SLT for AI Safety

This sequence draws from a position paper co-written with Simon Pepin Lehalleur, Jesse Hoogland, Matthew Farrugia-Roberts, Susan Wei, Alexander Gietelink Oldenziel, Stan van Wingerden, George Wang, Zach Furman, Liam Carroll, Daniel Murfet. Thank you to Stan, Dan, and Simon for providing feedback on this post. 

Singular Learning Theory (SLT) is a theory of Bayesian statistics that suggests the key to understanding deep learning is the geometry (specifically, the degeneracies) of the loss function and parameter-function map. SLT provides a starting point for understanding how learned algorithms underlie generalization and how training data determines those learned algorithms.