What is AI, what is it not, how we use it in physics and how it impacts... you

2025-04-02

AI Summary

This paper addresses three critical challenges in high-energy physics (HEP): widespread conceptual ambiguity regarding artificial intelligence (AI), fragmented adoption of AI/ML techniques, and the absence of ethical frameworks. Methodologically, it provides a systematic review of foundational AI/ML concepts and persistent misconceptions, traces thirty years of AI evolution in particle physics, and introduces a cross-paradigm analytical framework integrating supervised/unsupervised learning, generative modeling, Bayesian inference, explainable AI (XAI), and simulation-driven learning—with emphasis on simulation-based inference, uncertainty-aware learning, and real-time anomaly detection. Its primary contribution is the first comprehensive AI cognition framework for physicists, unifying technical evolution, methodological reflection, and societal responsibility; it proposes a novel HEP research paradigm aligned with rapid AI advancement. The work culminates in an AI literacy guide for the physics community, advocating uncertainty quantification and responsible AI as standard practice, while furnishing empirical foundations from physics for interdisciplinary AI governance.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Philosophy and Ethics of AI: AI & Epistemology
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

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.

Problem

• Defining AI and distinguishing it from traditional programming methods
• Reviewing AI/ML applications in High Energy Physics analyses
• Addressing societal impacts and promoting responsible AI engagement

Innovation

• Simulation-Based Inference in HEP
• Uncertainty-aware machine learning
• Fast ML for anomaly detection