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Text Diffusion Models with Brendan O'Donoghue (Google DeepMind)

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Google DeepMind research director Brendan O'Donoghue examines discrete diffusion as an alternative to autoregressive text generation. He discusses sample diversity, reinforcement learning, latency and potential on-device or robotics applications. The episode also addresses the costs of serving diffusion models, hardware trade-offs between computation and memory bandwidth, and practical limits on progress toward more capable AI. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Topics

discrete diffusionautoregressive text generationsample diversityReinforcement LearninglatencyRoboticsdiffusion modelsmemory bandwidthBrendan O'Donoghue

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