书童按：本篇是德米斯·哈萨比斯（Demis Hassabis）爵士于2025年7月接受Lex Fridman的播客采访实录，他完全可被视为全世界最懂人工智能的寥寥数人其中之一。其采访中涉及进化系统、低维流形可学习性、信息比物质和能量更基本、开放世界游戏等观点，精彩绝伦，令人击节称赞。初稿采用Deepseek翻译，经自动化中英混排，书童仅做简单校对及批注，中文全部译文多达4万余字，分为上、中、下三个部分发出，以飨诸君。
本文系采访实录之中篇，上篇请移步此链接。
生命的起源
Origin of life
Lex Fridman (00:46:00) 我为接下来像瘾君子一样的问题道歉，但你认为我们将能够模拟一个模型，生命的起源吗？所以能够第一次模拟从非生命有机体，到生命有机体的诞生？
Lex Fridman (00:46:00) I apologize for the pothead questions ahead of time, but do you think we’ll be able to simulate a model, the origin of life? So being able to simulate the first from non-living organisms, the birth of a living organism?
Demis Hassabis (00:46:19) 我认为那当然是最深奥和最迷人的问题之一。我热爱生物学这一领域。Nick Lane 有一本很棒的书，他是这个领域的顶级专家之一，叫《生命进化的跃升》（书童注：原作英文书名为《Life Ascending: The Ten Great Inventions of Evolution》，2015年简体中文译本名为《生命的跃升:40亿年演化史上的十大发明》，2020年再版译名《生命进化的跃升:40亿年生命史上10个决定性突变》）。我认为它太棒了。它也谈到了大过滤器（Great Filter）可能是什么，是在过去还是在我们前面？我认为它们很可能在过去，如果你读了那本书，看看产生任何生命是多么不可能。然后从单细胞到多细胞似乎是一个难以置信的巨大飞跃，我认为在地球上花了十亿年才做到，对吧？所以它向你展示了这有多难。
Demis Hassabis (00:46:19) I think that’s one of course one of the deepest and most fascinating questions. I love that area of biology. There’s people, there’s a great book by Nick Lane, one of the top experts in this area called The Ten Great Inventions of Evolution. I think it’s fantastic. And it also speaks to what the great filters might be, prior or are they ahead of us? I think they’re most likely in the past, if you read that book of how unlikely to go have any life at all. And then single cell to multi-cell seems an unbelievably big jump that took a billion years, I think on earth to do, right? So it shows you how hard it was.
Lex Fridman (00:46:55) 是的，细菌在很长一段时间里都非常快乐。
Lex Fridman (00:46:55) Right? Bacteria were super happy for a very long time.
Demis Hassabis (00:46:56) 在它们以某种方式捕获线粒体之前，持续了很长时间，对吧？我不明白为什么 AI 不能以某种模拟形式帮助解决这个问题。这是一个通过组合空间的搜索过程。开始是一滩化学汤，原始汤，也许是在些热液喷口附近的土壤上。这是初始条件。（这环境）能产生看起来像细胞的东西吗？也许那将是虚拟细胞项目之后的下一个阶段（我要做的事情），嗯，像那样的东西（生命）如何从化学汤中涌现出来？
Demis Hassabis (00:46:56) For a very long time before they captured mitochondria somehow, right? I don’t see why not, why AI couldn’t help with that. Some kind of simulation. Again, it’s a bit of a search process through a combinatorial space. Here’s all the chemical soup that you start with, the primordial soup, that maybe was on earth near these hot vents. Here’s some initial conditions. Can you generate something that looks like a cell? So perhaps that would be a next stage after the virtual cell project is well, how could something like that emerge from the chemical soup?
Lex Fridman (00:47:31) 嗯，如果生命的起源有一个“第 37 手”（书童注：AlphaGo与李世石第二局比赛中，AlphaGo执黑第37手如同神来之笔）我会很高兴。我认为那是最大的谜团之一。我认为最终我们会发现（非生命到生命）是连续体：不存在非生命和生命之间的显著界限，但我们能让定义严谨化。
Lex Fridman (00:47:31) Well, I would love it if there was a Move 37 for the origin of life. I think that’s one of the great mysteries. I think ultimately what we’ll figure out is their continuum. There’s no such thing as a line between non-living and living. But if we can make that rigorous.
Demis Hassabis (00:47:44) 是的。
Demis Hassabis (00:47:44) Yes.
Lex Fridman (00:47:45) 从大爆炸到今天，一直是同一个过程。如果你能打破我们心中原有的概念，从非生命到生命起源，中间存在一堵墙，并且（证明）它不是一条分界线，而是一个连接物理学、化学和生物学的连续谱，没有显著界限。
Lex Fridman (00:47:45) That the very thing from the Big Bang to today has been the same process. If you can break down that wall that we’ve constructed in our minds of the actual origin from non-living to living, and it’s not a line that it’s a continuum that connects physics and chemistry and biology. There’s no line.
Demis Hassabis (00:48:03) 我的意思是，这就是我一生致力于 AI 和 AGI 工作的全部原因，因为我认为它可以是帮助我们回答这类问题的终极工具。我真的不明白为什么普通人不担心这些东西。我们怎么能没有一个好的生命和非生命的定义，时间的本质，更不用说意识和重力，所有这些事情以及量子力学的怪异之处？对我来说，它们一直在我面前尖叫，而且声音越来越大。就像，这里到底发生了什么？我的意思是从更深层次的意义上说，现实的本质，这必须是能回答所有这些问题的最根本的问题。如果你想一想，这有点疯狂。我们可以一直互相凝视，以及所有这些生命体。我们可以用显微镜检查它，几乎把它拆解到原子水平。然而我们仍然无法清楚地用一个简单的方式回答那个问题：如何定义生命？这有点神奇。
Demis Hassabis (00:48:03) I mean, this is my whole reason why I worked on AI and AGI my whole life, because I think it can be the ultimate tool to help us answer these kinds of questions. And I don’t really understand why the average person doesn’t worry about this stuff more. How can we not have a good definition of life and not living a non-living and the nature of time and let alone consciousness and gravity and all these things and quantum mechanics weirdness? It’s just to me, I’ve always had this sort of screaming at me in my face and it’s getting louder. It’s like, what is going on here? And I mean that in the deeper sense, the nature of reality, which has to be the ultimate question that would answer all of these things. It’s sort of crazy if you think about it. We can stare at each other and all these living things all the time. We can inspect it microscopes and take it apart almost down to the atomic level. And yet we still can’t answer that clearly in a simple way. That question of how do you define living? It’s kind of amazing.
Lex Fridman (00:49:05) 是的，生命，你可以某种程度上通过谈论来回避思考。但意识，我们显然有这种主观的、有意识的经验，就像我们感觉像是自己存在于世界中心。然后你怎么能不对这一切奇妙的事实而惊叹呢？我的意思是，人类确实与周围世界的神秘之处斗争了很长很长一段时间……这世界有很多谜团，比如太阳和雨是怎么回事？那究竟是什么？去年我们下了很多雨，今年却没有雨。我们做错了什么？人类问这个问题已经很久了。
Lex Fridman (00:49:05) Yeah, living, you can kind of talk your way out of thinking about. But consciousness, we have this very obviously subjective, conscious experience like we’re at the center of our own world and feels like something. And then how are you not screaming at the mystery of it all? I mean, but really humans have been contending with the mystery of the world around them for a long, long… There’s a lot of mysteries like what’s up with the sun and the rain? What’s that about? And then last year we had a lot of rain, and this year we don’t have rain. What did we do wrong? Humans have been asking that question for a long time.
