Advancement of Continual Learning Architectures for Scalable Artificial Intelligence in Evolving Data Ecosystems
Keywords:
continual learning, catastrophic forgetting, scalable artificial intelligence, lifelong learning, evolving data ecosystems, task-incremental learning, memory replay, dynamic environments, modular architectures, knowledge consolidationAbstract
Continual learning (CL) has emerged as a pivotal paradigm in advancing artificial intelligence (AI) systems that operate in dynamic and evolving data environments. As AI is increasingly deployed across diverse, real-time, and large-scale applications, scalability and adaptability have become core requisites. Continual learning architectures seek to address challenges of catastrophic forgetting, domain shift, and knowledge integration in non-stationary settings. This paper explores the advancement of CL architectures by identifying foundational constraints, evaluating innovations in model scalability, and proposing pathways for sustainable deployment in evolving data ecosystems. Drawing on established, we highlight conceptual and architectural evolution across memory management, modular learning, and dynamic task inference. We also present a comparative analysis of architectural strategies and performance metrics used in representative CL systems. Through structured charts, flow diagrams, and comparative tables, the paper synthesizes the state of the field and suggests future research directions for robust, lifelong AI systems.
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Copyright (c) 2023 Richard Milion (Author)
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