  Conceptual abstraction and analogy-making are key abilities underlying
humans' abilities to learn, reason, and robustly adapt their knowledge to new
domains. Despite of a long history of research on constructing AI systems with
these abilities, no current AI system is anywhere close to a capability of
forming humanlike abstractions or analogies. This paper reviews the advantages
and limitations of several approaches toward this goal, including symbolic
methods, deep learning, and probabilistic program induction. The paper
concludes with several proposals for designing challenge tasks and evaluation
measures in order to make quantifiable and generalizable progress in this area.
