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Not based on that datum alone. IMHO solving INS blowup may qualify as superintelligence, depending on how much of the herculean effort it expended turns out to be necessary vs how much turns out to have been brute-forcing an excessively complicated solution to a problem that actually has a still-unknown simpler solution. But it's not quite general intelligence. With proof verification, math ends up being in the same category as Chess and Go: a system solving problems with automatically verifiable rules and outcomes can be trained to learn from "self-play" indefinitely, which makes the efficiency with which it learns much less relevant, which makes the inefficiency of modern AI training ("we're going to start with every bit of recorded human text, then try to generate more because that's not enough") much less of a handicap. General intelligence isn't "a billion man-years of training on math can make you able to do advanced mathematics", it's "a billion man-years of training on hunting and gathering plus twenty years of training on math can make you able to do advanced mathematics".
You may be right anyway. We're at the point where we're having difficulty just coming up with metrics where humans still beat the top AIs, despite the remaining deficit of transfer learning for the latter. Either they're passing all the important tests, or we're failing a big test ourselves.
Are we? It seems like the major problem is that every attempt to create a metric just creates a recursive Goodhart's law problem. The metric now becomes a target for training of further model capabilities because there are billions of dollars of incentives to do so. One would need to create a metric where there is no or a very restricted set of training data such that it is impossible for AI labs to actually finetune on said data.
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