I think that a is just a special case of a narrow AI.
Like, GAI is dangerous because it can do anything, and would probably ruin this section of the universe for us if its goals were misaligned with ours.
I'm not sure if GAI is needed to do highly domain-specific tasks like a.
Yeah, this looks right. I guess you could rephrase my post as saying that narrow AI could solve most problems we'd want an AI to solve, but with less danger than the designs discussed on LW (e.g. UDT over Tegmark multiverse).
According to Eliezer, making AI safe requires solving two problems:
1) Formalize a utility function whose fulfillment would constitute "good" to us. CEV is intended as a step toward that.
2) Invent a way to code an AI so that it's mathematically guaranteed not to change its goals after many cycles of self-improvement, negotiations etc. TDT is intended as a step toward that.
It is obvious to me that (2) must be solved, but I'm not sure about (1). The problem in (1) is that we're asked to formalize a whole lot of things that don't look like they should be necessary. If the AI is tasked with building a faster and more efficient airplane, does it really need to understand that humans don't like to be bored?
To put the question sharply, which of the following looks easier to formalize:
a) Please output a proof of the Riemann hypothesis, and please don't get out of your box along the way.
b) Please do whatever the CEV of humanity wants.
Note that I'm not asking if (a) is easy in absolute terms, only if it's easier than (b). If you disagree that (a) looks easier than (b), why?