The remarkable advancements in artificial intelligence (AI), primarily driven
by deep neural networks, have significantly impacted various aspects of our
lives. However, the current challenges surrounding unsustainable computational
trajectories, limited robustness, and a lack of explainability call for the
development of next-generation AI systems. Neuro-symbolic AI (NSAI) emerges as
a promising paradigm, fusing neural, symbolic, and probabilistic approaches to
enhance interpretability, robustness, and trustworthiness while facilitating
learning from much less data. Recent NSAI systems have demonstrated great
potential in collaborative human-AI scenarios with reasoning and cognitive
capabilities. In this paper, we provide a systematic review of recent progress
in NSAI and analyze the performance characteristics and computational operators
of NSAI models. Furthermore, we discuss the challenges and potential future
directions of NSAI from both system and architectural perspectives.

人工智能的显著进步主要是由深度神经网络所推动，然而不可持续的计算轨迹、有限的鲁棒性和缺乏解释性的问题要求开发下一代人工智能系统。神经符号人工智能 (NSAI) 作为一种有潜力的范式，将神经、符号和概率方法融合在一起，提高可解释性、鲁棒性和可信度，并能够从较少的数据中进行学习。最近的 NSAI 系统在具有推理和认知能力的协作人工智能场景中展示了巨大的潜力。在本文中，我们对 NSAI 的最新进展进行了系统综述，并分析了 NSAI 模型的性能特征和计算操作符。此外，我们还从系统和架构的角度讨论了 NSAI 面临的挑战和潜在的未来方向。