And then the other thing I would say on the backlash is that I think it's incumbent on the field, our field, the AI field and industry, to show the unequivocal benefits more clearly and more concretely. And that's what we try to do with our AI for Science work and applying AI to advancing science and medicine.
## 00:14:23
**中文**
这也是我整个职业生涯都致力于用 AI 工具推进 AlphaFold、药物发现等工作的原因。AI 可以应用于非常多的科学领域，我们正在材料科学、数学、能源等方面这样做。我认为我们需要更多展示这些成果。
**English / Original**
So that's why I've spent my whole career working on using these AI tools for things like AlphaFold, as you mentioned, and drug discovery. And I think there's so many areas of science that AI can be applied to. And we're trying to do that-- material science, mathematics, energy sources. So that's what I think we need to show more of.
## 00:14:41
**中文**
我也希望其他同行机构做得更多。迈克：德米斯，接着你谈到的沟通风格，你与《经济学人》封面上的其他“希腊神祇”不同，你以科学家身份为傲。你不是末日论者，也不是加速主义者。为什么有些 AI 领袖不断谈论 AI 的负面影响？
**English / Original**
And I hope our other peer organizations do more of as well. MIKE ALLEN: Now, Demis, taking your point about style of communication, your other fellow Greek gods on the cover of "The Economist," you're proudly a scientist. You're not a doomer. DEMIS HASSABIS: Mm-hmm. MIKE ALLEN: You're not an accelerationist. Why do some AI leaders talk about the negatives of AI?
## 00:15:06
**中文**
这对他们有什么帮助？德米斯：我处在中间，我称自己为“谨慎的乐观主义者”。我是乐观的——否则不会为此工作 30 年。我相信，只要我们不急躁，并让社会有时间在未来几年把事情做对，AI 之类技术带来的益处将远远超过负面影响。
**English / Original**
How does that help them? DEMIS HASSABIS: Well, I'm in the middle. So I call myself a cautious optimist. MIKE ALLEN: Cautious optimist. DEMIS HASSABIS: So I'm optimistic. Obviously, I'm optimistic-- otherwise, I wouldn't have been doing this for 30 years-- that the benefits are going to far outweigh the negatives with something like AI, as long as we take our time, we give ourselves the time, as society, to get this next few years right.
## 00:15:28
**中文**
如果做对了，我们谈论的是未来数百年、甚至上千年的人类繁荣。迈克：而我们所有人正在亲历它，太不可思议了。德米斯：但要做对面临很多挑战。因此我们必须给自己时间，而不是只为推出下一样东西而竞速。
**English / Original**
We're talking about the next hundreds, maybe thousand years of human flourishing, if we get this right. MIKE ALLEN: And all of us are living that. That's amazing. DEMIS HASSABIS: But there's a lot of challenges to get that right. So we've got to give ourselves the time to do it, rather than just racing to just get the next thing out.
## 00:15:51
**中文**
我认为，我们应当把注意力放在这里，并共同讨论所有这些工作将产生的更宏大影响。迈克：接下来有一段特别内容，来自拍摄德米斯和 DeepMind 长达五年的精彩纪录片《The Thinking Game》，是你们伦敦旧办公室里的一幕。
**English / Original**
And I think that's where we've got to put our attention to and come together to discuss the bigger impacts that all of this work is going to produce. MIKE ALLEN: We have a treat, a clip from "The Thinking Game," a remarkable documentary about Demis and DeepMind, filmed over five years. We have a fascinating scene from your old offices in London.
## 00:16:15
**中文**
这不是排练或重现，而是完全偶然，摄像机恰好在场。请看。【视频播放】“现在我们有了一件科学家可以实际使用的工具。”“有人来问：我研究的这种蛋白质与疟疾或某种传染病有关，我们不知道它的结构，能用 AlphaFold 解决吗？”
**English / Original**
This was not rehearsed or recreated. Totally serendipitous. Cameras just happened to be there. Let's watch. [VIDEO PLAYBACK] - Now we had a tool that can be used practically by scientists. - These people asking us, I've got this protein involved in malaria or some infectious disease. We don't know the structure. Can we use AlphaFold to solve it?
## 00:16:38
**中文**
“我们一个月就能轻松预测所有已知序列。”“一个月内预测所有已知序列？”“对，轻而易举。”“嗯。”“十亿、二十亿个，而且它们……”“那为什么不直接这么做？对，应该全部做掉。”“现在确实可以……”“这好太多了，为什么不这么做？”“这是其中一个方案。”
**English / Original**
- We can easily predict all known sequences in a month. - All known sequences in a month? - Yeah, easily. - Mm-hmm. - A billion, 2 billion. And they are-- - So why don't we just do that? Yeah, we should just do the lot. - Well, I mean, now you can really-- - That's way better. Why don't we just do that? - Well, so that's one of the options.
## 00:16:54
**中文**
“对。”“这里有……”“我们应该直接运行——这是个绝妙主意。应该把所有存在的蛋白质都跑一遍，然后发布结果。以前怎么没人建议？当然应该这么做。我们为什么还想着做一项服务，让人们提交蛋白质？直接把所有蛋白质折叠出来，再交给全世界所有人。”
**English / Original**
- Right. - There's this-- - We should just run-- that's a great idea. We should just run every protein in existence and then release that. Why didn't someone suggests this before? Of course, that's what we should do. Why are we thinking about making a service and then people submit their protein? We just fold everything and then give it to everyone in the world.
