A big question that determines a lot about what risks from AGI/ASI may look like has to do with the kind of things that our universe's laws allow to exist. There is an intuitive sense in which these laws, involving certain symmetries as well as the inherent smoothing out caused by statistics over large ensembles and thus thermodynamics, etc., allow only certain kinds of things to exist and work reliably. For example, we know "rocket that travels to the Moon" is definitely possible. "Gene therapy that allows a human to live and be youthful until the age of 300" or "superintelligent AGI" are probably possible, though we don't know how hard. "Odourless ambient temperature and pressure gas that kills everyone who breathes it if and only if their name is Mark with 100% accuracy" probably is not. Are there known attempts at systematising this issue using algorithmic complexity, placing theoretical and computational bounds, and so on so forth?
This is very, very dependent on what assumptions you fundamentally make about the nature of physical reality, and what assumptions you make about how much future civilizations can alter physics.
I genuinely think that if you want to focus on the long term, unfortunately we'd need to solve very, very difficult problems in physics to reliably give answers.
For the short term limitations that are relevant to AI progress, I'd argue that the biggest one is probably thermodynamics stuff, and in particular the Landauer limit is a good approximation for why you can't make radically better nanotechnology than life without getting into extremely weird circumstances, like reversible computation.
By having a MD in Engineering and a Physics PhD, following the same exact courses you recommend as potentially containing the answer and in fact finding no direct answer to these specific questions in them.
You could argue "the answer can be derived from that knowledge" and sure, if it exists it probably can, but that's why I'm asking. Lots of theories can be derived from other knowledge. Most of machine learning can be derived from a basic knowledge of Bayes' theorem and multivariate calculus, but that doesn't make any math undergrad a ML expert. I was ask... (read more)