Demis Hassabis (00:49:46) 正是。所以我猜我们已经发展了很多机制来应对这些我们无法完全理解的深层奥秘，我们只能看到，但无法完全理解，我们必须继续日常生活。我们在某种程度上让自己保持忙碌。在某种程度上，我们是否让自己分心了？（书童注：《苏菲的世界》一书中，有详细讨论成年人倾向于丧失对存在本身的敏锐和惊奇。）
Demis Hassabis (00:49:46) Exactly. So I guess we’ve developed a lot of mechanisms to cope with these deep mysteries that we can’t fully, we can see, but we can’t fully understand and we have to just get on with daily life. And we keep ourselves busy in a way. In a way, did we keep ourselves distracted?
Lex Fridman (00:50:01) 我的意思是，天气是人类历史上最重要的问题之一。现在仍然是，（所以）我们闲聊时，天气是首选方向。
Lex Fridman (00:50:01) I mean, weather is one of the most important questions of human history. We still, that’s the go-to small talk direction of the weather.
Demis Hassabis (00:50:09) 是的。尤其是在英格兰。
Demis Hassabis (00:50:09) Yes. Especially in England.
Lex Fridman (00:50:11) 然后众所周知，这是一个极其难以建模的系统。即使是那个系统，Google DeepMind 也取得了进展。
Lex Fridman (00:50:11) And then which is famously is an extremely difficult system to model. And even that system, Google DeepMind has made progress on.
Demis Hassabis (00:50:22) 是的，我们创造了世界上最好的天气预报系统，它们比传统的流体动力学系统更好，传统系统通常在大型超级计算机上计算，需要几天时间来计算。我们已经设法用神经网络系统，用我们的 WeatherNet 系统，模拟了天气动力学。再次，这很有趣，即使非常复杂，几乎在某些情况下濒临混沌系统，那些动力学过程也可以被建模。
Demis Hassabis (00:50:22) Yes, we’ve created the best weather prediction systems in the world and they’re better than traditional fluid dynamics sort of systems that usually calculated on massive supercomputers takes days to calculate it. And we’ve managed to model a lot of the weather dynamics with neural network systems, with our WeatherNet system. And again, it’s interesting that those kinds of dynamics can be modeled even though very complicated, almost bordering on chaotic systems in some cases.
(00:50:50) 其中很多有趣的方面都可以被这些神经网络系统建模，包括最近我们对气旋路径的预测，飓风可能去哪里。当然，超级有用，对世界超级重要，并且非常及时、非常快速以及准确地做到这一点非常重要。我认为这又是一个非常有前途的方向，这样你就可以对非常复杂的现实世界系统进行前向预测和模拟。
(00:50:50) A lot of the interesting aspects of that can be modeled by these neural network systems, including very recently we had cyclone prediction of where paths of hurricanes might go. Of course, super useful, super important for the world and it’s super important to do that very timely and very quickly and as well as accurately. And I think it’s very promising direction again, of simulating so that you can run forward predictions and simulations of very complicated real world systems.
Lex Fridman (00:51:18) 我应该提一下，我在德克萨斯州有机会遇到一个叫做“风暴追逐者”（Storm Chasers）的社区。他们真正令人难以置信的是，我需要和他们多谈谈，就是他们极其精通技术，因为他们必须做的是，他们使用模型来预测风暴（龙卷风）在哪里。所以这是疯狂到进入风暴眼、保护你的生命、以及预测极端事件将在哪里的美丽组合，必须拥有日益复杂的天气模型。
Lex Fridman (00:51:18) I should mention that I’ve gotten a chance in Texas to meet a community of folks called the Storm Chasers. And what’s really incredible about them, I need to talk to them more, is they’re extremely tech-savvy because what they have to do is they have to use models to predict where the storm is. So it’s this beautiful mix of crazy enough to go into the eye of the storm and in order to protect your life and predict where the extreme events are going to be, they have to have increasingly sophisticated models of weather.
Demis Hassabis (00:51:50) 是的。
Demis Hassabis (00:51:50) Yeah.
Lex Fridman (00:51:50) 这是作为生命有机体身处其中和科学前沿的美丽平衡。他们实际上可能正在使用 DeepMind 系统。
Lex Fridman (00:51:50) It is a beautiful balance of being in it as living organisms and the cutting edge of science. They actually might be using DeepMind systems.
Demis Hassabis (00:52:01) 是的。希望他们是。我很想加入他们其中的一次追逐。它们看起来太棒了。对。那太棒了，能真正体验一次。
Demis Hassabis (00:52:01) Yeah. But hopefully they are. And I’d love to join them in one of those checks. They look amazing. Right. That’s great to actually experience it one time.
通往 AGI 之路
Path to AGI
Lex Fridman (00:52:07) 正是。正确地预测某物将会到来以及它将如何演变，这太不可思议了。你估计我们到 2030 年会有 AGI，所以围绕这个有一些有趣的问题。我们将如何真正知道我们达到了目标，以及什么可能是 AGI 的“第 37 手”。
Lex Fridman (00:52:07) Exactly. And then also to experience the correct prediction where something will come and how it’s going to evolve. It’s incredible. You’ve estimated that we’ll have AGI by 2030, so there’s interesting questions around that. How will we actually know that we got there and what may be the move quote, “Move 37” of AGI.
Demis Hassabis (00:52:33) 我的估计是未来五年内，比如到 2030 年，有 50% 的机会。所以我认为有很好的机会AGI会发生。部分在于你对 AGI 的定义是什么？当然人们现在在争论这个，我的标准相当高，并且一直是可以匹配大脑所具有的认知功能。所以我们知道我们的大脑几乎近似是通用的图灵机，当然我们用我们的心智创造了不可思议的现代文明。所以那也说明了大脑的通用性有多强。
Demis Hassabis (00:52:33) My estimate is sort of 50% chance by in the next five years, so by 2030 let’s say. So I think there’s a good chance that that could happen. Part of it is what is your definition of AGI? Of course people arguing about that now and mind’s quite a high bar and always has been of can we match the cognitive functions that the brain has? So we know our brains are pretty much general Turing machines approximate, and of course we created incredible modern civilization with our minds. So that also speaks to how general the brain is.
(00:53:06) 对我们来说，要知道我们有一个真正的 AGI，我们必须确保它拥有所有这些能力。它不是一种参差不齐的智能，就如今天那些，有些东西它很擅长，但其他东西它有缺陷。这就是我们目前拥有的系统的情况。它们不一致。所以你希望智能在整个范围内具有一致性。
(00:53:06) And for us to know we have a true AGI, we would have to make sure that it has all those capabilities. It isn’t kind of a jagged intelligence where some things, it’s really good at, like today’s systems, but other things it’s really flawed at. And that’s what we currently have with today’s systems. They’re not consistent. So you’d want that consistency of intelligence across the board.
