This chapter explores the phenomenon of weak supervision in deep learning models and how strong models can generalize beyond their weak supervisors. It discusses challenges in scaling alignment techniques and proposes simple methods for improvement, as well as exciting future directions and the introduction of the super-alignment fast grants program.
OpenAI's Superalignment team, launched this summer, has just published their first paper about weak-to-strong generalizations, and how they can analogize using weaker models to train more advanced models to simulate humans trying to control superhuman AI. Before that on the Brief, Intel's latest in the AI chip race.
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