Self-Supervised Learning for AI Agents

AI agents use self-supervised learning to generate their own training signals from data, rather than relying on manually labeled datasets.

This approach allows agents to improve from vast amounts of unstructured data, making them more adaptable in tasks like understanding text, images, or speech in real-time applications.