(00:53:28) 然后我们缺少一些，我认为，能力，比如真正的发明能力和我们之前谈到的创造力。所以你希望看到（AGI拥有）这些能力。你如何测试？一种方法是对数万个我们知道人类能完成的认知任务进行一种暴力测试。也许还让几百位世界顶级专家，每个学科领域的陶哲轩那样的人，可以使用这个系统，给他们一两个月的时间，看看他们是否能发现系统中有明显的缺陷。如果他们不能，那么我认为你可以相当确信我们有了一个完全通用的系统。
(00:53:28) And then we have some missing, I think, capabilities like the true invention capabilities and creativity that we were talking about earlier. So you’d want to see those. How you test that? I think you just test it. One way to do it would be kind of brute force test of tens of thousands of cognitive tasks that we know that humans can do. And maybe also make the system available to a few hundred of the world’s top experts, the Terence Taos of each subject area and give them a month or two and see if they can find an obvious flaw in the system. And if they can’t, then I think you can be pretty confident we have a fully general system.
Lex Fridman (00:54:11) 也许稍微反驳一下，似乎随着智能在所有领域的提高，人类会很快认为这是理所当然的，就像你提到的，陶哲轩，这些杰出的专家。他们可能在几周内，很快就会认为它所能做的所有令人难以置信的事情是理所当然的。我认为我自己，首先，是人类。我认同为人类。有些人听我说话，他们会说，“那家伙不擅长说话，结结巴巴的。”所以即使人类也有明显的跨领域限制，甚至就在演算、数学和物理学等等之外。我想知道是否需要像“第 37 手”这样的东西，所以在积极的一面，而不是用一万个认知任务的轰炸，在那里会有一两个让你觉得，天哪，这很特别。
Lex Fridman (00:54:11) Maybe to push back a little bit, it seems like humans are really incredible as the intelligence improves across all domains to take it for granted, like you mentioned, Terence Tao, these brilliant experts. They might quickly in a span of weeks, take for granted all the incredible things it can do and then focus in on, well, aha, right there. I consider myself, first of all, human. I identify as human. Some people listen to me talk and they’re like, “That guy is not good at talking the stuttering.” So even humans have obvious across domains limits, even just outside of calc, mathematics and physics and so on. I wonder if it will take something like a Move 37, so on the positive side versus a barrage of 10,000 cognitive tasks where it’ll be one or two where it’s like, holy shit, this is special.
Demis Hassabis (00:55:14) 所以我认为是那样。正是。我认为那种全面的测试，只是为了确保你得到一致性。但我认为还有像“第 37 手”那样的灯塔时刻，我会寻找这些像发明一个新的猜想，或一个关于物理学的新假设。就像爱因斯坦所做的那样。
Demis Hassabis (00:55:14) So I think that. Exactly. So I think there’s the sort of blanket testing to just make sure you’ve got the consistency. But I think there are the sort of lighthouse moments like the Move 37 that I would be looking for. So one would be inventing a new conjecture or a new hypothesis about physics like Einstein did.
(00:55:33) 所以你甚至可以非常严格地运行那个回溯测试，以 1900 年为止，然后给系统 1900 年之前写的所有东西，然后看看它是否能提出狭义相对论和广义相对论，对吧？就像爱因斯坦所做的那样。那将是一个有趣的测试。另一个是它能发明一个像围棋一样的游戏吗？不仅仅是提出“第 37 手”的新策略，而是能发明一个像围棋一样有深度、一样有审美美感、一样优雅的游戏吗？这些是我会留意的事情。可能一个系统能够做几件那样的事情，才能表明它是非常通用的，不仅仅是一个领域。所以我认为那至少是我会寻找的标志，表明我们有了一个达到 AGI 水平的系统，然后也许为了充实那一点，你也会检查它们的一致性，确保那个系统也没有漏洞。
(00:55:33) So maybe you could even run the back test of that very rigorously, have a cut-off of 1900 and then give the system everything that was written up to 1900 and then see if it could come up with special relativity and general relativity, right? Like Einstein did. That would be an interesting test. Another one would be can it invent a game like Go? Go not just come up with Move 37, a new strategy, but can it invent a game that’s as deep as aesthetically beautiful, as elegant as Go? And those are the sorts of things I would be looking out for. And probably a system being able to do several of those things for it to be very general, not just one domain. And so I think that would be the signs at least that I would be looking for, that we’ve got a system that’s AGI level and then maybe to fill that out, you would also check their consistency, make sure there’s no holes in that system either.
Lex Fridman (00:56:27) 是的，像一个新的猜想或科学发现之类的东西。那会是一种很酷的感觉。
Lex Fridman (00:56:27) Yeah, something like a new conjecture or scientific discovery. That would be a cool feeling.
Demis Hassabis (00:56:32) 是的，那太神奇了。所以它不仅仅是帮助我们做那件事，而是实际上提出了全新的东西。
Demis Hassabis (00:56:32) Yeah, that would be amazing. So it’s not just helping us do that, but actually coming up with something brand new.
Lex Fridman (00:56:38) 而你会在那个房间里。
Lex Fridman (00:56:38) And you would be in the room for that.
Demis Hassabis (00:56:40) 绝对。
Demis Hassabis (00:56:40) Absolutely.
Lex Fridman (00:56:40) 那可能在宣布之前的两个月或三个月。而你只会坐在那里努力不发推文。
Lex Fridman (00:56:40) It would be probably two or three months before announcing it. And you would just be sitting there trying not to Tweet.
Demis Hassabis (00:56:49) 类似那样。正是。就像这是什么惊人的新物理想法？然后我们可能会与该领域的世界专家检查并验证它，并仔细检查它的工作原理。我猜它也会解释它的工作原理。是的。那将是一个惊人的时刻。
Demis Hassabis (00:56:49) Something like that. Exactly. It’s like what is this amazing new physics idea? And then we would probably check it with world experts in that domain and validate it and go through its workings. And I guess it would be explaining its workings too. Yeah. It would be an amazing moment.
Lex Fridman (00:57:07) 你担心我们作为人类，即使是专家人类，像你可能会错过它吗？可能会错过——
Lex Fridman (00:57:07) Do you worry that we as humans, even expert humans, like you might miss it? Might miss-
Demis Hassabis (00:57:12) 嗯，它可能相当复杂。所以它可能是，我在那里做的类比是，我不认为它对最好的人类科学家来说会完全神秘，但可能有点像，例如在国际象棋中，如果我和 Garry Kasparov 或 Magnus Carlsen 交谈并和他们下一盘棋，他们走了一步妙招，我可能想不出那步棋。但他们可以事后解释为什么那步棋有道理。我们能够在某种程度上理解它，不是达到他们的水平，但如果他们善于解释——这实际上也是智力的一部分——能够以简单的方式解释你正在思考的事情，我认为那对最好的人类科学家来说将是非常可能的。
Demis Hassabis (00:57:12) Well, it may be pretty complicated. So it could be, the analogy I give there is I don’t think it will be totally mysterious to the best human scientists, but it may be a bit like, for example in chess, if I was to talk to Garry Kasparov for Magnus Carlsen and play a game with them and they make a brilliant move, I might not be able to come up with that move. But they could explain why afterwards that move made sense. And we would be to understand it to some degree, not to the level they do, but if they were good at explaining, which is actually part of intelligence too, is being able to explain in a simple way that what you’re thinking about, I think that that will be very possible for the best human scientists.
