AI Self Learning

AI self-learning refers to systems that can acquire new knowledge, skills, or behaviors autonomously without explicit human supervision or programming for each learning instance. This capability encompasses several approaches including self-supervised learning, where models learn from unlabeled data by creating their own training signals, continual learning that adapts to new information while retaining previous knowledge, and meta-learning that develops learning strategies applicable to novel tasks. Self-learning AI systems employ techniques like curiosity-driven exploration, active learning for strategic data selection, and transfer learning to apply existing knowledge to new domains. Examples include reinforcement learning agents that improve through trial-and-error interaction with environments, language models that learn from internet text, and computer vision systems that discover patterns in visual data. For AI agents, self-learning enables autonomous skill acquisition, adaptation to changing environments, and continuous improvement without constant human intervention, making systems more independent and capable of handling unforeseen scenarios.