Abstract: In this paper, I pose a major challenge for AI researchers: to develop

systems that learn in a human-like manner. I briefly review the

history of machine learning, noting that early work made close contact

with results from cognitive psychology but that this is no longer the

case. I identify seven characteristics of human behavior that, if

reproduced, would offer better ways to acquire expertise than

statistical induction over massive training sets. I illustrate these

points with two domains - mathematics and driving - where people

are effective learners and review systems that address them. In

closing, I suggest ways to encourage more research on human-like

learning.