## 00:17:11
**中文**
谁知道由此会产生多少发现？【播放结束】【掌声】迈克：这是一句会流传后世的话，也是标志性时刻。我们亲眼看到 DeepMind 在运转：你说“那为什么不做？为什么不直接做？”告诉我们，你的“DeepMind”当时在想什么、现场发生了什么。【笑声】德米斯：这个具体例子也正好回应你上一个关于加速的问题。
**English / Original**
Who knows how many discoveries will be made from that? [END PLAYBACK] [APPLAUSE] MIKE ALLEN: So a quote for the ages, an iconic moment. We saw the DeepMind working, Demis, when you said, so why don't we do that? So why don't we just do that? Tell us what was in your DeepMind and what was happening. [LAUGHTER] DEMIS HASSABIS: So look, this is the-- that particular example-- and actually, I'll just get back to your last question as well about acceleration.
## 00:17:48
**中文**
在我看来，这正是我们想要的加速。就某种意义而言，我整个职业生涯所做的事情已经是极致加速。今天社会面临许多挑战，从疾病、能源到环境生态系统和气候变化。我们需要 AI 这样的技术帮助解决这些其他问题。
**English / Original**
This is the type acceleration that we want in my opinion. So you couldn't get any more accelerations, in a sense, than what I spent my whole career doing. I think there are so many challenges facing society today, from disease to energy, our environmental ecosystems, climate change. We need technology like AI to come and help us solve these other problems.
## 00:18:13
**中文**
其中最重要的，正如我昨天所说，是用 AI 改善人类健康。借助 AlphaFold，蛋白质折叠和蛋白质结构预测是开发药物、治疗疾病的关键环节之一——并非唯一环节，却是关键环节。当时我在那场会议上突然算明白了，因为 AlphaFold 不仅非常准确，
**English / Original**
And the number one, in my opinion, as I said yesterday, is applying AI to improving human health. With AlphaFold, protein folding and predicting protein structures is one of the critical pieces of developing drugs to help with these diseases-- only one piece but a critical piece. And what was happening there is I just sort of worked out because AlphaFold was-- literally, in that meeting-- because AlphaFold was so accurate.
## 00:18:38
**中文**
而且速度极快，几秒钟就能折叠一种蛋白质。我知道科学界已知蛋白质有数亿种。粗略估算后，我意识到，只要投入一定数量的计算机，一年内就能折叠全部蛋白质。因此没有必要采用通常做法：搭建服务器，让人们提交自己关注的蛋白质的遗传序列、氨基酸序列。
**English / Original**
But it's also so fast. So it could fold a protein in a matter of seconds. And then I knew there were a couple hundred million proteins known to science. And just the back of envelope calculation, I could feel that if we put a certain amount of computers on it, we could fold all of them in a year. So there was no need to set up the normal way of doing this, which is like a server and have people submit their genetic sequences, the amino asset sequences of the protein they cared about.
## 00:19:06
**中文**
传统上，你会花一周去折叠它，再把结构返回给研究者。我们可以直接把所有结果免费发布到世界上，存入由剑桥的欧洲生物信息研究所运营的大型数据库，让全球所有研究人员几乎以 Google 搜索的速度访问任意蛋白质。
**English / Original**
And then you'd go away and fold it for a week and give them back the structure. We could just put all of it out there, for free, into the world, on a huge database run by the European Bioinformatics Institute in Cambridge, and just let everybody around the world, all the researchers around the world, access whatever protein they wanted, almost at the speed of a Google Search.
## 00:19:28
**中文**
对我们而言，很明显，这是这项突破能够产生的最大影响，是“良性加速”的例子。另一方面，我也不属于那种盲目乐观、认为毫无风险、尽管放手推进、完全不存在危险的阵营。当我们把 AGI 这样意义重大的事物带入世界、进入一个新时代时，必须极其谨慎地想清楚。
**English / Original**
So that, to us, felt like-- it was obvious, to me, that was the biggest impact we could do with this breakthrough. So I think that's an example of great acceleration. But on the other hand, I'm not in the camp of the Pollyanna camp of, well, there's no risk here. Just let it rip. There's no danger. Of course, when we're bringing in something this momentous, as something like AGI-- so a new era we're going into, a new-- we have to think that through, very carefully.
## 00:19:56
**中文**
一定会有挑战和风险，也会有办法尝试缓解，但前提仍是我们集体给自己足够的时间。迈克：摄像机跟随你五年，而你也愿意让他们进入，这说明你意识到自己身处一个将塑造人类的前沿。
**English / Original**
And there are going to be challenges and risks. And there'll be ways which we can try and mitigate that but, again, only if we give ourselves enough time, collectively, to do that. MIKE ALLEN: The fact that cameras followed you for five years, the fact that you're willing to let them in, you recognize that you're on the frontier of something that will shape humanity.
## 00:20:17
**中文**
人很难像旁观者那样看自己。你从纪录片的拍摄过程或实际观看中学到了什么？德米斯：其实我非常讨厌在镜头里看到自己，所以答应这件事并不容易。不过，这是拍摄《AlphaGo》电影的同一团队——有些人可能看过那部记录 AlphaGo 对局的影片。
**English / Original**
It's hard to see ourselves as others see us. What did you learn from the documentary, the process, or actually watching it? DEMIS HASSABIS: Well, I actually hate seeing myself on camera, so it was a hard thing to agree to. But it's the same film crew that did the "AlphaGo" movie-- if some of you have seen that, for the AlphaGo match.
## 00:20:37
**中文**
他们在最后一刻加入拍摄。我认为那是一部非常感人的纪录片，记录了现代 AI 历史上相当重要的时刻：我们与李世石的那场大赛。我们刚纪念了它十周年，实在令人难以置信。有时感觉仿佛过去了一个世纪。
**English / Original**
And they came along right at the last minute to do that. And I thought it was a really moving documentary recording of quite an important moment in sort of modern AI history, the big match we had against Lee Sedol. We just celebrated the 10-year anniversary of that, which is kind of unbelievable, really. In some ways, it feels like a century has gone by.
