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There are nonetheless vital 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 robust fashions to mimic present weak mannequin errors, which may make generalization tougher sooner or later.
Nonetheless, we imagine our setup captures some key difficulties of aligning future superhuman fashions, enabling us to start out making empirical progress on this drawback immediately. There are lots of 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 imagine 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 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 assist analysis associated to weak-to-strong generalization.
Determining learn how to align future superhuman AI methods to be secure has by no means been extra vital, and it’s now simpler than ever to make empirical progress on this drawback. We’re excited to see what breakthroughs researchers uncover.
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