Publications
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
This paper introduces Guaranteed Safe (GS) AI, an approach to AI safety that ensures high-assurance quantitative safety guarantees. It relies on three core components—a world model, a safety specification, and a verifier—to mathematically verify that AI systems meet safety requirements.
Adversarial Policies Beat Superhuman Go AIs
We describe an attack on the state-of-the-art Go-playing AI system, KataGo. The adversaries do not win by learning to play Go better than KataGo but instead by tricking KataGo into making serious blunders, demonstrating that even superhuman AI systems may harbor surprising failure modes.
imitation: Clean Imitation Learning Implementations
We describe a software package called "imitation" which provides PyTorch implementations of several imitation and reward learning algorithms, including three inverse reinforcement learning algorithms, three imitation learning algorithms, and a preference comparison algorithm.