## 00:20:58
**中文**
有时又像昨天刚发生。后来他们提议以完全不干预的方式旁观拍摄，我们选择信任他们。他们恰好在那样的关键时刻在场，确实很神奇。他们只是偶尔过来记录一些事情，碰巧就在那场会议里。
**English / Original**
In other ways, it feels like yesterday. And then they proposed to, basically, be a kind of fly on the wall. And we trusted them with that. And they happened to be around at key moments like that one. It was kind of amazing. They happened to be in that meeting because then they come over, every now and again, and record a few things.
## 00:21:17
**中文**
我们同意拍摄的原因之一，是我们认为这项技术会带来社会层面的转型。人们会想了解技术背后的人、他们的动机、为什么这样做，以及我们认为它能带来哪些益处。我希望纪录片展现了这些。
**English / Original**
And one of the reasons we agreed to do that is we felt that this is a societal-level transformation, this technology. We felt that people would want to know something about the people behind it, what their motivations were, why were they doing this, what do we think the good that can come from it. And I hope that documentary shows that.
## 00:21:39
**中文**
它得到的反响似乎不错。我认为摄制组很好地呈现了我们的动机和热情，例如 AI for Science。迈克：快速游览一下你的思想和世界——仿真与机器人。那里正在发生什么？为什么仿真是如此关键的里程碑？德米斯：我认为仿真非常重要。
**English / Original**
It seems to have gone down well and I think does portray-- they've done a good job of portraying what our motivations and passions are, things like AI for Science. MIKE ALLEN: Demis, a quick tour of your mind and world-- simulations and robotics. What's going on and why are simulations such a key milestone? DEMIS HASSABIS: I think simulations are really important.
## 00:22:01
**中文**
我对仿真的着迷与对 AI 一样由来已久。事实上，我编写的第一个真正专业的作品是 20 世纪 90 年代的游戏《主题公园》；它既包含 AI，也是一款仿真游戏，是这两方面的早期作品之一，于 1994 年发行。我一直认为二者高度互补：一方面，可以用 AI 学习复杂现象的仿真。例如我们昨天展示的 WeatherNext 系统，它预测飓风等极端天气路径的速度和准确性都高于传统系统。
**English / Original**
I've been sort of enamored with simulations as long as I've had with AI. In fact, the first real professional thing I programmed was "Theme Park," this game I wrote in the '90s, which was both AI, had AI in it, and it was a simulation game, one of the first for both. It came out in 1994. And so I've always seen them as quite complementary, so both in the sense that you can use AI to learn simulations about complex phenomena-- so for example, our WeatherNext system that we showcased yesterday that helps predict the paths of hurricanes and other extreme weather events, much more quickly and more accurately than traditional systems.
## 00:22:42
**中文**
另一方面，仿真还能为 AI 系统提供数据。例如，我们一直使用交互式 3D 模型 Genie，在现实中几乎不可能碰到的“十亿分之一”异常情境中测试 Waymo 汽车。比如行驶时周围突发森林火灾、一架双翼飞机紧急降落在高速公路上，或一头大象突然出现在道路上。
**English / Original**
But also, it can provide data for AI systems. So for example, we've been using Genie, our interactive 3D model, to test Waymo cars in very unusual one-in-a-billion-type situations that you would never see in the real world. So for example, what if you're driving down a road and there's a forest fire around you, or a biplane has done an emergency landing on the motorway, or an elephant's appeared in the road?
## 00:23:13
**中文**
这些情形绝不会出现在日常训练数据中，但你需要知道系统会如何反应。因此，这类仿真非常适合测试。如果系统表现不佳，还可以用仿真生成更多数据供其学习。迈克：好的。
**English / Original**
These kind of things, which you're never going to see in your everyday training data, but you want to know, how is your system going to react? And so these types of simulations are very useful for testing that. And if your system doesn't do well there, you can use it to generate more data that you can learn from. MIKE ALLEN: OK.
## 00:23:33
**中文**
这会是你最喜欢的问题。我知道你喜欢谈科学方法。关于 AI for Science 与健康——你说过，人类健康是 AI 的终极应用场景。德米斯：是的，从极限意义上说，我认为 AI 是科学的终极工具。作为科学家，我开启整段旅程就是为了更好地理解周围世界。
**English / Original**
This is going to be your favorite question. I know you love to talk about the scientific method. AI for science and health-- and you've talked about human health as the ultimate use case for AI. DEMIS HASSABIS: Yes, so I think that the AI is the ultimate tool for science in the limit. It's why I started this whole journey was to try, and as a scientist, understand better the world around us.
## 00:23:59
**中文**
我当时认为，我们可以借助一件非凡工具，帮助即使最聪明的专家也更快梳理所收集数据的全部复杂性，并从中发现联系、洞见与结构。还有什么工具会比 AI 更合适？现在这正逐渐成为现实。看到各领域的科学进程加快，是非常令人兴奋的时刻；许多领域像蛋白质折叠一样，已经停滞了三四十年。
**English / Original**
And I felt that our minds, we could use an incredible tool to help even the smartest experts more quickly navigate all the complexity of the data that we collect and try and find connections and insights and structure in that data. And what better tool than AI to do that? And I think that's coming to fruition now. And it's just a very, very exciting time to see the pace of science pick up in all sorts of fields-- many fields where we've been stuck for 30 or 40 years, like we were with protein folding.
