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WhySpace comments on Risks from Approximate Value Learning - Less Wrong
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I actually brought up a similar question in the open thread, but it didn't really go very far. May or may not be worth reading, but it's still not clear to me whether such a thing is even practical. It's likely that all substantially easier AIs are too far from FAI to still be a net good.
I've come a little closer to answering my questions by stumbling on this Future of Humanity Institute video on "Reduced Impact AI". Apparently that's the technical term for it. I haven't had a chance to look for papers on the subject, but perhaps some exist. No hits on google scholar, but a quick search shows a couple mentions on LW and MIRI's website.
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).