cover of episode Beyond Uncanny Valley: Breaking Down Sora

Beyond Uncanny Valley: Breaking Down Sora

2024/2/24
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Anjney Midha
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Stefano Ermon
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Anjney Midha:Sora模型的出现速度之快和视频质量之高令人震惊,远超预期。这标志着AI视频生成技术取得了重大突破,但仍处于早期阶段,未来有很大的提升空间。 Anjney Midha还探讨了Sora模型的训练成本以及未来AI生成视频的普及程度。他认为,虽然目前训练成本很高,主要由大型企业承担,但随着技术的进步,成本可能会降低,推理成本也会随着模型压缩技术的进步而降低。 Anjney Midha还关注了高质量视频数据获取和标注的挑战,并探讨了初创公司和大型公司在获取和使用视频数据方面可能采取的不同策略。他认为,可以使用人工参与的流程来标注视频数据,并结合人工标注和自动标注方法来提高效率。 Anjney Midha还探讨了更大的上下文窗口对视频模型灵活性的影响,以及各种技术(如基于注意力的方法、嵌入式方法、环形注意力、快速注意力和状态空间模型)如何用于扩展视频模型的上下文窗口大小。 Stefano Ermon:Sora模型的成功是扩散模型和Transformer架构结合的成果。扩散模型比GANs更稳定,更容易训练,并且可以在推理时利用更深层次的计算图,而无需在训练时付出高昂的代价。 Stefano Ermon详细解释了视频生成比文本或图像生成更复杂的原因,包括计算成本更高、高质量的公开可用视频数据集有限以及视频内容比图像更复杂等。 Stefano Ermon还探讨了Sora模型可能使用的技术,包括基于Transformer的架构、潜在编码来压缩数据并提高效率以及使用合成数据来提高训练数据的质量。 Stefano Ermon还探讨了模型能够生成长而连贯的视频的原因,他认为这可能是由于训练数据的高质量以及模型能够学习物理、对象持久性等概念。他指出,模型能够学习这些概念,可能是因为这些知识有助于模型更好地压缩和预测视频数据。 Stefano Ermon还对AI生成视频的未来发展趋势进行了展望,他认为,随着技术的进步,其他公司可能会开发出性能相近的模型,但OpenAI可能会保持领先地位。他还认为,更大的上下文窗口对于视频理解和生成非常有用,并且各种技术可以用于扩展视频模型的上下文窗口大小。 最后,Stefano Ermon还谈到了AI视频生成技术在通往通用人工智能的道路上的意义,他认为,高质量的AI视频生成模型可以作为一种世界模拟器,并为构建能够与现实世界交互的智能体提供有价值的知识。

Deep Dive

Shownotes Transcript

In early 2024, the notion of high fidelity, believable AI-generated video seemed a distant future to many. Yet, a mere few weeks into the year, OpenAI unveiled Sora, its new state of the art text-to-video model producing videos of up to 60 seconds. The output shattered expectations – even for other builders and researchers within generative AI – sparking widespread speculation and awe.

How does Sora achieve such realism? And are explicit 3D modeling techniques or game engines at play?

In this episode of the a16z Podcast, a16z General Partner Anjney Midha connects with Stefano Ermon, Professor of Computer Science at Stanford and key figure at the lab behind the diffusion models now used in Sora, ChatGPT, and Midjourney. Together, they delve into the challenges of video generation, the cutting-edge mechanics of Sora, and what this all could mean for the road ahead.

**Resources: **

Find Stefano on Twitter: https://twitter.com/stefanoermon)

Find Anjney on Twitter: https://twitter.com/anjneymidha)

Learn more about Stefano’s Deep Generative Models course: :

https://deepgenerativemodels.github.io)

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