The article discusses how learning strategies that seem illogical can actually solve complex problems, and how learning from fewer examples can lead to more effective learning. These findings apply to both pure theoretical inductive inference and practical algorithmic learning. This suggests that ideas from inductive inference can be valuable for improving algorithmic learning theory.