Lex Fridman (00:57:52) 但我好奇，也许你可以在围棋方面启发我，我想知道 Magnus 或 Garry 是否有一些招法，他们起初会认为那是一步坏棋而 dismiss 掉？
Lex Fridman (00:57:52) But I wonder, maybe you can educate me on the side of Go, I wonder if there’s moves from Magnus or Garry where they at first will dismiss it as a bad move?
Demis Hassabis (00:58:02) 是的，当然，有可能。但之后他们会凭直觉弄清楚为什么这步棋有效。然后凭经验，游戏的好处之一是，它是一种科学测试。你赢了游戏还是没有赢？然后那告诉你，好吧，那步棋最终是好的，那个策略是好的。然后你可以回去分析那一点，甚至向自己多解释一点为什么。探索它周围，这就是国际象棋分析之类的东西的工作方式。所以也许这就是我的大脑这样工作的原因，因为我从四岁起就一直在做那件事，并且在某种程度上是那方面的严格训练。
Demis Hassabis (00:58:02) Yeah, sure, it could be. But then afterwards they’ll figure out with their intuition why this works. And then empirically, the nice thing about games is, one of the great things about games is it’s a sort of scientific test. Do you win the game or not win? And then that tells you, okay, that move in the end was good, that strategy was good. And then you can go back and analyze that and explain even to yourself a little bit more why. Explore around it, and that’s how chess analysis and things like that works. So perhaps that’s why my brain works like that because I’ve been doing that since I was four and it’s sort of hardcore training in that way.
Lex Fridman (00:58:39) 但即使是现在，当我生成代码时，也有这种细微的、迷人的争论在发生，我可能首先认为一组生成的代码在某些有趣的细微方面是不正确的。但我总是要问这个问题，这里是否有更深刻的洞见，而我才是那个不正确的人？随着系统变得越来越智能，你将不得不应对这一点。就像，这是一个错误还是一个特性，你刚刚想出来的东西？
Lex Fridman (00:58:39) But even now when I generate code, there is this kind of nuanced, fascinating contention that’s happening where I might at first identify as a set of generated code is incorrect in some interesting nuanced ways. But then I always have to ask the question, is there a deeper insight here that I’m the one who’s incorrect? And that’s going to, as the systems get more and more intelligent, you’re going to have to contend with that. It’s like, is this a bug or a feature, what you just came up with?
Demis Hassabis (00:59:14) 是的。而且它们会相当复杂，但当然，你也可以想象 AI 系统生成那个代码或无论什么，然后人类程序员查看它，但也不是在没有 AI 工具帮助的情况下。所以这将是一种有趣的，也许是不同的 AI 工具，监控工具是生成它的那些工具。
Demis Hassabis (00:59:14) Yeah. And they’re going to be pretty complicated to do, but of course it will be, you can imagine also AI systems that are producing that code or whatever that is, and then human programmers looking at it, but also not unaided with the help of AI tools as well. So it’s going to be kind of an interesting, maybe different AI tools to the ones the monitoring tools are the ones that generated it.
Lex Fridman (00:59:36) 所以如果我们看那个 AGI 系统，抱歉又提起来，但 AlphaEvolve，它超级酷。所以 AlphaEvolve 在编程方面实现了类似递归自我改进的可能性。如果你能想象那个 AGI 系统，也许不是第一个版本，而是之后的几个版本，那实际上是什么样子的？你认为它会简单吗？你认为它会像是一个自我改进的——
Lex Fridman (00:59:36) So if we look at that AGI system, sorry to bring it back up, but AlphaEvolve, it’s super cool. So AlphaEvolve enables on the programming side, something like recursive self-improvement potentially. If you can imagine what that AGI system, maybe not the first version, but a few versions beyond that, what does that actually look like? Do you think it’ll be simple? Do you think it’ll be something like a self-improving-
Lex Fridman (01:00:00) 就像，你认为它会简单吗？你认为它会像是一个自我改进的程序，一个简单的程序吗？
Lex Fridman (01:00:00) Like, do you think it’ll be simple? Do you think it’ll be something like a self-improving program and a simple one?
Demis Hassabis (01:00:06) 我的意思是潜在那是可能的。我会说我甚至不确定那是可取的，因为那是一种硬起飞（hard takeoff） scenario。但这些当前的系统，如 Alpha Evolve，它们有人类在循环中决定各种事情，有独立的混合系统相互作用。
Demis Hassabis (01:00:06) I mean potentially that’s possible. I would say I’m not sure it’s even desirable because that’s a kind of hard takeoff scenario. But these current systems like Alpha Evolve, they have human in the loop deciding on various things, there’re separate hybrid systems that interact.
(01:00:22) 人们可以想象最终端到端地做那件事。我不明白为什么那不会可能，但现在我认为系统还不够好，无法在代码架构方面做到那一点。再次，这有点重新联系到提出一个新的猜想假设的想法，如果你给它们非常具体的关于你试图做什么的指令，它们很好，但如果你给它们一个非常模糊的高层指令，目前那行不通。我认为这与发明一个像围棋一样好的游戏的想法有关，对吧？
(01:00:22) One could imagine eventually doing that end to end. I don’t see why that wouldn’t be possible, but right now I think the systems are not good enough to do that in terms of coming up with the architecture of the code. And again, it’s a little bit reconnected to this idea of coming up with a new conjectural hypothesis, how they’re good if you give them very specific instructions about what you’re trying to do, but if you give them a very vague high level instruction, that wouldn’t work currently. And I think that’s related to this idea of invent a game as good as Go, right?
(01:00:55) 想象那就是提示词。那很…所以当前的系统我不知道如何处理那个，如何将其缩小到易于处理的东西。我认为有类似的，看，就做一个更好的你自己版本。那限制太少了。但我们做到了。如你所知，像 AlphaVol 这样的东西，比如更快的矩阵乘法，所以当你把它缩小到非常具体的事情时，它非常擅长逐步改进那件事。
(01:00:55) Imagine that was the prompt. That’s pretty. And so the current systems wouldn’t know I think what to do with that, how to narrow that down to something tractable. And I think there’s similar, look, just make a better version of yourself. That’s too unconstrained. But we’ve done it. And as you know with AlphaVol, like things like faster matrix multiplication, so when you hone it down to very specific thing you want, it’s very good at incrementally improving that.
(01:01:21) 但目前这些更多的是增量改进，小的迭代。而如果你想要理解上的巨大飞跃，你需要一个更大的进步。
(01:01:21) But at the moment these are more incremental improvements, sort of small iterations. Whereas if you wanted a big leap in understanding, you’d need a much larger advance.
Lex Fridman (01:01:34) 是的。但它也可能成为反对硬起飞 scenario 的论据。它可能只是一系列增量改进，就像矩阵乘法。它必须坐在那里思考几天如何逐步改进一个东西，它确实递归地解决了。随着你进行越来越多的改进，它会慢下来。
Lex Fridman (01:01:34) Yeah. But it could also be sort of the pushback against hard takeoff scenario. It could be just a sequence of incremental improvements, like matrix multiplication. It has to sit there for days thinking how to incrementally improve a thing and it does solve recursively. And as you do more and more improvement, it’ll slow down.
