It seems like most people think that reduced impact is as hard as value learning.
I think that’s not quite true; it depends on details of the AIs design.
I don’t agree that “It’s likely that all substantially easier AIs are too far from FAI to still be a net good.”, but I suspect the disagreement comes from different notions of “AI” (as many disagreements do, I suspect).
Taking a broad definition of AI, I think there are many techniques (like supervised learning) that are probably pretty safe and can do a lot of narrow AI tasks (and can maybe even be composed into systems capable of general intelligence).
For instance, I think the kind of systems that are being built today are a net good (but might not be if given more data and compute, especially those based on Reinforcement Learning).
It seems like most people think that reduced impact is as hard as value learning.
I think that’s not quite true; it depends on details of the AIs design.
I don’t agree that “It’s likely that all substantially easier AIs are too far from FAI to still be a net good.”, but I suspect the disagreement comes from different notions of “AI” (as many disagreements do, I suspect).
Taking a broad definition of AI, I think there are many techniques (like supervised learning) that are probably pretty safe and can do a lot of narrow AI tasks (and can maybe even be composed into systems capable of general intelligence). For instance, I think the kind of systems that are being built today are a net good (but might not be if given more data and compute, especially those based on Reinforcement Learning).