The future of intelligence | Demis Hassabis (Co-fo

章节 1：引言与计算的边界

📝 本节摘要：

本章作为访谈的开篇，首先通过一段引人深思的预告（Teaser）奠定了对话的基调：Demis Hassabis 提出了“50% 扩展（Scaling）+ 50% 创新”的 AGI 技术路径，并探讨了将 AGI 作为人类思维模拟器的哲学意义，以及图灵机（Turing Machine）的计算极限。随后，主持人 Hannah Fry 正式开启访谈，回顾了过去一年 AI 领域的剧变——从大语言模型向智能体 AI（Agentic AI）的重心转移。Demis 感叹过去一年的进展密度堪比十年，特别提到了 Gemini 3 的发布以及他对“世界模型”（World Models）的兴奋。

[原文] [Demis Hassabis]: Effectively, you can think of as 50% of our effort is on scaling, 50% of it is on innovation. My betting is you're going to need both to get to AGI. I've always felt this, that if we build AGI, and then use that as a simulation of the mind, and then compare that to the real mind, we will then see what the differences are and potentially what's special and remaining about the human mind. Maybe that's creativity. Maybe it's emotions. Maybe it's dreaming, consciousness. There's a lot of hypotheses out there about what may or may not be computable. And this comes back to the Turing machine question of, what is the limit of a Turing machine?

[译文] [Demis Hassabis]: 实际上，你可以认为我们 50% 的精力放在扩展（Scaling）上，50% 的精力放在创新上。我敢打赌，要实现 AGI（通用人工智能），这两者缺一不可。我一直有这样的感觉：如果我们构建出 AGI，然后将其用作思维的模拟，再将其与真实的人类思维进行对比，我们将看到两者之间的差异，并可能发现人类思维中保留下来的、独特的特质是什么。也许是创造力。也许是情感。也许是做梦，或者是意识。关于哪些东西是可计算的、哪些不是，存在很多假说。这又回到了图灵机（Turing Machine）的问题：图灵机的极限究竟在哪里？

[原文] [Hannah Fry]: So there's nothing that cannot be done within these computational--

[译文] [Hannah Fry]: 这么说，没有什么是在这些计算——

[原文] [Demis Hassabis]: Well, put it this way. Nobody's found anything in the universe that's non-computable, so far.

[译文] [Demis Hassabis]: 嗯，这么说吧。到目前为止，还没有人在宇宙中发现任何不可计算的东西。

[原文] [Hannah Fry]: So far. [THEME MUSIC] Welcome to "Google DeepMind: The Podcast" with me, Professor Hannah Fry. It has been an extraordinary year for AI. We have seen the center of gravity shift from large language models to agentic AI. We've seen AI accelerate drug discovery and multimodal models integrated into robotics and driverless cars. Now, these are all topics that we've explored in detail on this podcast. But for the final episode of this year, we wanted to take a broader view, something beyond the headlines and product launches, to consider a much bigger question. Where is all this heading, really? What are the scientific and technological questions that will define the next phase? And someone who spends quite a lot of their time thinking about that is Demis Hassabis, CEO and co-founder of Google DeepMind. Welcome back to the podcast, Demis.

[译文] [Hannah Fry]: 到目前为止。[主题音乐] 欢迎收听《Google DeepMind：播客》，我是汉娜·弗莱（Hannah Fry）教授。对于 AI 来说，这是非凡的一年。我们目睹了重心从大语言模型（Large Language Models）向智能体 AI（Agentic AI）转移。我们看到了 AI 加速药物研发，以及多模态模型被整合进机器人和无人驾驶汽车中。这些都是我们在本播客中详细探讨过的主题。但在今年的最后一期节目中，我们想把视野放得更宽一些，超越新闻头条和产品发布，去思考一个更宏大的问题。这一切究竟将走向何方？定义下一阶段的科学和技术问题会是什么？有一位花了很多时间思考这些问题的人，他就是 Demis Hassabis，Google DeepMind 的首席执行官兼联合创始人。欢迎回到播客，Demis。

[原文] [Demis Hassabis]: Great to be back.

[译文] [Demis Hassabis]: 很高兴回来。

[原文] [Hannah Fry]: I mean, quite a lot has happened in the last year.

[译文] [Hannah Fry]: 我是说，过去一年里确实发生了很多事。

[原文] [Demis Hassabis]: It has.

[译文] [Demis Hassabis]: 确实如此。

[原文] [Hannah Fry]: What is the biggest shift, do you think?

[译文] [Hannah Fry]: 你认为最大的转变是什么？

[原文] [Demis Hassabis]: Oh, wow. I mean, it's just so much has happened, as you said. It feels like we packed in 10 years in one year. I think a lot's happened. I mean, certainly, for us, the progress of the models-- we've just released Gemini 3, which we're really happy with-- the multi-modal capabilities, all of those things have just advanced really well. And then probably the thing, I guess, over the summer that I'm very excited about is world models being advanced. I'm sure we're going to talk about that.

[译文] [Demis Hassabis]: 噢，哇。我是说，正如你所说，发生的事情太多了。感觉我们在一年里塞进了十年的进展。我想发生了很多事。对我来说，当然是我们模型的进步——我们刚刚发布了 Gemini 3，我们要对它非常满意——多模态能力，所有这些方面都进展得非常好。然后我想，整个夏天最让我兴奋的事情大概就是世界模型（World Models）的进步了。我相信我们一会儿会谈到这个。

[原文] [Hannah Fry]: Yeah, absolutely. We will get on to all of that stuff in a little bit more detail in a moment.

[译文] [Hannah Fry]: 是的，当然。我们稍后会详细讨论所有这些内容。