From Human Memory to AI Continual Learning: How Nested Learning Solves the “Amnesia” Problem in Large Models

If you’ve been following machine learning’s evolution, you’ve probably noticed a strange paradox: while today’s AI systems can write poetry, debug code, and reason through complex problems, they still struggle with something a three-year-old does effortlessly—learning new things without forgetting old ones. It’s like meeting someone who can recite the entire encyclopedia but can’t remember your name five minutes after you meet. Google Research’s recent introduction of Nested Learning, presented at NeurIPS 2025, challenges this fundamental limitation. This isn’t another incremental architecture tweak. It’s a rethinking of how we understand deep learning itself, inspired by how the human brain continually …