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World Models | John Langford (Microsoft AI Labs)

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John Langford of Microsoft AI Labs discusses whether compact implicit models of the world can improve data efficiency. The conversation examines belief-state compression, Transformer caches, JEPA-style objectives and his Next Latent research. It also covers the value of algorithmic research alongside scaling, agent-assisted experiments, open models, the history of CAPTCHA and the behavior of modern optimizers. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Topics

compact implicit modelsdata efficiencybelief-state compressionTransformer cachesJEPA-style objectivesNext Latentalgorithmic researchopen modelsmodern optimizers

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