以下是对内容的中文翻译： 这期 Lightcone 播客深入探讨了生成式人工智能的迷人世界，重点关注视频生成领域的最新进展，特别是 OpenAI 的 Sora。主持人与 Dan 一起，通过分析使用文本提示创建的片段来探索 Sora 的能力。其中一个片段描绘了一个人形机器人在 2050 年的郊区街道上帮助一只金毛寻回犬。他们对高清的画质以及机器狗和金毛犬动作中相对准确的物理效果印象深刻。与之前的图像生成模型不同，Sora 能够准确地在视频中渲染文本。尽管并非完美，存在一些视觉上的不一致，比如漂浮的物体和设计奇怪的街道，但整个一分钟视频的长期视觉一致性是相对于旧技术的重大改进。 另一个片段展示了一个无人机摄像头环绕着金门大桥。在准确识别桥梁的同时，该片段也揭示了地理精度上的不足以及桥梁结构中的某些视觉故障。尽管存在这些细微的缺陷，主持人仍然认为 Sora 是视频生成领域的重大突破，尤其是与早期导致不连贯和不一致结果的尝试相比。 Dan 简要概述了 Sora 的架构，解释了它将传统用于文本的 Transformer 模型和用于图像生成的扩散模型相结合。还集成了一个时间组件来保持帧的一致性。OpenAI 使用带有“时空补丁”的视频训练 Sora，这些“时空补丁”是跨越空间和时间的三维像素矩阵，大小各异。这结合了机器人技术论文、Transformer 以及文本。虽然 OpenAI 对其内部运作方式讳莫如深，但据推测该模型的成本是 GPT4 的十倍。 随后，讨论转向 Y Combinator (YC) 批次中的公司如何以远少于 OpenAI 的资源构建令人印象深刻的基础模型。关键组成部分是数据、计算和专业知识，而 YC 公司正在“破解”这些领域的每一个。主持人强调了几家 YC 公司来说明这一点。 Infiniti AI 通过在相对较小的数据集上训练模型，来创建特定个人的深度伪造视频。提到的一个例子是仅使用几个小时的 Lightcone 播客视频训练的模型。Cinclap 是另一家创建实时唇语同步 API 的公司。他们的模型使用单个 A100 GPU 进行训练，使用低分辨率视频来显著压缩数据需求。通过与 Azure 的合作，提供给 YC 公司的专用 GPU 集群能够实现更快的迭代。 由 21 岁刚毕业的大学生创立的 Sanato，开发了一个被认为是世界上最好的文本到歌曲模型。他们允许用户输入歌词并选择特定的艺术家来演唱歌曲。主持人强调，这一壮举是通过自学和随时可用的在线资源实现的。 Metalware 由前硬件工程师开发，正在为硬件设计创建副驾驶。他们能够使用从教科书扫描的图形和信息来训练基础模型，从而能够使用计算强度较低的 GPT 2.5 模型。 GuyLab 正在构建一个可解释的基础模型，通过使模型的输出更加透明，来解决深度学习的“黑盒子”性质。 对话随后转变为何时更适合构建自定义模型与微调现有模型的问题。这个决定取决于任务。特定和基于细分领域的应用可以从自定义模型中受益，而更通用的语言任务则非常适合 GPT-4。专业知识可能被高估了，个人可以通过专注学习相对快速地获得尖端知识。可以通过 YC 获得计算资源，使得数据，特别是高质量、较小的数据集，成为主要的区别因素。 主持人谈到了越来越多地使用合成数据来训练模型。他们解释说，合成数据最初面临怀疑，但已被证明是一种生成高质量训练数据的有效方法，尤其是在真实世界数据稀缺或难以获取的情况下。他们还探讨了人工智能模拟现实世界物理的更广泛影响，将其扩展到娱乐之外的领域，如天气预测 (Atmo)、药物发现 (Diffuse Bio) 和大脑功能分析 (Pure Middle)。 本期节目最后介绍了正在构建消费级人形机器人的 K-scale labs，以及 Playground 公司的简要概述，该公司正在创建可以与 Midjourney 和 Stability AI 相媲美的图像模型，尽管他们的资金明显较少。主持人也对正在构建 CAD 设计人工智能模型的 Draft Date 感到兴奋。他们强调，通过专注于特定垂直领域并训练专门的模型，公司有很多机会与更大的参与者竞争。

The Lightcone podcast episode dives into the fascinating world of generative AI, focusing on recent advancements in video generation, specifically OpenAI's Sora. The hosts, along with Dan, explore the capabilities of Sora by analyzing clips created using text prompts. One clip depicts a humanoid robot assisting a golden retriever on a suburban street in 2050. They are impressed by the high definition and the relatively accurate physics displayed in the robot's and dog's movements. Unlike previous image generation models, Sora demonstrates the ability to render text accurately within the video. Though not perfect, with some visual inconsistencies like a floating object and an oddly designed street, the long-term visual consistency throughout the minute-long video is a significant improvement over older technologies. Another clip showcases a drone camera circling the Golden Gate Bridge. While recognizing the bridge accurately, the clip reveals imperfections in geographic accuracy and certain visual glitches in the bridge's structure. Despite these minor flaws, the hosts acknowledge Sora as a significant breakthrough in video generation, especially when compared to earlier attempts that resulted in disjointed and inconsistent results. Dan provides a brief overview of Sora's architecture, explaining its combination of a transformer model, traditionally used for text, and a diffusion model, employed for image generation. A temporal component is integrated to maintain frame consistency. OpenAI trained Sora using videos with "space-time patches," three-dimensional matrices of pixels spanning space and time, in varying sizes. This is a