Continuous Learning is an AI capability in which a system can incorporate new data, feedback, or experiences over time to improve its performance or adapt to changing conditions. Rather than remaining dependent on a model trained only once, a continuously learning system can update its knowledge or behavior as new information becomes available.
This approach is useful in environments where patterns, user behavior, or operating conditions change frequently. Recommendation systems can adapt to evolving preferences, fraud detection models can respond to emerging transaction patterns, and predictive maintenance systems can refine their understanding as new equipment data is collected. Continuous Learning typically requires mechanisms for ongoing data collection, model updating, performance evaluation, and safeguards against learning from inaccurate or harmful data.
