Self Directed Learning

Self-directed learning (SDL) explores how systems can autonomously acquire knowledge and skills without explicit external guidance, focusing on efficient data selection, model adaptation, and performance evaluation. Current research emphasizes the development of algorithms and frameworks that enable SDL in diverse applications, including personalized keyword spotting, large language model refinement, and brain decoding, often leveraging techniques like autoencoders, reinforcement learning, and self-supervised learning. The advancements in SDL hold significant promise for improving the efficiency and adaptability of machine learning systems, as well as offering insights into human learning processes and potentially leading to more robust and generalizable AI.