A Field Guide to Memory – Human and Artificial (counterparts.ai)

A new guide has been released exploring the intricacies of memory, both human and artificial, underscoring the limitations of current AI language models, which often operate like they have severe anterograde amnesia—able to process information but incapable of retaining it after interactions. This guide delves into the mechanisms underlying human memory, including the concept of the "engram," the physical trace memories leave in the brain. By comparing these cognitive processes to AI implementations, the guide outlines key areas for advancement in memory retention technologies for AI. Significantly, this detailed investigation into memory mechanisms showcases eleven distinct processes that could bridge the gap between human and AI learning. It emphasizes the convergence of numerous players in the tech space—like Microsoft, Meta, and Snap—racing to develop advanced memory architectures from 2023 to 2026. By mapping patents, research, and innovations, the guide highlights how AI can benefit from understanding biological memory systems, paving the way for more effective long-term memory solutions in language models and other AI systems. This exploration not only adds depth to our understanding of AI capabilities but also presents strategic opportunities for future research and development in the field.