## 00:24:32
**中文**
我认为未来十年左右会看到许多这样的突破。迈克：现实世界中的 AI——人们已经在哪些方面感受到它？AI 反弹是否部分源于人们没有意识到它？德米斯：我们几乎一直在所使用的每种产品里使用 AI。
**English / Original**
And I think we're going to see a lot of those breakthroughs over the next decade or so. MIKE ALLEN: AI in the real world-- what are some of the ways that people are already feeling it and is part of the AI lash that they don't realize it? DEMIS HASSABIS: Well, I mean, we're using AI all the time in pretty much every product that we use.
## 00:24:53
**中文**
从我们的角度看，Maps 到 Gmail 的所有产品都已经得到增强，或正在利用 AI 升级。我希望随着下一代助手出现——比如昨天谈到的 Gemini Spark，以及眼镜、智能眼镜——它们最终能把时间还给我们。这就是我的希望。
**English / Original**
So everything, from our point of view, Maps to Gmail has now been enhanced and is in the middle of being upgraded with AI. What I hope to see is with the next generation of assistants-- so you saw us talk about Gemini Spark yesterday but also the glasses, the smart glasses-- is, eventually, this will give us back time. That's what I hope.
## 00:25:17
**中文**
我希望 AI 助手越聪明，我们要处理的行政杂务就越少，也越不必把时间耗在大量琐碎技术事务上，从而把时间还给我们，专注于真正重要的事：创造、家人，以及与其他人真实连接。我希望未来几年会发生这些变化。
**English / Original**
I hope that the smarter our AI assistants are, the less admin we need to do, the less time we actually need to spend with a lot of the technologies that are mundane and actually give us time back to focus on what we think is important, whether that's creativity, our families, and just connecting with other humans. That's, I think, in the next few years, what I hope will happen.
## 00:25:40
**中文**
与此同时，我们仍能保持连接，获得工作所需或感兴趣的信息，只是能在自己想要的时间更方便地获取，因为会有 AI 助手帮助管理这一切。迈克：回看昨天 Google I/O 主题演讲中的全部发布，主旨演讲把许多内容串联了起来。
**English / Original**
But we can still stay connected and get the information that we need to do our jobs or that interests us but more conveniently at the time that we want because we'll have an AI assistant that's helping us manage all of that. MIKE ALLEN: If you look at all the keynote announcements yesterday here at Google I/O, the keynote put a bow around a lot of it.
## 00:26:04
**中文**
但信息量也很大。Axios 的 Ina Fried 写了一个很好的标题：“AI 无处不在”。先按暂停键。在这些发布中，我们最该记住、聚焦什么？德米斯：最主要的是，智能体时代真的已经到来。这届 I/O 最让我们兴奋的是，正在把它整合进整个 Google 生态系统。
**English / Original**
But it was also a lot to take in. Ina Fried at Axios, had a great headline about AI everywhere. Hit pause. And what's something from these announcements that we should remember, focus on? DEMIS HASSABIS: I think the main thing is that the agentic era is really here. And I think what we were really excited about this I/O is how we're integrating that across the Google ecosystem.
## 00:26:31
**中文**
例如 Workspace 和人们每天使用并喜爱的所有工具，怎样与智能体系统安全交互？我们已经看到智能体的潜力，但从编程一直到管理行政负担，我们才刚刚触及它们能力的表面。我对这一切非常期待。
**English / Original**
So with Workspace, with all of the things that people use every day and love, how is that going to interact, securely, with an agentic system? I think we've seen the potential of agents, but I think we're just scratching the surface of what they can be, from coding to all the way to managing your admin load. I'm very excited about all of those things.
## 00:26:56
**中文**
此外，世界模型显然具有帮助现实世界的潜力；希望明年我们会谈到机器人等领域的一些重大进展。迈克：那么责任呢？你在工作中如何看待它？它会抑制还是加速发展？
**English / Original**
And then just, obviously, the potential of these world models to help in the real world, with things like-- hopefully, we'll be talking about this next year-- some big advances in areas like robotics. MIKE ALLEN: And responsibility-- how do you think about that in your work? Does it inhibit? Does it accelerate? How do you think about it?
## 00:27:16
**中文**
德米斯：责任至关重要，这并不真正是抑制还是加速的问题，而是我们所做一切的关键组成部分。长期从事这种技术并思考其影响时，我们当然期待它带来巨大而惊人的益处；但与此同时，它也是一种双重用途技术。
**English / Original**
DEMIS HASSABIS: I think it's a critical-- it's not really about inhibiting or accelerate. It's a critical part of everything we do. When you're working on this kind of technology, which we've been for a long time, and you're thinking about the implications, both, obviously, the enormous, amazing benefits that we're hoping for-- but that comes with-- it's a dual-purpose technology.
## 00:27:38
**中文**
这些能力也可能被恶意行为者改用于有害目的。因此，身处前沿、理解技术可能带来的正负后果的人，应当认真权衡两方面。迈克：我们旅程的最后一站是未来愿景。请告诉我们，帷幕后有哪些你所知道的事情，以及未来几个月应关注什么。
**English / Original**
Those things can be also repurposed by bad actors for harmful ends. So those two things weigh heavily on us, or should do, that are at the forefront in the frontier, and understand both the negative and the positive consequences of what this technology can do. MIKE ALLEN: And the last stop on our tour, future vision. Tell us what you know is behind the curtain and what we should be watching for in, let's say, coming months.
## 00:28:09
**中文**
德米斯：目前所有人都在研究的一件大事是“自我改进”。我们已经看到它的较慢版本，例如编程智能体帮助工程师提高编程能力和生产率。问题是下一层是什么？编程和数学是相当特殊的情况，因为它们的输出可以验证。
**English / Original**
DEMIS HASSABIS: Well, the big thing that everyone's working at the moment on is this idea of self-improvement. So of course, we're seeing a sort of a slow version of that, with coding agents helping our own engineers get better and more productive with coding. The question is, is like, what's the next level of that? And I think coding and mathematics are quite special cases because the outputs are verifiable.
