AI needs highly effective continuous learning

A hallmark of human cognition is the ability for continual, data-efficient learning and knowledge consolidation. Current techniques like RAG, which treat past interactions as a static corpus for retrieval, fail to replicate this dynamic process, limiting agents to a non-evolving, superficial memory. Knowledge graphs, drawing from Semantic Web technologies, provide a robust formalism for representing episodic memory for rich, contextual retrieval. The critical gap, however, is not just in memory representation, but in the autonomous mechanisms for lifelong learning—enabling an agent to generalize and evolve its internal models from that stored experience.

I would love to see more engineering and research (or Common Lisp hacking if you roll that way 😃) directed towards algorithms for building, maintaining, accessing and using episodic memories of agents’ experiences.