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The industry is shifting toward AI models that predict physical interactions, causality, and intent through video generation. However, unlike traditional AI, these models create predictions about future states, which creates a critical validation bottleneck—you can't simply test them against existing labeled data.
Topics explored:
Validation paradox: How do you evaluate a model that generates predictions of what should happen rather than classifying what did happen?
Data curation at scale: Managing petabyte-scale video datasets while capturing rare edge cases (the "long tail" of unusual events) without prohibitive storage costs
Temporal consistency: Ensuring coherent physics and causality across video sequences when training data is fragmented across multiple storage systems
Solutions explored:
Feedback loops connecting generative model outputs back to real-world physical validation
Federated data strategies for managing distributed video datasets
Collaborative evaluation frameworks for assessing model quality
Techniques to transform video from raw storage into structured, queryable data assets
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