What is AI, what is it not, how we use it in physics and how it impacts... you
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Abstract (arXiv)
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 unicodex2013 as both researchers and informed citizens unicodex2013 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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 13 references that are in the map. First posted on arXiv 2 Apr 2025.
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2 of 13 references.
References · 2
- 2021A Living Review of Machine Learning for Particle Physicscited by 78
- 2019MadMiner: Machine learning-based inference for particle physicscited by 32
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