(01:01:55) 所以，通往 AGI 的道路不会是随着时间的推移逐渐改进。
(01:01:55) So there be, the path to AGI won’t be like a gradual improvement over time.
Demis Hassabis (01:02:03) 是的。如果只是增量改进，那就会是那样看起来。所以问题是，它能提出一个新的飞跃吗，比如 Transformer 架构？它能在 2017 年我们和 Brain 做的时候做到吗？不清楚这些系统，我们的 AlphaVol 之类的东西，能否做出如此大的飞跃。所以肯定这些系统是好的。我们有系统我认为可以做增量爬山（优化），这是一个关于从这里开始是否只需要这些的更大问题，或者我们是否实际上还需要一两个重大突破。
Demis Hassabis (01:02:03) Yes. If it was just incremental improvements, that’s how it would look. So the question is, could it come up with a new leap like the Transformers architecture? Could it have done that back in 2017 when we did it and Brain did it? And it’s not clear that these systems, something our AlphaVol wouldn’t be able to do, make such a big leap. So for sure these systems are good. We have systems I think that can do incremental hill climbing, and that’s a kind of bigger question about is that all that’s needed from here, or do we actually need one or two more big breakthroughs.
Lex Fridman (01:02:34) 而同样类型的系统也能提供突破吗？所以让它成为一堆 S 曲线，像增量改进，但也偶尔有飞跃。
Lex Fridman (01:02:34) And can the same kind of systems provide the breakthroughs also? So make it a bunch of S-curves like incremental improvement, but also every once in a while, leaps.
Demis Hassabis (01:02:44) 是的，我不认为有人有系统可以明确显示那些巨大的飞跃，我们有很多系统做你当前所在的 S 曲线的爬山（优化）。
Demis Hassabis (01:02:44) Yeah, I don’t think anyone has systems that can have shown, unequivocally those big leaps that we have a lot of systems that do the hill climbing of the S-curve that you’re currently on.
Lex Fridman (01:02:55) 而那“第 37 手”就是一个飞跃。
Lex Fridman (01:02:55) And that would be the move 37 is a leap.
Demis Hassabis (01:02:59) 是的，我认为那会是一个飞跃，类似那样的东西。
Demis Hassabis (01:02:59) Yeah, I think it would be a leap, something like that.
缩放定律
Scaling laws
Lex Fridman (01:03:01) 你认为缩放定律在预训练/后训练/测试时计算方面仍然强劲吗？你在那方面的另一面，预计 AI 进展会碰壁吗？
Lex Fridman (01:03:01) Do you think the scaling laws are holding strong on the pre-training/post-training test time compute? Do you on the flip side of that, anticipate AI progress hitting a wall?
Demis Hassabis (01:03:13) 我们当然觉得在缩放方面还有很大的空间。所以实际上所有步骤，预训练、后训练和推理时间。所以有三种缩放正在同时发生。再次，这取决于你能有多创新，我们为自己拥有最广泛和最深入的研究团队而自豪。我们有惊人的、不可思议的研究人员，比如提出 Transformer 的 Noam Shazir，领导 AlphaGo 项目的 Dave Silver 等等。
Demis Hassabis (01:03:13) We certainly feel there’s a lot more room just in the scaling. So actually all steps pre-training, post-training, and inference time. So there’s sort of three scalings that are happening concurrently. And again there, it’s about how innovative you can be and we pride ourselves on having the broadest and deepest research bench. We have amazing, incredible researchers and people like Noam Shazir who came up with Transformers and Dave Silver who led the AlphaGo project and so on.
(01:03:47) 那个研究基础意味着，如果需要一些新的突破，比如 AlphaGo 或 Transformer，我会支持我们成为实现那个的地方。所以实际上我相当喜欢当地形变得艰难时，对吧？因为那时它更倾向于从仅仅是工程转向真正的研究，研究加工程，那是我们的甜蜜点，我认为那更难。发明东西比快速跟进更难。
(01:03:47) And that research base means that if some new breakthrough is required, like an AlphaGo or Transformers, I would back us to be the place that does that. So I’m actually quite like it when the terrain gets harder, right? Because then it veers more from just engineering to true research, and research plus engineering, and that’s our sweet spot and I think that’s harder. It’s harder to invent things than to fast follow.
(01:04:17) 所以我们不知道，我会说是否需要新东西，或者缩放现有东西是否就足够了，是五五开。所以，以真正的实证方式，我们正在尽可能努力地推动这两方面。新的蓝天想法，也许我们一半的资源在那上面。然后最大限度地缩放当前的能力。我们仍然在 Gemini 的每个不同版本上看到一些梦幻般的进展。
(01:04:17) And so we don’t know, I would say it’s kind of 50/50 whether new things are needed or whether the scaling the existing stuff is going to be enough. And so, in true kind of empirical fashion, we are pushing both of those as hard as possible. The new blue sky, ideas and maybe about half our resources are on that. And then scaling to the max, the current capabilities. And we’re still seeing some fantastic progress on each different version of Gemini.
Lex Fridman (01:04:48) 你那样说很有趣，就深度团队而言，如果通往 AGI 的进展不仅仅是缩放计算，所以是问题的工程方面，并且更多的是科学方面，需要突破，那么你相信 DeepMind，以及 Google DeepMind，在那个领域处于有利地位，能够表现出色。
Lex Fridman (01:04:48) That’s interesting the way you put it in terms of the deep bench, that if progress towards AGI is more than just scaling compute, so the engineering side of the problem, and is more on the scientific side where there’s breakthroughs needed, then you feel confident DeepMind as well, Google DeepMind as well positioned to kick ass in that domain.
Demis Hassabis (01:05:13) 嗯，我的意思是，如果你看看过去十年或 15 年的历史，也许是，我不知道，支撑现代 AI 领域今天的突破中，80-90% 最初来自 Google Brain、Google Research 和 DeepMind。所以是的，我希望那能继续下去。
Demis Hassabis (01:05:13) Well, I mean if you look at the history of the last decade or 15 years, it’s been maybe, I don’t know, 80-90% of the breakthroughs that underpins modern AI field today was from originally Google Brain, Google Research and DeepMind. So yeah, I would back that to continue hopefully.
Lex Fridman (01:05:31) 所以在数据方面，你担心耗尽高质量数据，尤其是高质量人类数据吗？
Lex Fridman (01:05:31) So on the data side, are you concerned about running out of high quality data, especially high quality human data?
Demis Hassabis (01:05:37) 我不太担心那个。部分是因为我认为有足够的数据，并且已经证明可以让系统变得相当好。这又回到了模拟。你是否有足够的数据来制作模拟，这样你就可以创建更多的合成数据，这些数据来自正确的分布？显然那是关键。所以你需要足够的真实世界数据才能创建那种数据生成器，我认为我们目前正处于那个阶段。
Demis Hassabis (01:05:37) I’m not very worried about that. Partly because I think there’s enough data, and it’s been proven to get the systems to be pretty good. And this goes back to simulations again. Do you have enough data to make simulations, so that you can create more synthetic data that are from the right distribution? Obviously that’s the key. So you need enough real-world data in order to be able to create those kinds of data generators, and I think that we’re at that step at the moment.