## 00:28:36
**中文**
你能验证答案是否正确，也能按需生成任意数量的合成数据。有证据表明，这会形成不断复利的飞轮，是所有领先实验室都在重金投入的有趣领域。另一个方向是提高系统的可靠性、稳健性和一致性，包括其工作方式、工具使用、指令遵循，并确保它们执行长期计划时可靠。
**English / Original**
You can verify whether they're correct. And you can also generate as much synthetic data as you want. So I think there's evidence that this is a kind of compounding flywheel. So I think that's a very interesting space that all the leading labs are investing heavily on. And then I think the other area is just to build the reliability and robustness of these systems and consistency, really, of how these systems work, tool use, and instruction following, and make sure that they're reliable over long-term plans.
## 00:29:11
**中文**
我认为这会成为下一次阶跃式变化，其中一部分是确保系统稳健、安全且有保障。智能体越强大、我们委托给它们的任务越多，就越必须从网络安全角度确保它们严格执行预期行为、过程可被监控，并以安全方式完成任务。
**English / Original**
So I think, again, that's going to be the next step change for these things. And part of that is making sure that they're robust and safe and secure. I think the more powerful these agents get and the more we delegate tasks to them, the more we have to make sure, from a cybersecurity standpoint, they're doing exactly what we're expecting them to do-- and we can monitor that-- and they're being done securely.
## 00:29:35
**中文**
迈克：这是理解阶跃式变化的好框架，你提到了一致性。那么以 2026 年为例，阶跃式变化会分成哪些方面？德米斯：首先是一致性。随着接近 AGI，我们不应再看到系统在某些提问方式下表现卓越，却仍会被另一些方式难倒。
**English / Original**
MIKE ALLEN: OK. So that's a good frame way to think about step changes. And you pointed to consistency, right? What would our step change buckets be for, say, 2026? DEMIS HASSABIS: Well, consistency, we shouldn't be seeing-- as we get closer to something like AGI, we shouldn't be seeing systems that are brilliant at certain things when posed in certain ways, but they're still tripped up.
## 00:29:56
**中文**
比如给它一个更简单的数学问题，却用它没见过、稍微偏离分布的方式表述，它就失败。AGI——通用智能——不应如此，人类智能也不是这样。如果一个人擅长某类游戏或任务，再给他同一领域中严格意义上更简单的问题，他也能解决。
**English / Original**
If you present them with a simpler problem, say, in mathematics, but you present it in a certain way that it hasn't-- it's a little bit more out of distribution. And I think that shouldn't be the case for an AGI system, a general intelligence. That's not the case with human intelligence. If you're good at one sort of game or one kind of a task and you give that person a strictly simpler problem in the same sort of domain, they'll be able to solve that problem too.
## 00:30:28
**中文**
它不应呈现这种参差不齐的“锯齿状智能”。另一个仍然缺失、我们也在聚焦的重要领域是持续学习：系统完成训练并投放真实世界后，如何继续学习？怎样进一步适应和学习？这也与个性化有关，要让系统按照用户期望的程度适配其具体情境。
**English / Original**
But there won't be this kind of jagged intelligence. I think another big area that's missing and we're focusing on, too, is continual learning. How do you have systems that learn after their training, after they've been released out into the wild? How can they adapt and learn further? And that's also related to personalization and making sure that the systems are kind of personalized to the extent that you want, as the user, to your particular context.
## 00:30:55
**中文**
迈克：最后一个未来问题。一年后的 Google I/O 2027，我们会谈论什么？核心主题是什么？德米斯：现实世界中会有更多智能体嵌入工作流程。现在已经开始出现，未来一年会逐渐成熟。
**English / Original**
MIKE ALLEN: Last one on the future. A year from now, at Google I/O 2027, what do you think we'll be talking about? What will be the centerpiece? DEMIS HASSABIS: I think there'll be a lot more agents in the real world, kind of embedded in our workflows. I think we're starting to see that now. I think, over the next year, that will mature.
## 00:31:18
**中文**
这会是一件大事。我也希望现实世界中的 AI 出现重大进展：把能够理解世界的模型用于机器人领域，实现显著跃迁。例如 Gemini Robotics——目前最先进的机器人模型——将被用于许多不同形态的机器人。
**English / Original**
So I think that will be a huge thing. And I hope, also, there'll be big advances in AI in the real world. So I'm hoping we'll see some big leaps forward in applying these kinds of models that understand the world, Gemini robotics, the SOTA robotics model out there, right now, but applied to many different form factors in robotics.
## 00:31:42
**中文**
迈克：关于现实世界中的 AI，你举过飓风“梅利莎”的例子，以及 AI 如何用于天气预报。德米斯：是的，这是我们昨天展示的一个例子，说明如何以惊人的方式运用 AI 和仿真。
**English / Original**
MIKE ALLEN: And AI in the real world-- I think something you've talked about as an example, Hurricane Melissa-- DEMIS HASSABIS: Mm-hmm. MIKE ALLEN: --and how AI can be used in weather forecasting? DEMIS HASSABIS: Yes, so that was one of the examples we showed yesterday of an incredible way that we can use AI and simulation.
