Neuro-symbolic AI

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Neuro-symbolic AI describes a relatively newer approach to artificial intelligence that combines neural networks and symbolic AI approaches, such as knowledge representation and automated reasoning, to create more robust, more reliable, and more trustworthy AI. This combination allows statistical patterns to be combined with explicitly defined rules and knowledge to give AI systems the ability to better represent, reason and generalize. Thus, neuro-symbolic AI provides a reasoning infrastructure to state-of-the-art machine learning for solving a wider range of problems more effectively. Neuro-symbolic AI recognises the value of deep learning as the “substrate” of AI that provides efficient computational models of learning from data. At the same time, it seeks to address deep learning’s main limitations: lack of reliability, data and energy efficiency, fairness, and trust. Thus, neuro-symbolic AI is argued to leverage the strengths of the two predominant approaches to AI, at the same time...

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