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 ...
Solving the value learning problem is (IMO) the key technical challenge for AI safety.
How good or bad is an approximate solution?
EDIT for clarity:
By "approximate value learning" I mean something which does a good (but suboptimal from the perspective of safety) job of learning values. So it may do a good enough job of learning values to behave well most of the time, and be useful for solving tasks, but it still has a non-trivial chance of developing dangerous instrumental goals, and is hence an Xrisk.
Considerations:
1. How would developing good approximate value learning algorithms effect AI research/deployment?
It would enable more AI applications. For instance, many many robotics tasks such as "smooth grasping motion" are difficult to manually specify a utility function for. This could have positive or negative effects:
Positive:
* It could encourage more mainstream AI researchers to work on value-learning.
Negative:
* It could encourage more mainstream AI developers to use reinforcement learning to solve tasks for which "good-enough" utility functions can be learned.
Consider a value-learning algorithm which is "good-enough" to learn how to perform complicated, ill-specified tasks (e.g. folding a towel). But it's still not quite perfect, and so every second, there is a 1/100,000,000 chance that it decides to take over the world. A robot using this algorithm would likely pass a year-long series of safety tests and seem like a viable product, but would be expected to decide to take over the world in ~3 years.
Without good-enough value learning, these tasks might just not be solved, or might be solved with safer approaches involving more engineering and less performance, e.g. using a collection of supervised learning modules and hand-crafted interfaces/heuristics.
2. What would a partially aligned AI do?
An AI programmed with an approximately correct value function might fail
* dramatically (see, e.g. Eliezer, on AIs "tiling the solar system with tiny smiley faces.")
or
* relatively benignly (see, e.g. my example of an AI that doesn't understand gustatory pleasure)
Perhaps a more significant example of benign partial-alignment would be an AI that has not learned all human values, but is corrigible and handles its uncertainty about its utility in a desirable way.