Knowledge Retention

Knowledge retention, the ability of systems (biological or artificial) to remember previously learned information without catastrophic forgetting, is a central challenge in both neuroscience and artificial intelligence. Current research focuses on mitigating forgetting in continual learning settings, employing techniques like experience replay, robust feature distillation, and adaptive sampling policies within various model architectures, including large language models and reinforcement learning agents. These advancements are crucial for developing more robust and efficient AI systems and for gaining a deeper understanding of human memory mechanisms, with implications for fields ranging from education to personalized medicine.