这段内容是 Lex Fridman 和 Demis Hassabis 之间的一段对话。 Hassabis 深入探讨了他的诺贝尔奖讲座的核心，提出自然界中发现的任何模式都可以被经典学习算法有效地发现和建模。这源于他在 AlphaGo 和 AlphaFold 中的工作，这些模型被构建来导航复杂、高维的空间。蛋白质折叠的成功，证明自然界能够高效地解决这个问题，表明自然系统具有受进化过程塑造的结构，使其可以被学习。Hassabis 暗示，任何经过进化的东西都可以被有效地建模。他扩展了这个概念，认为即使是地质构造、行星轨道和陨石形状也经历了生存过程，留下了可以被“反向学习”并有效预测的模式。 Hassabis 考虑创建一个名为 LNS 的复杂度类，代表可学习的自然系统，即可以被经典系统有效建模的系统。他推测是否存在一类新的问题，可以通过神经网络过程来解决，并映射到这些自然系统上。Hassabis 认为信息是最主要的，是宇宙的基本单位，并将宇宙视为一个信息系统，这使得 P=NP 问题成为一个物理问题。 Hassabis 认为，许多问题，比如 AlphaGo 和 AlphaFold，都可以被构建成这样一种方式：通过对系统动力学和属性进行建模，可以使用经典系统在多项式时间内有效地搜索解决方案。他认为经典系统可以比以前认为的走得更远，甚至可以建模蛋白质并达到世界冠军水平的围棋能力。他还讨论了细胞自动机和混沌系统，并认为物理学存在一个潜在的结构。 Lex 询问了 AlphaFold 的交互方面，即如何将基因映射到功能，从而产生可以被有效建模的内核。他强调了梯度的作用。 他们讨论了 DeepMind 的视频生成模型 Vio，该模型可以令人惊讶地模拟液体、镜面反射和材料。他指出，人工智能系统正在通过 YouTube 视频逆向工程物理学，提取底层结构。 对话转移到电子游戏。Hassabis 表达了他对游戏的热爱，以及 AI 在创造令人惊叹的个性化体验方面的潜力。他想制作开放世界游戏。Hassabis 提到了《Black and White》，其中包含早期的强化学习系统。他正在考虑从人工智能领域休假，去制作一款电子游戏。 他们进一步讨论了 Alpha Evolved，这是 Google DeepMind 用于进化算法的系统，也是未来超级智能系统的一个可能组成部分。他相信大型语言模型 (LLM) 和其他计算技术的结合。 谈到人工智能科学家，他们辩论了研究品味，即人工智能系统是否能够做出判断来引导人类科学家并产生新颖的想法。Hassabis 承认这是最难模仿的能力之一。Hassabis 讨论了模拟细胞。 他们思考人工智能是否可以模拟生命的起源，即从非生物到生物的过渡。他相信人工智能可以帮助确定事物是如何从原始汤中出现的。 然后他们讨论了 AGI (通用人工智能)。Hassabis 估计到 2030 年，AGI 的概率为 50%。他通过匹配认知功能来定义 AGI。 Hassabis 讨论了领导谷歌从Gemini系统失败到成功的经验，包括团队、文化以及削减官僚作风以加快进展。 他们谈到了规模定律、数据方面（高质量数据）以及为构建模型而进行的计算规模扩展。如果聚变成为 2030 年至 2040 年的主要能源，会对地球产生什么影响。 他们讨论了人类文明是否会走向自我毁灭。最后，讨论了什么让他对人类文明的未来充满希望。

This is a conversation between Lex Fridman and Demis Hassabis. Hassabis delves into the core of his Nobel Prize lecture, proposing that any pattern found in nature can be efficiently discovered and modeled by classical learning algorithms. This stems from his work with AlphaGo and AlphaFold, where models were built to navigate complex, high-dimensional spaces. The success in protein folding, where nature efficiently solves the problem, suggests that natural systems have structure shaped by evolutionary processes, making them learnable. Hassabis hints that anything evolved can be efficiently modeled. He expands on this concept, suggesting that even geological formations, planetary orbits, and asteroid shapes have undergone survival processes, leaving behind patterns that can be "reverse learned" and predicted efficiently. Hassabis contemplates creating a complexity class, LNS, for learnable natural systems, those that can be efficiently modeled by classical systems. He speculates if a new class of problems exists that is solvable by neural network processes, mapped onto these natural systems. Hassabis believes information is primary, the fundamental unit of the universe, and views the universe as an informational system, making the P=NP question a question of physics. Hassabis believes that many problems, like AlphaGo and AlphaFold, can be framed in a way that modeling the dynamics and properties of the system allows for efficient, polynomial-time search for solutions using classical systems. He believes classical systems can go much further than previously thought, even to modeling proteins and playing Go at world champion level. He also discusses cellular automata and chaotic systems. He suggests that there is an underlying structure to physics. Lex asks about the interaction side of AlphaFold, how genes can be mapped to a function, leading to the kernel that can be efficiently modeled. He emphasizes the role of gradients. They discuss DeepMind's video generation model, Vio, which can model liquids surprisingly well, specular lighting, and materials. He notes that AI systems are reverse-engineering physics from YouTube videos, extracting underlying structure. The conversation transitions to video games. Hassabis expresses his love for gaming and the potential of AI to create mind-blowing, personalized experiences. He wants to build open-world games. Hassabis points to Black and White that had early reinforcement learning systems. He considers a sabbatical from AI to build a video game. They further discuss Alpha evolved which is the Google DeepMind system that evolves algorithms and is a possible component for future superintelligent systems. He believes in the combination of LLMs and other computational techniques. Turning to AI scientists, they debate research taste, whether AI systems can have the judgment to steer human scientists and generate novel ideas. Hassabis acknowledges that this is one of the most difficult capabilities to mimic. Hassabis discusses simulating a cell. They contemplate whether AI can model the origin of life, the transition from non-living to living organisms. He believes AI can help determine how something emerged from the primordial soup. They then discuss AGI. Hassabis estimates a 50% chance of AGI by 2030. He defines AGI by matching cognitive function. Hassabis discusses what it took to lead google from losing to winning with the Gemini system, including, the team, culture, and cutting away bureaucracy to shipping progress. They spoke of scaling Laws, the data side ( high quality data), Scaling to compute for building. If fusion is the main source of energy in 2030 to 2040, how it would impact earth. They spoke of if there's gonna be a point humans civilization destroys itself. Finally. what gives you hope for the future of human civilization.