= 27d567bc2d48d9f40e9ccc20ea370b15)
capybaralet comments on Risks from Approximate Value Learning - Less Wrong Discussion
You are viewing a comment permalink. View the original post to see all comments and the full post content.
= 783df68a0f980790206b9ea87794c5b6)
Loading…= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Powered by Reddit
= f037147d6e6c911a85753b9abdedda8d)
You are viewing a comment permalink. View the original post to see all comments and the full post content.
Comments (10)
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).