Thinking of mathematics, an abstraction is only as valuable as what it can tell us about the underlying system— a laplace transformation into the frequency domain isn't particularly interesting unless we either a) happen to care about some frequency domain property of the function, or b) we can manipulate the transformed function and then un-transform it afterward, yielding a novel insight in the original time domain.
My layman's assessment is that the current state of the art for machine cognition is about on the level of identifying that such a transformation might be possible, but not actually applying it or doing anything about it, much less unapplying it afterward.
Maybe we disagree about whether that last point means it can meaningfully “do abstraction” or not.