  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.
