självlärande

Självlärande is a term used to describe systems or processes that improve their performance autonomously through experience rather than being explicitly reprogrammed. In the context of artificial intelligence, självlärande systems adapt to new data, tasks or environments by updating their behavior based on feedback signals, such as rewards or corrections, often without human intervention.

Key approaches fall under online learning, reinforcement learning and self-supervised learning. Reinforcement learning trains an agent through interactions with an environment to maximize cumulative reward, while online learning updates models incrementally as new data arrives. Self-supervised learning derives supervisory signals from the data itself, enabling learning from unlabeled information. Lifelong or continual learning aims to acquire new skills without forgetting previously learned ones, addressing persistence over time.

Benefits of självlärande systems include greater adaptability to changing conditions, reduced need for manual reconfiguration and potential scalability across tasks. Challenges encompass safety and robustness, data drift, exploration-exploitation balance, computational demands, and issues of interpretability and accountability. Ensuring alignment with human values and preventing undesirable behavior due to mis-specified objectives are ongoing concerns.

Applications span robotics and autonomous control, adaptive user interfaces and recommendations, industrial automation, fraud detection, and educational technologies that tailor content to learners. The research landscape combines methods from reinforcement learning, optimization, meta-learning and deep learning to enable more capable and resilient self-improving systems. In education contexts, the term självlärande can also describe human self-regulated learning, illustrating its broad usage beyond technology.