  Concept induction requires the extraction and naming of concepts from noisy
perceptual experience. For supervised approaches, as the number of concepts
grows, so does the number of required training examples. Philosophers,
psychologists, and computer scientists, have long recognized that children can
learn to label objects without being explicitly taught. In a series of
computational experiments, we highlight how information in the environment can
be used to build and align conceptual systems. Unlike supervised learning, the
learning problem becomes easier the more concepts and systems there are to
master. The key insight is that each concept has a unique signature within one
conceptual system (e.g., images) that is recapitulated in other systems (e.g.,
text or audio). As predicted, children's early concepts form readily aligned
systems.
