Developing theories of how information is processed to yield inductive inferences is a key step in understanding intelligence in humans and machines. Humans, across tasks as diverse as vision and decision making, appear to be extremely adaptive and successful in dealing with uncertainty in the world. Yet even a cursory examination of the books and journals covering machine learning reveals that this branch of AI rarely draws on the cognitive system as a source of insight. In this article I show how fast and frugal heuristics - cognitive process models of inductive inference - frequently outperform a wide selection of standard machine learning algorithms. This finding suggests a cognitive-inspired route toward robust inference in the context of meta-learning.