Lex Fridman (01:06:05) 是的，你在科学和生物学方面做了很多不可思议的事情，用不是那么多的数据做了很多。
Lex Fridman (01:06:05) Yeah, you’ve done a lot of incredible stuff on the side of science and biology, doing a lot with not so much data.
Demis Hassabis (01:06:12) 是的。
Demis Hassabis (01:06:12) Yeah.
Lex Fridman (01:06:12) 我的意思仍然是大量数据，但我想足够——
Lex Fridman (01:06:12) I mean it’s still a lot of data, but I guess enough to-
Demis Hassabis (01:06:15) 来启动它。正是。正是。
Demis Hassabis (01:06:15) To get that going. Exactly. Exactly
计算
Compute
Lex Fridman (01:06:18) 是的。扩展计算对于构建 AGI 有多关键？这是一个工程问题。它几乎是一个地缘政治问题，因为它也融入了供应链和能源。一件你非常关心的事情，即潜在的聚变。所以在能源方面也要创新。你认为我们会继续扩展计算吗？
Lex Fridman (01:06:18) Yeah. How crucial is the scaling of compute to building AGI? That’s an engineering question. It’s almost a geopolitical question because it also integrated into that is supply chains and energy. A thing that you care a lot about, which is potentially fusion. So innovating on the side of energy also. Do you think we’re going to keep scaling compute?
Demis Hassabis (01:06:42) 我认为是的，有几个原因。我认为计算，你拥有的用于训练的计算量，通常需要集中放置，所以实际上甚至数据中心之间的带宽限制都会影响那一点。所以即使在那里也有额外的限制，那对于训练显然很重要，最大的模型你可以，但也因为现在 AI 系统在产品中并被全球数十亿人使用，你现在需要大量的推理计算。
Demis Hassabis (01:06:42) I think so, for several reasons. I think compute, there’s the amount of compute you have for training, often it needs to be co-located, so actually even bandwidth constraints between data centers can affect that. So there’s additional constraints even there and that’s important for training, obviously the largest models you can, but there’s also because now AI systems are in products and being used by billions of people around the world, you need a ton of inference compute now.
(01:07:10) 然后除此之外，还有思维系统，去年的新范式，它们在测试时给的推理时间越长，它们就越聪明。所有那些事情都需要大量的计算，我真的看不到那会放缓，并且随着 AI 系统变得更好，它们会变得更有用，对它们的需求也会更大。所以从训练方面来看，训练方面实际上只是那其中一部分。它甚至可能变成整体所需计算中较小的部分。
(01:07:10) And then on top of that there’s the thinking systems, the new paradigm of the last year that where they get smarter, the longer amount of inference time you give them at test time. So all of those things need a lot of compute and I don’t really see that slowing down, and as AI systems become better, they’ll become more useful and there’ll be more demand for them. So both from the training side, the training side actually is only just one part of that. It may even become the smaller part of what’s needed in the overall compute that’s required.
Lex Fridman (01:07:42) 是的，那是一种几乎 meme 式的东西，即 VL3 令人难以置信的方面取得了成功。人们有点取笑，它越成功，服务器就越吃力。
Lex Fridman (01:07:42) Yeah, that’s one sort of almost meme-y kind of thing, which is the success in the incredible aspects of VL3. People kind of make fun of the more successful it becomes, the servers are sweating.
Demis Hassabis (01:07:55) 是的。
Demis Hassabis (01:07:55) Yes.
Lex Fridman (01:07:55) 推理。
Lex Fridman (01:07:55) The inference.
Demis Hassabis (01:07:57) 是的，是的，正是。我们做了一个服务器煎鸡蛋之类的小视频。没错。我们将不得不弄清楚如何做到这一点。有很多有趣的硬件创新，我们有自己的 TPU 产品线，我们正在研究仅推理的东西，仅推理芯片，以及我们如何能使那些更高效。
Demis Hassabis (01:07:57) Yeah, yeah, exactly. We did a little video of the servers frying eggs and things. That’s right. And we are going to have to figure out how to do that. There’s a lot of interesting hardware innovations that we do as we have our own TPU line and we’re looking at inference-only things, inference-only chips and how we can make those more efficient.
(01:08:15) 我们也对构建 AI 系统非常感兴趣，并且我们已经做了帮助能源使用的工作，所以帮助数据中心能源，比如冷却系统高效，电网优化，然后最终是帮助等离子体约束聚变反应堆之类的事情。我们与 Commonwealth Fusion 在这方面做了很多工作，也可以想象反应堆设计。
(01:08:15) We’re also very interested in building AI systems and we have done the help with energy usage, so help data center energy like for the cooling systems be efficient, grid optimization, and then eventually things like helping with plasma-containment fusion reactors. We’ve done lots of work on that with Commonwealth Fusion, and also one could imagine reactor design.
(01:08:38) 然后材料设计我认为是最令人兴奋的之一。新型太阳能材料，太阳能电池板材料，室温超导体一直在我的梦想突破清单上，以及最优电池。我认为解决任何一件那样的事情都将对气候和能源使用产生革命性的影响。我们可能很接近了，并且再次在未来五年内，拥有能够实质性帮助解决这些问题的 AI 系统。
(01:08:38) And then material design I think is one of the most exciting. New types of solar material, solar panel material room temperature superconductors has always been on my list of dream breakthroughs, and optimal batteries. And I think a solution to any one of those things would be absolutely revolutionary for climate and energy usage. And we’re probably close, and again in the next five years to having AI systems that can materially help with those problems.
能源的未来
Future of energy
Lex Fridman (01:09:05) 如果要打赌，抱歉问这个荒谬的问题，但 20、30、40 年后能源的主要来源是什么。你认为会是核聚变吗？
Lex Fridman (01:09:05) If you were to bet, sorry for the ridiculous question, but what is the main source of energy in 20, 30, 40 years. Do you think it’s going to be nuclear fusion?
Demis Hassabis (01:09:15) 我认为聚变和太阳能是我会押注的两个。太阳能，我的意思是它当然是天空中的聚变反应堆，我认为真正的问题在于电池和传输。所以除了更高效、越来越高效的太阳能材料，也许最终在太空中，这些戴森球式的想法。
Demis Hassabis (01:09:15) I think fusion and solar are the two that I would bet on. Solar, I mean it’s the fusion reactor in the sky of course, and I think really the problem there is batteries and transmission. So as well as more efficient, more and more efficient solar material perhaps eventually in space, these kind of Dyson Sphere type ideas.
(01:09:36) 聚变我认为绝对是可行的，似乎，如果我们有正确的反应堆设计，并且我们能足够快地控制等离子体等等，我认为这两件事实际上都会得到解决。所以我们可能至少会有那些可能是可再生能源、清洁、几乎免费或也许免费能源的两个主要来源。
(01:09:36) And fusion I think is definitely doable, it seems, if we have the right design of reactor and we can control the plasma and fast enough and so on, and I think both of those things will actually get solved. So we’ll probably have at least those are probably the two primary sources of renewable, clean, almost free or perhaps free energy.
