The Inside View cover image

Jesse Hoogland on Developmental Interpretability and Singular Learning Theory

The Inside View

00:00

Singular Learning Theory: A New Approach to Machine Learning

SLT predicts discrete changes, sudden changes in the kinds of computations being performed by a model over the course of training. It tells us that we should expect there to be phase transitions and that would tell us something very significant about what makes neural networks different from other kind of networks. The theory is built in another learning paradigm, Bayesian learning. And so there's still some work to apply this to the learning paradigm we have in the case of neural Networks.

Transcript
Play full episode

The AI-powered Podcast Player

Save insights by tapping your headphones, chat with episodes, discover the best highlights - and more!
App store bannerPlay store banner
Get the app