Structured Intelligence: Merging Neural and Symbolic AI

In order to close the gap between statistical learning and explicit knowledge representation, a new interdisciplinary field called neuro-symbolic AI combines deep learning with symbolic reasoning. Symbolic reasoning offers structured decision-making and logical inference, while deep learning excels at pattern recognition and feature extraction. Key architectures, hybrid models, and differentiable symbolic reasoning techniques are all covered in this paper’s thorough analysis of recent developments in neuro-symbolic AI. We investigate its uses in a number of fields, such as automated reasoning, natural language processing, robotics, and explainable AI. We also examine issues like scalability, interpretability, and knowledge transfer that arise when combining neural and symbolic approaches. We conclude by outlining possible avenues for future research, emphasizing the necessity of enhancing neuro-symbolic AI systems’ generalization, resilience, and effectiveness.