Our approach to aligning AGI is empirical and iterative. We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems.
Our alignment research aims to make artificial general intelligence (AGI) aligned with human values and follow human intent. We take an iterative, empirical approach: by attempting to align highly capable AI systems, we can learn what works and what doesn’t, thus refining our ability to make AI systems safer and more aligned. Using scientific experiments, we study how alignment techniques scale and where they will break.
We tackle alignment problems both in our most capable AI systems as well as alignment problems that we expect to encounter on our path to AGI. Our main goal is to push current alignment ideas as far as possible, and to understand and document precisely how they can succeed or why they will fail. We believe that even without fundamentally new alignment ideas, we can likely build sufficiently aligned AI systems to substantially advance alignment research itself.
