Few-shot learning

Few-shot learning is the capacity of a system to generalize from a small number of examples — typically fewer than a dozen — rather than requiring the massive datasets that drive conventional machine learning. It is the closest artificial approximation to human concept acquisition: a child needs only one or two examples to learn 'giraffe,' while a neural network might need thousands. The gap between human and machine few-shot learning is one of the central unsolved problems in artificial intelligence and the primary motivation for meta-learning research.

The challenge is not merely statistical. A system that learns from few examples must bring powerful inductive biases to the task — prior knowledge about what kinds of patterns are likely, what features matter, and how concepts compose. These biases cannot be learned from the few examples themselves; they must be meta-learned from prior experience across many related tasks. Few-shot learning is therefore not a distinct technique but a diagnostic: it measures whether a system has acquired the right learning structure, not merely the right function.

The extreme case — learning from a single example — is one-shot learning, which remains largely beyond current artificial systems despite being routine in human cognition.