That was my point that, AI has been confused with the deep learning gains in the past years, even though, the hard part about how to coordinate all those inputs and also give the right commands to actuators is still not near to solved, much less a completely virtual superintelligence that has its own "virtual" goals and virtual simulations of both its inputs and potential actuators in the real world. This would be akin to a kind of intelligence algorithm, and not deep learning vector algorithms that reveal structures in data.
My guess is that paperclipping the world takes magnitudes more intelligence than understanding what a human means when saying something ambiguous.
For example, maybe you inadvertently programmed it to "be totally literally 100% certain that you've completed the task I told you to do". Then from its perspective, there's always a tiny, tiny chance that its sensors are being fooled or malfunctioning, so it can't be literally 100% certain that it's ever made a paperclip successfully, so by your programming, it should keep making more paperclips. This is independent of whether or not you wanted it to do that: it's a result of what you programmed it to do and what you told it to do.
We also know that paperclipping the world will get in the way of other goals, like going to the show or making money.
What is "ridiculous" about continuing a task because we're not certain that it's done yet? It's only your human moral system saying that. Just because humans usually don't value things enough to pursue them to the exclusion of all else…
It understands some to mean more than one and less than infinity. In fact, some means less than "a lot". The meaning of a lot depends on the context, which happens to be paperclips for me.
What is "some" paperclips for me? It depends on how many papers I might need to clip (or whatever use I might have for paperclips). My super intelligent assistant would be able to work out a good estimate.
After having an estimate, it can go make me "some" paperclips, and then stop somewhere short of paper clipping the entire world.
Alternatively, it could just ask me how many "some" means.
I don't pretend to have any sort of expertise in these sorts of discussions, so I thought I would throw out some easily wikipedia'd terms that seem to back your thoughts regarding convergence vs. exponentiation.
What an agent considers to be "good" is orthogonal to how intelligent that agent is. An agent of arbitrary intelligence can have arbitrary goals; the goals of an intelligent agent need not in principle look anything like those of a human. The only reason a superintelligent AI's goals would look like those of a human is because the humans very, very carefully programmed them into the AI somehow. Very, very careful programming is not a feature of how humans currently approach AI research.
Of course, the obvious solution is: "make the objective function equivalent to 'do what I want you to do'", but the problem is we might not know how to encode that without help from a super-AI.
Doesn't matter how determined the machine is to make them, the rest of the world won't exactly lay down and let it. Nor would its goals often be possible with the amount of resources available in its surroundings.
It's basically the cat problems from this tongue in cheek article: