Incremental Task

Incremental task learning focuses on developing systems that can continuously learn new tasks without forgetting previously acquired knowledge, a crucial challenge in artificial intelligence and robotics. Current research emphasizes efficient algorithms and model architectures, such as those based on low-rank factorization, knowledge distillation, and prompt engineering, to address the "catastrophic forgetting" problem inherent in sequential learning. These advancements are significant for improving the adaptability and robustness of AI systems in dynamic environments, with applications ranging from personalized recommendations and robotic manipulation to medical diagnosis and process mining. The development of effective incremental learning methods is vital for creating more versatile and efficient AI systems capable of handling real-world complexities.