Physics-driven human-like working memory outperforms digital networks in dynamic vision

Researchers have developed a physics-driven AI system called Intrinsic Plasticity Network (IPNet) that uses magnetic tunnel junctions to create human-like working memory. The system demonstrates 18x error reduction in dynamic vision tasks while reducing memory-energy overhead by over 90,000x compared to traditional digital AI systems.