## 00:32:02
**中文**
天气是非常复杂的动态系统，传统方法要解大量方程，包括纳维－斯托克斯方程和流体动力学。这类传统系统需要数周运行，能向前预测的时间也有限。面对飓风等极端天气事件，你希望尽早知道结果；正如昨天视频所示，即使只提前一天，对应急服务、减少人员伤亡和财产损失也极其重要。
**English / Original**
So weather, I think it's a very complicated, dynamic system, involves solving a lot of equations in the traditional way, Navier-Stokes equations, fluid dynamics. So these kinds of traditional systems take weeks to run. And they're kind of limited in how far ahead they can look. And for a situation like an extreme weather event, like a hurricane, you want to know-- and as we showed in the video yesterday, even a day ahead of time is very important for emergency services and reducing the damage to human lives, and also, property that these extreme weather events come with.
## 00:32:42
**中文**
这只是我们取得重大进展的一个例子：让 AI 从现有数据中学习仿真，而不是人工直接编写仿真的力学机制。迈克：你在获得诺贝尔奖时接受采访，给年轻时的自己建议说：“相信自己的想法，即便它们看起来相当异想天开。”
**English / Original**
So I think that's just one example of the big strides we're making with applying AI to learn simulations from available data, rather than directly programming the mechanics of a simulation by hand. MIKE ALLEN: So your advice to your younger self-- in an interview that you gave in connection when you were awarded the Nobel Prize, you said believe in your ideas, even though they can seem quite outlandish.
## 00:33:09
**中文**
你说自己确实这样做了。AI 如何让这句话更加成立？你曾说，从最早阶段就对想法抱有信心会决定一切。德米斯：要想有机会实现任何宏大目标，你都需要对自己的想法有信心，这一点非常明确。
**English / Original**
And you said you, personally, had done that. How has AI made that even more true? You said having confidence in your ideas, from the very earliest stage, makes all the difference. DEMIS HASSABIS: Well, I think you need to have confidence in your ideas on anything big if you're going to stand a chance of achieving it. That's very clear.
## 00:33:31
**中文**
但我认为不只是信心，还有热情，以及对这项工作重要性的信念。今天 AI 是全世界谈论的头等大事；早在 2010 年创办 DeepMind 乃至更早之前，我们一直设想这可能会发生。
**English / Original**
But I also think that it's not really just about confidence. It's also your passion and your belief in how important that that work is. So when I talk about AI, for example, obviously, we're here today where AI is the biggest thing that the world's talking about. And I think that we always imagined, back when we started DeepMind in 2010 and before then, that that could happen.
## 00:33:53
**中文**
但即使没有发生——即使 AI 仍只是相对小众的学术课题——我现在也依然会研究 AI，因为我认为它是最迷人、最重要的问题。事实证明时机是对的。当时我们认为这是一项大约 20 年的使命。
**English / Original**
But I would have also been still working on AI now, whether or not that had happened-- so if it was still a more of a niche, you could say, academic subject-- because I think it was the most fascinating problem and most important problem to be working on. It's worked out. The timing was right. We thought, back then, that it was a kind of 20-year mission.
## 00:34:13
**中文**
我们的进度几乎完全符合计划。但无论如何，我都会做这件事。因此，这不止是信心，而是近乎痴迷于这个问题，相信它是正确且最有趣的问题——至少对我的思维方式和热情而言如此。建设这件非凡工具，也是我推进科学的表达方式；而它本身又是一件有趣的科学产物。
**English / Original**
We're sort of exactly on schedule. But I would have been doing it anyway. So it's a little bit beyond confidence. It's a sense of almost obsession with that problem and it being the right problem and the most interesting problem, at least for my mind and the way that my mind works and the passions that I had. That was my expression of trying to advance science was to build this amazing tool that was also an interesting scientific artifact, in and of itself.
## 00:34:40
**中文**
还有什么会比把一生和职业投入其中更有趣？30 年前刚开始这一切时，我就是这样想的。迈克：德米斯，你为何选择这条路，历史学家将会研究。塞巴斯蒂安·马拉比的新书《The Infinity Machine》提到，你在伦敦一家酒吧里与他聊了 30 多个小时。
**English / Original**
So what more an interesting thing could one spend your life and career on? That's how I felt 30 years ago when I first started on all of this. MIKE ALLEN: Yeah, so, Demis, how you chose that road-- and historians will study this, but Sebastian Mallaby is out with a great book, "The Infinity Machine." And he said that you spent 30-plus hours, more than 30 hours with him at a London pub.
## 00:35:05
**中文**
你可能是历史上第一位接受传记作者访谈 30 小时却不后悔的人。这本书把你的选择归结为：你决定用自己的头脑和能力理解宇宙。请讲讲你选择的路，以及没有选择的路。德米斯：先说明一下，我们在酒吧里没喝酒。
**English / Original**
I will say that you might be the first biographee in history to spend 30 hours and not regret it. But this book boils down your choice to-- you chose to use your mind, your skills to understand the universe. Tell us that road taken and roads that you didn't take. DEMIS HASSABIS: Yeah, I should say, we weren't drinking in the pub.
## 00:35:28
**中文**
30 个小时只喝咖啡。迈克：爱尔兰咖啡？德米斯：不是。时间过得很快，因为我们很谈得来。我其实没计时，是他提到这个数字。我们进行了非常有趣、话题极广的讨论。对我来说，踏上这段旅程是因为一直着迷于科学中的宏大问题：现实的本质、意识的本质。
**English / Original**
It was just coffee for 30 hours. MIKE ALLEN: Irish? DEMIS HASSABIS: So no, it went fast because we actually got on really well. And I mean, I actually wasn't counting the hours, but he suggested that. And we had a very fun discussion around-- very wide ranging on all sorts of topics. And for me, I started off on this journey because I've always been fascinated by the big questions in science, the nature of reality, the nature of consciousness.
