Explore a novel approach to understanding neural network behaviors in this 59-minute lecture by Paul Christiano from the Alignment Research Center. Delve into the limitations of current mechanistic interpretability research and the challenges of formal proofs for model properties. Discover an alternative strategy for explaining specific neural network behaviors that balances between informal understanding and rigorous proofs. Gain insights into a promising research direction and theoretical questions aimed at improving AI safety and interpretability. Learn how this approach, while not as comprehensive as formal proofs, may offer comparable safety benefits in the field of AI alignment.