Self-supervised learning

Self-supervised learning is a training approach in which a model creates its own learning signal from unlabeled data, such as predicting a hidden word from its context. It underpins modern language model pretraining.

Self-supervised learning sits in the Foundations part of the Agentik {OS} glossary, which defines the words used to build and run AI agent systems.

Also called SSL.