Lex Fridman (01:09:58) 多么激动人心的时代。如果我和你一起穿越到一百年后的未来，如果我们已经通过了 I 型卡尔达肖夫文明尺度（Kardashev scale），你会有多惊讶？
Lex Fridman (01:09:58) What a time to be alive. If I traveled into the future with you a hundred years from now, how much would you be surprised if we’ve passed a type one Kardashev scale civilization?
Demis Hassabis (01:10:11) 如果是从现在起一百年的时间尺度，我不会那么惊讶。我的意思是，如果我们以我们刚刚讨论的方式之一破解能源问题，或者非常高效的太阳能，那么如果能源是免费、可再生和清洁的，那就解决了一大堆其他问题。
Demis Hassabis (01:10:11) I would not be that surprised if it was a hundred-year timescale from here. I mean I think it’s pretty clear if we crack the energy problems in one of the ways we’ve just discussed or very efficient solar, then if energy is kind of free and renewable and clean, then that solves a whole bunch of other problems.
(01:10:32) 例如，水资源获取问题就消失了，因为你可以直接使用海水淡化。我们有技术，只是太昂贵了。所以只有相当富裕的国家，如新加坡和以色列等，实际上使用它。但如果它便宜，那么所有有海岸的国家都可以，而且你会有无限的火箭燃料。你可以只用能源将海水分离成氢和氧，那就是火箭燃料。
(01:10:32) So for example, the water access problem goes away because you can just use desalination. We have the technology, it’s just too expensive. So only fairly wealthy countries like Singapore and Israel and so on actually use it. But if it was cheap, then all countries that have a coast could, but also you’d have unlimited rocket fuel. You could just separate seawater out into hydrogen and oxygen using energy and that’s rocket fuel.
(01:10:57) 所以结合 Elon 的惊人的自行着陆火箭，那么可能就像太空巴士服务一样。所以那开启了不可思议的新资源和领域。小行星采矿我认为会成为一件事，以及最大程度的人类繁荣走向星际。那也是我梦想的事情，就像卡尔·萨根（Carl Sagan）的那种将意识带入宇宙、唤醒宇宙的想法。我认为如果我们把 AI 做对了，并用它解决了一些这些问题，人类文明将在充分的意义上做到那一点。
(01:10:57) So combined with Elon’s, amazing self landing rockets, then it could be you sort of like a bus service to space. So that opens up incredible new resources and domains. Asteroid mining I think will become a thing, and maximum human flourishing to the stars. That’s what I dream about as well is like Carl Sagan’s sort of idea of bringing consciousness to the universe, waking up the universe. And I think human civilization will do that in the full sense of time if we get AI right, and crack some of these problems with it.
Lex Fridman (01:11:30) 是的，我想知道如果你只是一个在太空中飞行的游客，那会是什么样子。你可能会注意到地球，因为如果你解决了能源问题，你可能会看到很多太空火箭。所以这里会有交通拥堵，但是在太空中。
Lex Fridman (01:11:30) Yeah, I wonder what it would look like if you’re just a tourist flying through space. You would probably notice earth because if you solve the energy problem, you would see a lot of space rockets probably. So it would be traffic here in London, but in space.
Demis Hassabis (01:11:46) 是的，正是。
Demis Hassabis (01:11:46) Yes, exactly.
Lex Fridman (01:11:46) 就是很多火箭。然后你可能会看到漂浮在太空中的某种能源，比如太阳能。所以地球表面看起来会更具技术性。然后你会利用那种能源的力量来保护自然……
Lex Fridman (01:11:46) It’s just a lot of rockets. And then you would probably see floating in space, some kind of source of energy like solar potentially. So earth would just look more on the surface, more technological. And then you would use the power of that energy then to preserve the natural…
Demis Hassabis (01:12:05) 是的。
Demis Hassabis (01:12:05) Yes.
Lex Fridman (01:12:06) 像雨林和所有那种东西。
Lex Fridman (01:12:06) Like the rainforest and all that kind of stuff.
Demis Hassabis (01:12:07) 正是。因为人类历史上第一次我们将不会受到资源限制。我认为那对人类来说可能是一个惊人的新时代，那里不是零和，对吧？我拥有这块土地，你就没有。或者如果老虎拥有它们的森林，那么当地的村庄就不能，它们要用什么？我认为这将有很大帮助。不，它不会解决所有问题，因为仍然存在其他人类弱点，但至少它将消除一个，我认为一个大载体，即资源的稀缺性，包括土地和更多材料和能源。
Demis Hassabis (01:12:07) Exactly. Because for the first time in human history we wouldn’t be resource constrained. And I think that could be amazing new era for humanity where it’s not zero-sum, right? I have this land, you don’t have it. Or if the tigers have their forest, then the local villages can’t, what are they going to use? I think that this will help a lot. No, it won’t solve all problems because there’s still other human foibles that will still exist, but it will at least remove one, I think one of the big vectors, which is scarcity of resources, including land and more materials and energy.
(01:12:45) 我们有时称之为另一个关于这种激进充裕时代的呼吁，那里有充足的资源可供分配。当然下一个大问题是确保那是公平的，共享的，社会中的每个人都从中受益。
(01:12:45) And we should be sometimes call it another call about this kind of radical abundance era, where there’s plenty of resources to go around. Of course the next big question is making sure that that’s fairly, shared fairly and everyone in society benefits from that.
人性
Human nature
Lex Fridman (01:13:01) 所以人性中有某种东西，我去了，就像波拉特（Borat），像我的邻居。你制造麻烦。我们确实引发冲突，这就是为什么游戏贯穿始终，正如我实际上越来越多地了解到的，即使在古代历史中，也服务于将人们推离战争，实际热战的目的。所以也许我们可以设计出越来越复杂的电子游戏，它们吸引我们，给我们那种……挠冲突的痒处，无论那是什么，但我们，人性。
Lex Fridman (01:13:01) So there is something about human nature where I go, its like Borat, like my neighbor. You start trouble. We do start conflicts and that’s why games throughout, as I’m learning actually more and more, even in ancient history, serve the purpose of pushing people away from war, actually hot war. So maybe we can figure out increasingly sophisticated video games that pull us, they give us that… Scratch the itch of conflict, whatever that is, but us, the human nature.
Demis Hassabis (01:13:38) 像……是的。
Demis Hassabis (01:13:38) Like… Yeah.
Lex Fridman (01:13:38) 然后避免实际的热战，那会随着日益复杂的技术而来，因为我们现在，早已过了我们能够创造的武器实际上可以摧毁整个人类文明的阶段。所以那不再是与你邻居开始的好方法。最好下一盘国际象棋。
Lex Fridman (01:13:38) And then avoid the actual hot wars that would come with increasingly sophisticated technologies because we’re now, we’ve long passed the stage where the weapons we’re able to create can actually just destroy all of human civilization. So that’s no longer a great way to start with your neighbor. It’s better to play a game of chess.
Demis Hassabis (01:14:03) 或者足球。
Demis Hassabis (01:14:03) Or football.
