There are nonetheless essential disanalogies between our present empirical setup and the final word drawback 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 might make generalization more durable sooner or later.
Nonetheless, we consider our setup captures some key difficulties of aligning future superhuman fashions, enabling us to begin making empirical progress on this drawback as we speak. 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 should always count on good weak-to-strong generalization.
We consider that is an thrilling alternative for the ML analysis group to make progress on alignment. To kickstart extra analysis on this space,
- We’re releasing open supply code to make it simple to get began with weak-to-strong generalization experiments as we speak.
- We’re launching a $10 million grants program for graduate college students, lecturers, and different researchers to work on superhuman AI alignment broadly. We’re particularly excited to assist analysis associated to weak-to-strong generalization.
Determining find out how to align future superhuman AI programs to be protected has by no means been extra essential, and it’s now simpler than ever to make empirical progress on this drawback. We’re excited to see what breakthroughs researchers uncover.