The Challenges of Interpretability in AI Research

Interpretability as a goal in AI research is being able to look inside our systems and say what they're doing. Sam says there are two main ways to approach this problem. One is to try to decipher the systems we already have, to understand what these billions of numbers going up and down actually mean. The other avenue of research is trying to build systems that can do a lot of the powerful things that we're excited about with something like UBD4 but where there aren't giant inscrutable piles of numbers in the middle.