Lex Fridman (01:14:03) 或者足球。是的。
Lex Fridman (01:14:03) Or football. Yeah.
Demis Hassabis (01:14:05) 我认为那就是现代体育的意义，我喜欢看足球，我只是觉得，我也经常踢，它在部落性方面非常 visceral，我认为它确实将很多那些能量引导到我认为是人类需要属于某个群体的那种方式，但以一种有趣的方式，一种健康的方式，而不是破坏性的方式，一种建设性的事情。
Demis Hassabis (01:14:05) And I think that’s what my modern sport is and I love football watching it and I just feel like, and I used to play it a lot as well, and it’s very visceral in its tribal, and I think it does channel a lot of those energies into which I think is a kind of human need to belong to some group, but into a fun way, a healthy way and not destructive way, kind of constructive thing.
(01:14:33) 我认为回到游戏 again 是，我认为它们最初对孩子们玩像国际象棋这样的东西也很棒，因为它们是世界的伟大缩影模拟。它们也是世界的模拟。它们是现实世界情境的简化版本，无论是扑克、围棋还是国际象棋，不同方面或外交，现实世界的不同方面。
(01:14:33) And I think going back to games again is I think they’re originally why they’re so great as well for kids to play things like chess is they’re great little microcosm simulations of the world. They’re simulations of the world too. They’re simplified versions of some real world situation, whether it’s poker or Go or chess, different aspects or diplomacy, different of the real world.
(01:14:53) 它允许你也在它们身上练习，因为你一生中有多少次能练习一个重大的决定时刻？接受什么工作，上什么大学？你也许能得到，我不知道，十几个左右的关键决定必须做，你必须尽你所能做出那些决定。而游戏是一种安全的环境，可重复的环境，你可以在那里提高你的决策过程，也许这有额外的好处，将一些能量引导到更具创造性和建设性的追求中。
(01:14:53) And it allows you to practice at them too, because how many times do you get to practice a massive decision moment in your life? What job to take, what university to go to? You get maybe, I don’t know, a dozen or so key decisions one has to make and you’ve got to make those as best as you can. And games is a kind of safe environment, repeatable environment where you can get better at your decision-making process, and it maybe has this additional benefit of channeling some energies into more creative and constructive pursuits.
Lex Fridman (01:15:24) 嗯，我认为练习输和赢也非常重要。
Lex Fridman (01:15:24) Well I think it’s also really important to practice losing and winning.
Demis Hassabis (01:15:28) 对。
Demis Hassabis (01:15:28) Right.
Lex Fridman (01:15:29) 输是一种非常，这就是我热爱游戏的原因。这就是我甚至热爱像巴西柔术这样的事情的原因，在那里你可以在安全的环境中一次又一次地被击败。它提醒你物理学，关于世界运作的方式，关于有时你输，有时你赢，你仍然可以和每个人做朋友。但那种输的感觉，我的意思是对我们人类来说，真正理解它很奇怪。那只是生活的基本部分。生活的一个基本部分就是输。
Lex Fridman (01:15:29) Losing is a really, that’s why I love games. That’s why I love even things like Brazilian Jiu-Jitsu where you can get your kicked in a safe environment over and over. It reminds you about physics, about the way the world works about sometimes you lose, sometimes you win, you can still be friends with everybody. But that feeling of losing, I mean it’s a weird one for us humans to really make sense of. That’s just part of life. That is a fundamental part of life is losing.
Demis Hassabis (01:16:00) 我认为据我理解，武术，还有像下棋这样的事情，至少我理解的方式，很大程度上与自我改进、自我认识有关。那，好吧，所以我做了这件事。这不真的是关于打败另一个人，而是关于最大化你自己的潜力。
Demis Hassabis (01:16:00) And I think the martial arts as I understand it, but also in things like light chess is at least the way I took it’s a lot to do with self-improvement, self-knowledge. That, okay, so I did this thing. It’s not about really beating the other person, it’s about maximizing your own potential.
(01:16:16) 如果你以健康的方式去做，你学会以某种方式利用胜利和失败。不要被胜利冲昏头脑，认为你就是世界上最好的。而失败让你保持谦卑，总是知道总有更多东西要学。总有一个更大的专家可以指导你。我相信你在武术中也学到了这一点。
(01:16:16) If you do it in a healthy way, you learn to use victory and losses in a way. Don’t get carried away with victory and think you’re just the best in the world. And the losses keep you humble, and always knowing there’s always something more to learn. There’s always a bigger expert that you can mentor you. I think you learn that I’m pretty sure in martial arts.
(01:16:35) 我认为那至少也是我在国际象棋中受训的方式。所以，同样地，它可能非常硬核和非常重要，当然你想赢，但你也需要学习如何以健康的方式应对挫折，并将你输掉某样东西时的感觉转化为建设性的事情，下次我要改进这个或在这方面变得更好。
(01:16:35) And I think that’s also the way that at least I was trained in chess. And so, in the same way, and it can be very hardcore and very important and of course you want to win, but you also need to learn how to deal with setbacks in a healthy way, and wire that feeling that you have when you lose something into a constructive thing of, next time I’m going to improve this or get better at this.
Lex Fridman (01:16:57) 有些东西是幸福的源泉，是意义的源泉，那些改进……它与输赢无关。
Lex Fridman (01:16:57) There is something that’s a source of happiness, a source of meaning that improvements that… It’s not about the winning or losing.
Demis Hassabis (01:17:04) 是的，精通。在某种程度上，没有什么比那更令人满意的了。就像，哦，哇，这个我以前不会做的事情。现在我会了。再次，游戏和体育运动、智力运动，它们的衡量方式很美妙，因为你可以衡量那种进步。
Demis Hassabis (01:17:04) Yes, the mastery. There’s nothing more satisfying in a way. It’s like, oh wow, this thing I couldn’t do before. Now I can. And again, games and physical sports and mental sports, their ways of measuring their beautiful, because you can measure that progress.
Lex Fridman (01:17:19) 是的，我想这就是为什么我喜欢角色扮演游戏，比如技能树上的数字上升，字面上那是我们人类意义的源泉，无论我们的——
Lex Fridman (01:17:19) Yeah, there’s something about I guess why I love role-playing games, like the number go up of on the skill tree, literally that is a source of meaning for us humans, whatever our-
Demis Hassabis (01:17:29) 是的，我们相当沉迷于这种数字上升。也许这就是我们制作那种游戏的原因，因为显然我们本身就是爬山（优化）系统，对吧？
Demis Hassabis (01:17:29) Yeah, we’re quite addicted to this sort of, these numbers going up. And maybe that’s why we made games like that because obviously that is something we’re hill climbing systems ourselves, right?
Lex Fridman (01:17:42) 是的。如果我们没有任何机制，那将相当可悲——
Lex Fridman (01:17:42) Yeah. It would be quite sad if we didn’t have any mechanism-
Demis Hassabis (01:17:45) 色带，我们到处都这样做，我们只是有那种……
Demis Hassabis (01:17:45) Color belts, we do this everywhere, where we just have thing that…
Lex Fridman (01:17:51) 我不想贬低那个。在人类中，那是深刻意义的源泉。
Lex Fridman (01:17:51) And I don’t want to dismiss that. There is a source of deep meaning across humans.