Uri and I discuss his recent perspective that conceives of brains as super-over-parameterized models that try to fit everything as exactly as possible rather than trying to abstract the world into usable models. He was inspired by the way artificial neural networks overfit data when they can, and how evolution works the same way on a much slower timescale.
Show notes:
Support the show to get full episodes, full archive, and join the Discord community. Gabriele Scheler co-founded the Carl Correns Foundation for Mathematical Biology....
Brian and I discuss a range of topics related to his latest book, The Alignment Problem: Machine Learning and Human Values. The alignment problem...
Dileep and I discuss his theoretical account of how the thalamus and cortex work together to implement visual inference. We talked previously about his...
