Continual Learning in Artificial Intelligence

Real-world AI systems must do something surprisingly difficult: keep learning without forgetting. This talk introduces Continual Learning (CL) — the field dedicated to building machine learning models that adapt to new information sequentially, without catastrophically overwriting what they already know. We examine the core stability-plasticity dilemma, survey the major algorithmic families addressing it (regularization, replay, architectural expansion, and meta-learning), and explore how large pretrained models are reshaping the landscape. Along the way, we connect theory to practice through applications in autonomous systems, healthcare, natural language processing, and edge AI. We also address the field's open challenges: benchmarking gaps, fairness risks, privacy constraints, and the governance of models that continuously evolve post-deployment. By the end, attendees will have a clear map of where continual learning stands today, what remains unsolved, and why it represents one of the most consequential research frontiers in modern AI.