The article "Myths and Legends in Learning" discusses common misconceptions in machine learning theory. It identifies six myths related to bias, search, computational theory, Occam’s razor, universal algorithms, and interactive learning. The paper also considers these issues from a Bayesian perspective. The researchers suggest questions that machine learning experts should explore both theoretically and experimentally.