The paper (2023) argues for integrating two historically divergent traditions in artificial intelligence (neural networks and symbolic reasoning) into a unified paradigm called Neurosymbolic AI. It argues that the path to capable, explainable, and trustworthy artificial intelligence lies in marrying perception-driven neural systems with structure-aware symbolic models.
