Continual Learning (Lifelong Learning)
Continual learning, also known as lifelong learning, refers to an AI system's ability to learn new tasks sequentially over time without forgetting previously learned knowledge. Unlike traditional models that are retrained from scratch or fine-tuned on new data, continual learning aims to incrementally adapt, addressing the problem of 'catastrophic forgetting' where new information overwrites old. This often involves memory replay, regularization techniques, or architectural changes to protect past knowledge.
It's like a student who learns history, then math, and can still recall history while mastering math, rather than forgetting history when they start math.
It's crucial for AI systems operating in dynamic, real-world environments where new data and tasks continuously emerge.
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