Continual and Lifelong Learning in Artificial Intelligence
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I3P115Keywords:
Continual Learning, Lifelong Learning, Catastrophic Forgetting, Knowledge Consolidation, Transfer Learning, Incremental Learning, Adaptive Neural Networks, Online Learning, Memory Replay, Task GeneralizationAbstract
Artificial intelligence systems have achieved remarkable performance in specialized tasks, yet most traditional models operate under a static learning paradigm: they are trained once on fixed datasets and then deployed without the ability to adapt continuously. In contrast, human intelligence evolves through ongoing experience, incrementally acquiring knowledge, refining skills, and integrating new information without catastrophically forgetting prior learning. Continual and lifelong learning in artificial intelligence seeks to replicate this adaptive capability by enabling models to learn sequentially from dynamic data streams while preserving previously acquired knowledge. This paradigm addresses critical limitations of conventional machine learning, including catastrophic forgetting, limited generalization across tasks, and inefficiency in data utilization. By integrating memory mechanisms, knowledge consolidation strategies, adaptive architectures, and transfer learning principles, continual learning systems aim to support scalable, flexible, and robust AI in real-world environments. This article presents a comprehensive and in-depth exploration of continual and lifelong learning, covering theoretical foundations, algorithmic approaches, system architectures, evaluation methodologies, application domains, and ethical considerations. It highlights the transformative potential of adaptive AI systems capable of sustained learning across time, domains, and tasks.
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