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What makes a task relatively more or less difficult for a machine compared toa human? Much AI/ML research has focused on expanding the range of tasks thatmachines can do, with a focus on whether machines can beat humans. Allowing fordifferences in scale, we can seek interesting (anomalous) pairs of tasks T, T'.We define interesting in this way: The "harder to learn" relation is reversedwhen comparing human intelligence (HI) to AI. While humans seems to be able tounderstand problems by formulating rules, ML using neural networks does notrely on constructing rules. We discuss a novel approach where the challenge isto "perform well under rules that have been created by human beings." Wesuggest that this provides a rigorous and precise pathway for understanding thedifference between the two kinds of learning. Specifically, we suggest a largeand extensible class of learning tasks, formulated as learning under rules.With these tasks, both the AI and HI will be studied with rigor and precision.The immediate goal is to find interesting groundtruth rule pairs. In the longterm, the goal will be to understand, in a generalizable way, whatdistinguishes interesting pairs from ordinary pairs, and to define saliencybehind interesting pairs. This may open new ways of thinking about AI, andprovide unexpected insights into human learning.

Can We Distinguish Machine Learning from Human Learning?