Continual Learning
Summary
Continual Learning is a critical challenge in artificial intelligence, focusing on the ability of models to learn and adapt to new tasks sequentially without forgetting previously acquired knowledge. This area of research addresses the problem of catastrophic forgetting, where neural networks tend to overwrite old information when learning new tasks. Recent advancements in this field include meta-learning approaches, such as the Neuromodulated Meta-Learning Algorithm (ANML), which draws inspiration from biological neuromodulatory processes. ANML employs an activation-gating function that enables context-dependent selective activation within neural networks, allowing for effective continual learning without catastrophic forgetting. This approach has demonstrated state-of-the-art performance, successfully learning hundreds of classes over thousands of updates, representing a significant step forward in developing AI systems capable of lifelong learning and adaptation.