Continuous Learning

Continuous learning (CL) aims to enable artificial intelligence models to learn new tasks sequentially without forgetting previously acquired knowledge, a challenge known as catastrophic forgetting. Current research focuses on mitigating this forgetting through techniques like knowledge distillation, replay buffers, and regularization, often leveraging pre-trained models (e.g., transformers) and exploring various architectures including spiking neural networks. The development of robust and efficient CL methods holds significant importance for deploying AI in dynamic real-world environments, impacting fields such as robotics, autonomous systems, and personalized medicine where continuous adaptation is crucial.