[pinkmuffinere]

I read this to mean “just because the process doesn’t match the problem, that doesn’t mean it’s inefficient”. But I think it does mean that. I expect we intuitively know that data structures which match the structure of a problem are more efficient than those that don’t. I think the same thing applies here.

I realize my argument is hand wavey, i haven’t defined “efficient“ (in space? Time? Energy?), and there are other shortcomings, but I feel this is “good enough” to be convincing

[wizzwizz4]

Example: a list of (key, value) pairs is a perfectly valid way to implement a map, and suffices. However, a more complicated tree structure, perhaps with hashed keys, is usually way more efficient, which is increasingly-noticeable as the number of pairs stored in the map grows large.

[jama211]

I suppose there’s something in what you’re saying, it’s just that’s it’s sorta vague and hard to parse for me. It also depends on the higher order problem space, for example: is it efficient if the problem is defined by “make something that can adapt to a problem space and solve it without manual engineering” rather than “make something with a long lead up time where you understand the problem space in advance and therefore have time to optimise the engine”. In the former, the neural network would indeed count as solving this efficiently, because of the given definition of the goal.