Jason Gross

In this engaging talk, Jason Gross, a researcher in mechanistic interpretability and software verification, dives into the fascinating world of compact proofs. He discusses their crucial role in benchmarking AI interpretability and how they help prove model performance. The conversation also touches on the challenges of randomness and noise in neural networks, the intersection of proofs and modern machine learning, and innovative approaches to enhancing AI reliability. Plus, learn about his startup focused on automating proof generation and the road ahead for AI safety!