I once heard a senior mainstream AI type suggest that we might try to quantify the intelligence of an AI system in terms of its RAM, processing power, and sensory input bandwidth.

Of course - this is correct. An AI system, like intelligence in general, is an algorithm and is thus governed by computational complexity theory and the physics of computation.

This at once reminded me of a quote from Dijkstra: "If we wish to count lines of code, we should not regard them as 'lines produced' but as 'lines spent': the current conventional wisdom is so foolish as to book that count on the wrong side of the ledger."

Code length is one fundemental quantitative measure (as in kolmogorv complexity), important in information theory, but it is not directly related nor to be confused with primary physical computational quantities such as space and time. Any pattern can be compressed - trading off time for space (expending energy to conserve mass).

If you want to measure the intelligence of a system, I would suggest measuring its optimization power as before, but then dividing by the resources used. Or you might measure the degree of prior cognitive optimization required to achieve the same result using equal or fewer resources. Intelligence, in other words, is efficient optimization.

The computational effeciency of one's intelligence algorithm is important, but effeciency is not the same as power, whether one is talking about heat engines or computational systems. Effeciency in computation is a measure of how much computation you get out for how much matter and energy you put in.

Intelligence, in typical english usage, does not connotate an effeciency measure - it connotates a power measure. If you have a super-computer AI that can think at a 3rd grade level, and you have a tiny cell phone AI that uses 1000 times less resources but thinks at a 2nd grade level, we'd still refer to the super-computer AI as being more intelligent, regardless of how effecient it is.

Intelligence is a computational power measure, but it is not a single scalar - it has temporal and spatial components (speed, depth, breadth, etc).

So if we say "efficient cross-domain optimization" - is that necessary to convey the wisest meaning of "intelligence", after making a proper effort to factor out anthropomorphism in ranking solutions?

I think "powerful generalized optimization" is closer to what you want, but one may also want to distinguish between static and dynamic intelligence (hard-coded vs adaptive). I'd also say that intelligence is a form of optimization, but optimization is a broader term. There are many computational optimization processes, most of which one would be hard pressed to call 'intelligent'.