## 00:35:55
**中文**
我甚至一直有点困惑：大多数人一生中并不会停下来真正思考这些就在眼前的宏大问题。比如时间是什么？我们并不知道。我们只有一种含糊的理解：它与熵的方向有关，而且是单向的。
**English / Original**
And I've always been sort of almost puzzled, actually, that when we go through life-- and most of us-- we don't really stop to really ponder those big questions, even though they're staring us in the face. Like, what is time? We don't know what time is. We have some kind of hand-wavy idea about it. It's a direction of entropy and it's one way.
## 00:36:15
**中文**
但我们并不真正知道时间是什么，这令我非常惊讶，更不用说引力、量子力学等谜团。我从记事起就对这些问题着迷。通常，对这些问题感兴趣的人会进入物理学；物理也是我在学校最喜欢的科目，但我开始阅读所有伟大科学家的传记。
**English / Original**
But we don't really know what it is, which is kind of astounding to me, let alone gravity, quantum mechanics, all of these mysteries. And so I've always been fascinated by that since I can remember. And normally, when you're fascinated by those questions, you go into physics. So physics was my favorite subject at school, but I started reading biographies of all the greats.
## 00:36:36
**中文**
我最喜欢费曼，当然还有爱因斯坦、温伯格等杰出物理学家。我意识到——至少在我看来——战后、尤其 20 世纪 70 年代以后，我们并未取得太大进展；而 20 世纪初，爱因斯坦、海森堡等人曾推动巨大进步。
**English / Original**
My favorites are, like, Feynman and, obviously, Einstein, Weinberg, all of these types of brilliant physicists. And what I realized is that-- or at least for me, post-war, post-1970s, we hadn't really made much progress, in my opinion, on-- we made a lot of progress at the beginning of the 1900s, like Einstein and Heisenberg and so on.
## 00:36:57
**中文**
到了 20 世纪后半叶，进展却没那么多。我开始思考原因，感觉也许即使是费曼这样的绝顶聪明头脑，在没有新工具帮助时，也正在接近人类心智能够领会和理解的极限。
**English / Original**
But then, towards this latter part of the 20th century, we hadn't made that much progress. And I was kind of wondering why this was. And I felt that it was like, maybe we're reaching the limit of what the human mind can comprehend and understand, even the most brilliant minds, like people like Feynman, without the aid of some new tools.
## 00:37:18
**中文**
这就是我阅读那些传记后的真实感受和主要结论。再加上大量科幻作品——阿西莫夫的《基地》系列、伊恩·班克斯的《文化》系列——以及《哥德尔、艾舍尔、巴赫》等哲学著作；那本书对我影响很大。所有线索汇聚起来：我们可以尝试直接回答“智能和意识的本质是什么？”
**English / Original**
So that was my real feeling. That was my kind of takeaway, I suppose, from reading some of those biographies. And then combining that with reading a lot of science fiction-- Asimov's "Foundation" series, Iain Banks's "Culture" series, and then philosophy books like "Godel, Escher, Bach"-- which was very formative for me, reading that book-- it all kind of came together in that, well, we could try and directly answer some of these questions about, What's the nature of intelligence and consciousness?
## 00:37:47
**中文**
方法是构建 AI，并把它与人类心智和大脑比较。同时，AI 本身也能成为用于科学、推进人类知识前沿的工具。因此在 30 多年前，对我而言这显然是最有趣、也可能最有价值的前进道路。迈克：受你关于传记的观点启发，当今仍在世、在工作且不属于 Google 的科学家中，有谁应受到更多关注、令你敬佩，或未来会有人为其作传？
**English / Original**
by building AI and comparing it to the human mind, the human brain. But also, it would be a tool, itself, that could be used for science and to advance the frontiers of what we know. So it was sort of obvious to me, 30-plus years ago, that that would be the most interesting and perhaps most valuable path forward. MIKE ALLEN: Inspired by the point you made about biographies, who's a scientist, living, working today, who doesn't work for Google, who either should get more attention, or you admire, or future biographies will be written about?
## 00:38:20
**中文**
德米斯：优秀科学家太多了。我不知道你是不是在问谁被低估了。许多重要的生物学家其实已经很知名，也获得过诺贝尔奖，例如保罗·纳斯和詹妮弗·杜德纳。他们是非凡的科学家，发明了 CRISPR 等技术。
**English / Original**
DEMIS HASSABIS: I mean, there's so many great scientists out there. I don't know if you're asking me who's sort of underrated. But I do feel like a lot of the big biology scientists-- I mean, they're well known, right? They've won Nobel prizes, like Paul Nurse and Jennifer Doudna. These are amazing scientists inventing CRISPR, these kinds of technologies.
## 00:38:41
**中文**
我迫不及待想看到下一代如何使用 AI 工具走出自己的道路，并借助这些非凡工具取得自己的重大突破。迈克：最后，我们给现场、全球以及未来会反复观看这场对话的建设者和未来建设者留下一条实用建议。
**English / Original**
And I think the next generation, I can't wait to see what they're going to do with the AI tools and make their own way and make their own big breakthroughs, with the aid of these amazing tools that we have. MIKE ALLEN: Demis, we're going to finish on a useful note for the builders and future builders in this room, watching around the world and will watch this conversation in perpetuity.
## 00:39:03
**中文**
你大部分时间在伦敦，但来到湾区时，能明显感到建设者的能量，这让我们所有人都很兴奋。许多人生活在这种能量里、创造着它。不过你也对大家发出警告：你感受到一种狂躁。德米斯：这里的能量确实很惊人。我大约一半时间在这里，另一半在伦敦。
**English / Original**
You spend most of your time in London. But when you come to the Bay Area, you feel a palpable builder energy, which is exciting for all of us. When we visit, a lot of people here are living it, creating that. But you also have a warning for all of us. You detect a freneticism. DEMIS HASSABIS: Yeah, it's amazing energy. I mean, I spend about half my time out here and half in London.
