My response in the comment section:
What I expect from formal "analytic philosophy" methods:
1) A useful decomposition of the issue into problems and subproblems (eg AI goal stability, AI agency, reduced impact, correct physical models on the universe, correct models of fuzzy human concepts such as human beings, convergence or divergence of goals, etc...)
2) Full or partial solutions some of the subproblems, ideally of general applicability (so they can be added easily to any AI design).
3) A good understanding of the remaining holes.
and lastly:
4) Exposing the implicit assumptions in proposed (non-analytic) solutions to the AI risk problem, so that the naive approaches can be discarded and the better approaches improved.
Ben expanded his original article by editing a reply to your points into the end.
We're pleased to announce the release of "Smarter Than Us: The Rise of Machine Intelligence", commissioned by MIRI and written by Oxford University’s Stuart Armstrong, and available in EPUB, MOBI, PDF, and from the Amazon and Apple ebook stores.
Can we instruct AIs to steer the future as we desire? What goals should we program into them? It turns out this question is difficult to answer! Philosophers have tried for thousands of years to define an ideal world, but there remains no consensus. The prospect of goal-driven, smarter-than-human AI gives moral philosophy a new urgency. The future could be filled with joy, art, compassion, and beings living worthwhile and wonderful lives—but only if we’re able to precisely define what a “good” world is, and skilled enough to describe it perfectly to a computer program.
AIs, like computers, will do what we say—which is not necessarily what we mean. Such precision requires encoding the entire system of human values for an AI: explaining them to a mind that is alien to us, defining every ambiguous term, clarifying every edge case. Moreover, our values are fragile: in some cases, if we mis-define a single piece of the puzzle—say, consciousness—we end up with roughly 0% of the value we intended to reap, instead of 99% of the value.
Though an understanding of the problem is only beginning to spread, researchers from fields ranging from philosophy to computer science to economics are working together to conceive and test solutions. Are we up to the challenge?
Special thanks to all those at the FHI, MIRI and Less Wrong who helped with this work, and those who voted on the name!