a16z Podcast Beyond Uncanny Valley: Breaking Down Sora
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Feb 24, 2024 
In this engaging discussion, Stefano Ermon, a leading Professor of Computer Science at Stanford, reveals the inner workings of OpenAI's groundbreaking Sora model for AI-generated video. He discusses the shift from GANs to diffusion models and the significance of high-quality training data. The conversation explores the uncanny valley and how Sora's capabilities could reshape our understanding of video compression and generation. Ermon also hints at the exciting future of personalized video content and its applications in various fields.
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Video Diffusion Complexity
- Video diffusion models are more complex than text or image generation due to several factors.
- These include higher compute costs, data availability and quality, and the complexity of video content itself.
Resource Challenges in Video Diffusion
- Training video diffusion models requires significantly more compute and memory than images.
- High-quality, labeled video datasets are also scarce, unlike readily available image datasets.
Sora's Architecture
- Sora likely uses a transformer-based architecture, unlike earlier convolutional approaches.
- It also probably operates on latent representations of video data for efficiency.