Daniel Murfet

Daniel Murfet works on singular learning theory and its applications to deep learning and AI alignment. His research examines how learning dynamics, phase transitions, and the geometry of neural networks can help explain how models learn; he has also worked in algebraic geometry, mathematical physics, and logic.

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