Zuoning Chen

Shiqiang Zhu, Ting Yu, Tao Xu et al.

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners.

Yifan Zhang, Xinkui Zhao, Ziying Li et al.

Heterogeneous hardware and dynamic workloads worsen long-standing OS bottlenecks in scalability, adaptability, and manageability. At the same time, advances in machine learning (ML), large language models (LLMs), and agent-based methods enable automation and self-optimization, but current efforts lack a unifying view. This survey reviews techniques, architectures, applications, challenges, and future directions at the AI-OS intersection. We chart the shift from heuristic- and rule-based designs to AI-enhanced systems, outlining the strengths of ML, LLMs, and agents across the OS stack. We summarize progress in AI for OS (core components and the wider ecosystem) and in OS for AI (component- and architecture-level support for short- and long-context inference, distributed training, and edge inference). For practice, we consolidate evaluation dimensions, methodological pipelines, and patterns that balance real-time constraints with predictive accuracy. We identify key challenges, such as complexity, overhead, model drift, limited explainability, and privacy and safety risks, and recommend modular, AI-ready kernel interfaces; unified toolchains and benchmarks; hybrid rules-plus-AI decisions with guardrails; and verifiable in-kernel inference. Finally, we propose a three-stage roadmap including AI-powered, AI-refactored, and AI-driven OSs, to bridge prototypes and production and to enable scalable, reliable AI deployment.