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The remarkable advancements in artificial intelligence (AI), primarily drivenby deep neural networks, have significantly impacted various aspects of ourlives. However, the current challenges surrounding unsustainable computationaltrajectories, limited robustness, and a lack of explainability call for thedevelopment of next-generation AI systems. Neuro-symbolic AI (NSAI) emerges asa promising paradigm, fusing neural, symbolic, and probabilistic approaches toenhance interpretability, robustness, and trustworthiness while facilitatinglearning from much less data. Recent NSAI systems have demonstrated greatpotential in collaborative human-AI scenarios with reasoning and cognitivecapabilities. In this paper, we provide a systematic review of recent progressin NSAI and analyze the performance characteristics and computational operatorsof NSAI models. Furthermore, we discuss the challenges and potential futuredirections of NSAI from both system and architectural perspectives.

Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI