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The specialization of human intelligence, perhaps paradoxically, is also the source of its generalization power.
To learn from data, one must make assumptions about it. The exact nature and structure of these assumptions in humans is what defines our capabilities -- strong enough to enable efficient learning, abstract enough to generalize to a broad range of tasks.
Slightly off topic, there's good reason to be suspicious of "generalists" who are not also "specialists" in something. I don't think one can generalize usefully unless one already has access to a generalizable "structure" in which new knowledge can be rooted; sorted, interpreted.
*off topic, in the same vein. I tend to think that deeper the specialization (as long as the object of specialization could afford a powerful enough meta-language), stronger the generalizability to areas outside the specialization.
The focus with concepts gives a way to find explanations, to prove some theorems. But, in the other hand, you need to find gates between concepts corpus to make more general explanations. I do not know if machine learning is the way to find the better universal language.
It seems that reinforcement learning gives a great challenge in the way it needs to learn several "specialisations" depending on the env that the system is interacting with and discovering. Then, it needs to make the sys learn the "specialisation" itself.
I think human jump to conclusions first then try to fill out the equations, eg what if space time is interwoven
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