There are nonetheless necessary disanalogies between our present empirical setup and the final word downside of aligning superhuman fashions. For instance, it might be simpler for future fashions to mimic weak human errors than for present sturdy fashions to mimic present weak mannequin errors, which may make generalization more durable sooner or later.
However, we imagine our setup captures some key difficulties of aligning future superhuman fashions, enabling us to begin making empirical progress on this downside immediately. There are various promising instructions for future work, together with fixing the disanalogies in our setup, growing higher scalable strategies, and advancing our scientific understanding of when and the way we must always count on good weak-to-strong generalization.
We imagine that is an thrilling alternative for the ML analysis neighborhood to make progress on alignment. To kickstart extra analysis on this space,
- We’re releasing open supply code to make it straightforward to get began with weak-to-strong generalization experiments immediately.
- We’re launching a $10 million grants program for graduate college students, teachers, and different researchers to work on superhuman AI alignment broadly. We’re particularly excited to help analysis associated to weak-to-strong generalization.
Determining learn how to align future superhuman AI techniques to be protected has by no means been extra necessary, and it’s now simpler than ever to make empirical progress on this downside. We’re excited to see what breakthroughs researchers uncover.