## 00:39:30
**中文**
融入这里建设者和创业生态系统的能量总是很棒，它显然举世无双。人们的热情以及对机会的拥抱令人惊叹。但正如你所说，我也察觉到一种狂热的急迫感，而这不利于长时间深入思考困难问题。
**English / Original**
It's always great to plug into the energy of the builder and entrepreneurial ecosystem out here. Obviously, it's second to none. And it's incredible to see that enthusiasm and that embracing of the opportunities. But I would also say I do also detect, as you say, this kind of frenetic rush as well. And I think that that's not conducive to thinking deeply about hard topics for a long amount of time.
## 00:40:02
**中文**
许多下一步进展会来自运用 AI 的深科技，也许是把 AI 用于另一个硬科学领域，那里机会很多。但你往往必须持续投入许多年才能看到成果。因此，人们需要一些更安静、能够反思的时刻，深入考虑自己的规划，而不是看到下一个事物就冲过去。很多时候，速度被置于方向之上。
**English / Original**
So I think a lot of the next advances are going to be in deep tech using AI, maybe applying to another area of hard science. I think there's a lot of opportunities there. And you're going to have to go at that for many, many years before there's a sort of result. So I think having some sort of quieter, reflective moments where you can think very deeply about what you're planning, not just rushing in the next thing that you see, the kind of-- I think a lot of the times, speed is favored over the direction.
## 00:40:37
**中文**
确保方向正确，往往比单纯的速度更重要。速度和速率都重要，但如果以每小时 100 英里的速度朝错误方向奔跑，实际上还不如站着不动，花一点时间确定正确方向。我常觉得这里对“速率”本身有些过度迷恋。
**English / Original**
And making sure you have the direction right is often more important than just the speed. Speed is important. Velocity is important. But if you're running 100 miles an hour in the wrong direction, that's actually worse than standing still and taking a moment to think about getting the direction right. So I often think there's an obsession, a bit of an obsession, out here with just velocity.
## 00:40:57
**中文**
速率确实关键，我不是说它不重要；但更关键的是速度向量的方向。迈克：非常实用，谢谢。Axios 总以一个轻松问题收尾。我想问“长寿逃逸速度”：其观点是，只要你我再活五年，或许就能永远活下去。
**English / Original**
And it's critical. So I'm not saying that's not important. But more critical is the direction of that velocity vector. MIKE ALLEN: Very useful. Thank you. So at Axios, we always finish with one fun thing. So I wanted to ask you about longevity escape velocity. The idea is, if you and I can live five more years, say, we'll live forever.
## 00:41:20
**中文**
你多大程度相信这个说法？你在做什么以确保自己能赶上？德米斯：我不太担心这类问题。面对这些事我非常务实。我希望通过 Isomorphic 以及 AlphaFold 所做的，是利用这些技术治愈严重疾病。
**English / Original**
How much stock do you put in that notion? And what are you doing to make sure that you make it? DEMIS HASSABIS: I don't really worry about that kind of question. I'm very pragmatic when it comes to these things. So what I'm trying to do with Isomorphic and what we try to do with AlphaFold is use these technologies to cure terrible diseases.
## 00:41:38
**中文**
这是我能想到的 AI 第一用途。至于衰老，我知道有许多衰老研究者和科学家，也有许多公司在努力研究；关于衰老究竟是什么，理论很多：它是所有疾病的汇总？本身就是一种疾病？还是需要重新编程？
**English / Original**
That's the number one thing I can think of using AI for. Now, when it comes to aging, there's, I know, lots of aging researchers and scientists. There are lots of companies trying to work on this. There's lots of theories about what it is. Is it the culmination of all diseases? Is it a disease in itself? Does it need to be reprogrammed?
## 00:41:55
**中文**
存在许多理论。我认为应先从解决我们已经知道的严重疾病开始，这会帮助人们活得更长、更健康。然后再看看还剩什么，并从那里继续。迈克：你个人最在意的健康习惯是什么？有时会戴 WHOOP 之类设备吗？德米斯：有时会。
**English / Original**
There's a lot of theories out there. I think that we should start with just by solving existing terrible diseases we know about. That will help us live longer and more healthy lives. And then let's see what's remaining. And then we'll take it from there. MIKE ALLEN: And what is your personal health obsession? So sometimes you have a whoop, right, or what is-- DEMIS HASSABIS: Yeah, sometimes.
## 00:42:13
**中文**
我会在少得可怜的空闲时间里尽量保持健康。我睡得不够，这是我必须努力解决的主要问题之一。迈克：你平均睡多久？德米斯：我尽量睡六小时，但有时做不到。【笑】迈克：感谢 Google I/O。
**English / Original**
Yeah. No, well, I try to stay as healthy as possible in the very few spare minutes I don't have. I don't sleep enough. That's one of the main issues I've got to try and address. MIKE ALLEN: What's your average sleep? DEMIS HASSABIS: Well, I try to get six hours, but sometimes I fail. [LAUGHS] MIKE ALLEN: Thank you to Google I/O.
## 00:42:32
**中文**
也感谢德米斯·哈萨比斯带来这场精彩对话。德米斯：谢谢你，迈克。谢谢，谢谢大家。【掌声】【音乐】
**English / Original**
And, Demis Hassabis, thanks for an incredible conversation. DEMIS HASSABIS: Thanks, Mike. Thank you. Thank you. [APPLAUSE] [MUSIC PLAYING]