What is AI, what is it not, how we use it in physics and how it impacts... you
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects
Evaluating Language Models For Threat Detection in IoT Security Logs
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series
E-ABIN: an Explainable module for Anomaly detection in BIological Networks
Exact Matrix Seriation through Mathematical Optimization: Stress and Effectiveness-Based Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.