The biggest piece (IMO) would be figuring out key properties of human values. If we look at e.g. your sequence on value learning, the main takeaway of the section on ambitious value learning is “we would need more assumptions”. (I would also argue we need different assumptions, because some of the currently standard assumptions are wrong—like utility functions.)
That’s one thing selection theorems offer: a well-grounded basis for new assumptions for ambitious value learning. (And, as an added bonus, directly bringing selection into the picture means we also have an angle for characterizing how much precision to expect from any approximations.) I consider this the current main bottleneck to progress on outer alignment: we don’t even understand what kind-of-thing we’re trying to align AI with.
(Side-note: this is also the main value which I think the Natural Abstraction Hypothesis offers: it directly tackles the Pointers Problem, and tells us what the “input variables” are for human values.)
Taking a different angle: if we’re concerned about malign inner agents, then selection theorems would potentially offer both (1) tools for characterizing selection pressures under which agents are likely to arise (and what goals/world models those agents are likely to have), and (2) ways to look for inner agents by looking directly at the internals of the trained systems. I consider our inability to do (2) in any robust, generalizable way to be the current main bottleneck to progress on inner alignment: we don’t even understand what kind-of-thing we’re supposed to look for.
The biggest piece (IMO) would be figuring out key properties of human values. If we look at e.g. your sequence on value learning, the main takeaway of the section on ambitious value learning is “we would need more assumptions”. (I would also argue we need different assumptions, because some of the currently standard assumptions are wrong—like utility functions.)
That’s one thing selection theorems offer: a well-grounded basis for new assumptions for ambitious value learning. (And, as an added bonus, directly bringing selection into the picture means we also have an angle for characterizing how much precision to expect from any approximations.) I consider this the current main bottleneck to progress on outer alignment: we don’t even understand what kind-of-thing we’re trying to align AI with.
(Side-note: this is also the main value which I think the Natural Abstraction Hypothesis offers: it directly tackles the Pointers Problem, and tells us what the “input variables” are for human values.)
Taking a different angle: if we’re concerned about malign inner agents, then selection theorems would potentially offer both (1) tools for characterizing selection pressures under which agents are likely to arise (and what goals/world models those agents are likely to have), and (2) ways to look for inner agents by looking directly at the internals of the trained systems. I consider our inability to do (2) in any robust, generalizable way to be the current main bottleneck to progress on inner alignment: we don’t even understand what kind-of-thing we’re supposed to look for.