National Science Foundation Workshop Outlines Neuroscience-Inspired AI Research Roadmap

A research paper published on arXiv identifies three critical shortcomings in contemporary artificial intelligence systems, based on findings from a National Science Foundation workshop held in August 2025. Current AI lacks the ability to effectively interact with the physical world, demonstrates brittle learning patterns, and suffers from unsustainable energy and data inefficiency. The paper proposes bridging neuroscience and AI by applying biological principles to address these gaps. Key neuroscience concepts recommended for integration include the co-design of body and controller, prediction through interaction, and multi-scale learning with neuromodulatory control. Hierarchical distributed architectures and sparse event-driven computation are also highlighted as essential approaches. The authors present a detailed research timeline with near-term, mid-term, and long-term objectives. They emphasize that achieving this vision requires training a new generation of interdisciplinary researchers who can work across neuroscience and engineering boundaries. The institution responsible for fostering this cross-disciplinary training is described as a crucial component of the proposed roadmap.