combination of robotics papers, plus transformer plus text. While OpenAI is cagey about the inner workings, it is speculated that the model costs ten times as much as GPT4. The discussion then shifts to how companies within the Y Combinator (YC) batch are building impressive foundational models with significantly fewer resources than OpenAI. The key components are data, compute, and expertise, and the YC companies are "hacking" each of these areas. The hosts highlight several YC companies to illustrate this point. Infiniti AI creates deepfake videos of specific individuals by training models on relatively small datasets. One example mentioned is a model trained on just a few hours of Lightcone podcast videos. Cinclap is another company that creates real-time lip-syncing APIs. Their models are trained on a single A100 GPU, using low-resolution video to significantly compress the data requirements. The dedicated GPU cluster offered to YC companies through a partnership with Azure enables much faster iteration. Sanato, founded by 21-year-old recent college grads, has developed a text-to-song model that is considered among the best in the world. They allow users to input lyrics and choose a specific artist to perform the song. The hosts emphasize that this feat was achieved through self-teaching and readily available online resources. Metalware, developed by former hardware engineers, is creating a co-pilot for hardware design. They were able to train a foundation model using scanned figures and information from textbooks, enabling them to use the less computationally intensive GPT 2.5 model. GuyLab is building an explainable foundation model, addressing the "black box" nature of deep learning by making the model's outputs more transparent. The conversation then transitions to the question of when it's more appropriate to build a custom model versus fine-tuning an existing one. The decision depends on the task. Specific and niche-based applications can benefit from custom models, while more general language tasks are well-suited to GPT-4. Expertise is perhaps overrated, with individuals able to acquire cutting-edge knowledge relatively quickly through dedicated study. Access to compute resources can be obtained through YC, making data, particularly high-quality, smaller datasets, the primary differentiator. The hosts touch upon the increasing use of synthetic data for training models. They explain that synthetic data initially faced skepticism, but it has proven to be an effective method for generating high-quality training data, particularly in scenarios where real-world data is scarce or difficult to obtain. They also explore the broader implications of AI's ability to simulate real-world physics, extending beyond entertainment into areas such as weather prediction (Atmo), drug discovery (Diffuse Bio), and brain function analysis (Pure Middle). The episode concludes with a look at K-scale labs which is building consumer humanoid robots, and a brief overview of Playground, a company creating image models that rival those from Midjourney and Stability AI, despite having significantly less funding. The hosts are also excited about Draft Date, building AI models for CAD design. They emphasize that there are many opportunities for companies to compete with larger players by focusing on specific verticals and training specialized models.