厘清AI在物理中的应用边界与社会影响，助力科研者理性使用。
What is AI, what is it not, how we use it in physics and how it impacts... you
- 对比机器学习与传统编程差异，明确AI本质
- 总结高能物理中仿真推断、不确定性感知等前沿应用
- 强调物理学家需主动应对AI变革，避免被动依赖
人工智能（AI）与机器学习（ML）在粒子物理领域已应用三十余年，深刻影响高能物理（HEP）分析的多个方面。随着AI影响力持续扩大，物理学家作为研究者与知情公民，亟需批判性审视其基础、误解与社会影响。本文探讨了AI的定义，剖析了机器学习与传统编程的区别，并简要回顾了AI/ML在高能物理中的应用，重点指出仿真推断、不确定性感知机器学习及快速机器学习在异常检测中的前景。此外，文章还讨论了AI系统带来的广泛社会危害，强调负责任参与的重要性。最后，呼吁调整研究实践以适应不断演进的AI环境，确保物理学家不仅受益于最新工具，更能引领创新。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) and Machine Learning (ML) have been prevalent in particle physics for over three decades, shaping many aspects of High Energy Physics (HEP) analyses. As AI's influence grows, it is essential for physicists $\unicode{x2013}$ as both researchers and informed citizens $\unicode{x2013}$ to critically examine its foundations, misconceptions, and impact. This paper explores AI definitions, examines how ML differs from traditional programming, and provides a brief review of AI/ML applications in HEP, highlighting promising trends such as Simulation-Based Inference, uncertainty-aware machine learning, and Fast ML for anomaly detection. Beyond physics, it also addresses the broader societal harms of AI systems, underscoring the need for responsible engagement. Finally, it stresses the importance of adapting research practices to an evolving AI landscape, ensuring that physicists not only benefit from the latest tools but also remain at the forefront of innovation.
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