Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence - Chance, Frances S.; Aimone, James B.; Musuvathy, Srideep S.; Smith, Michael R.; Vineyard, Craig M.; Wang, Felix (Frontiers in Computational Neuroscience, 2020)
While it is notable that early artificial intelligence (AI) works are often described as heavily influenced by neuroscience principles, these principles are often more grounded in what is currently considered cognitive neuroscience or psychology rather than the form or function of neural circuitry derived from neuroscience. Current AI algorithms demonstrate significant power in certain domains; some of these algorithms would seem to foretell a new generation of neural-inspired AI arising from a deeper exploration of neuroscience specifically for AI. This is because, at the same time, neuroscience stands poised to enter a new era of large-scale high-resolution data, and it seems natural to be asking how modern-day neuroscience could or should be leveraged to inform AI. However, interactions between the two fields do not appear to be growing as fast as each of the fields by themselves. From some points of view, the AI and neuroscience communities even seem to be diverging. Here we discuss cultural differences between the two fields, including divergent priorities as well as (at times) conflicting perspectives, that contribute to this separation. We highlight small but significant cultural shifts that we feel would greatly facilitate increased synergy between the two fields.