Continual Learning: Overcoming Catastrophic Forgetting for Adaptive AI Systems

Abstract

Continual learning is a fundamental challenge in artificial intelligence (AI) that aims to enable models to learn from a continuous stream of data while retaining previously acquired knowledge. Unlike traditional machine learning, which operates in static environments, continual learning requires algorithms to adapt incrementally to new tasks and evolving data distributions without catastrophic forgetting. This capability is crucial for deploying AI systems in dynamic real-world applications, including robotics, healthcare, natural language processing, and cybersecurity. This survey provides a comprehensive overview of continual learning, covering its core principles, learning paradigms, and major approaches. We discuss key strategies for mitigating catastrophic forgetting, including replay-based methods, regularization techniques, and dynamic architectures. Additionally, we explore diverse application domains where continual learning plays a crucial role, emphasizing its significance in lifelong learning scenarios. Despite recent advancements, several challenges remain, such as scalability constraints, the lack of standardized benchmarks, and the need for biologically inspired learning mechanisms. We outline open research directions, including memory-efficient learning, adaptive self-supervised techniques, and fairness-aware continual learning. By addressing these challenges, the AI community can develop more robust and flexible models capable of learning continuously over time. Through this survey, we aim to provide a structured foundation for researchers and practitioners interested in continual learning, highlighting its potential to drive the next generation of adaptive AI systems. We discuss the broader impact of continual learning on artificial intelligence and outline future directions to advance the field further.