What is AI, what is it not, how we use it in physics and how it impacts... you
- URL: http://arxiv.org/abs/2504.01827v1
- Date: Wed, 02 Apr 2025 15:35:43 GMT
- Title: What is AI, what is it not, how we use it in physics and how it impacts... you
- Authors: Claire David,
- Abstract summary: Artificial Intelligence (AI) and Machine Learning (ML) have been prevalent in particle physics for over three decades.<n>This paper critically examines its foundations, misconceptions, trends and impact.<n>Beyond physics, it also addresses the broader societal applications of AI.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- What Understanding Means in AI-Laden Astronomy [0.20336617819227906]
Artificial intelligence is rapidly transforming astronomical research.<n>This article argues that philosophy of science offers essential tools for navigating AI's integration into astronomy.<n>We propose "pragmatic understanding" as a framework for integration--recognizing AI as a tool that extends human cognition.
arXiv Detail & Related papers (2026-01-15T03:28:38Z) - AI Needs Physics More Than Physics Needs AI [0.0]
The 2024 Nobel Prizes in Chemistry and Physics recognized AI's potential, but broader assessments indicate the impact to date is often more promotional than technical.<n>We argue that while current AI may influence physics, physics has significantly more to offer this generation of AI.
arXiv Detail & Related papers (2025-12-18T09:31:05Z) - Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI [57.44526951497041]
We advocate for intelligent systems that ground learning in both physical principles and embodied reasoning processes.<n>Our synthesis envisions next-generation world models capable of explaining physical phenomena and predicting future states.
arXiv Detail & Related papers (2025-10-06T16:16:03Z) - The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS) [61.845407777089726]
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS)<n>We present here a summary and snapshot of the MPS community's perspective, as of Spring/Summer 2025.<n>We propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students.
arXiv Detail & Related papers (2025-09-02T18:00:00Z) - Can Theoretical Physics Research Benefit from Language Agents? [50.57057488167844]
Large Language Models (LLMs) are rapidly advancing across diverse domains, yet their application in theoretical physics research is not yet mature.<n>This position paper argues that LLM agents can potentially help accelerate theoretical, computational, and applied physics when properly integrated with domain knowledge and toolbox.<n>We envision future physics-specialized LLMs that could handle multimodal data, propose testable hypotheses, and design experiments.
arXiv Detail & Related papers (2025-06-06T16:20:06Z) - AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research [58.944125758758936]
The Science of Science (SoS) explores the mechanisms underlying scientific discovery.<n>The advent of artificial intelligence (AI) presents a transformative opportunity for the next generation of SoS.<n>We outline the advantages of AI over traditional methods, discuss potential limitations, and propose pathways to overcome them.
arXiv Detail & Related papers (2025-05-17T15:01:33Z) - AI in the Cosmos [0.0]
I highlight examples of AI applications in astrophysics, including source classification, spectral energy distribution modeling, and discuss the achievable advancements through generative AI.<n>The use of AI introduces challenges, including biases, errors, and the "black box" nature of AI models, which must be resolved before their application.<n>These issues can be addressed through the concept of Human-Guided AI (HG-AI), which integrates human expertise and domain-specific knowledge into AI applications.
arXiv Detail & Related papers (2024-12-13T12:30:11Z) - Advancing Perception in Artificial Intelligence through Principles of
Cognitive Science [6.637438611344584]
We focus on the cognitive functions of perception, which is the process of taking signals from one's surroundings as input, and processing them to understand the environment.
We present a collection of methods in AI for researchers to build AI systems inspired by cognitive science.
arXiv Detail & Related papers (2023-10-13T01:21:55Z) - A Survey on Brain-Inspired Deep Learning via Predictive Coding [85.93245078403875]
Predictive coding (PC) has shown promising performance in machine intelligence tasks.
PC can model information processing in different brain areas, can be used in cognitive control and robotics.
arXiv Detail & Related papers (2023-08-15T16:37:16Z) - Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems [268.585904751315]
New area of research known as AI for science (AI4Science)<n>Areas aim at understanding the physical world from subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales.<n>Key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods.
arXiv Detail & Related papers (2023-07-17T12:14:14Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward [60.43248801101935]
This article gives a bird's eye view of the essential scientific tools and approaches informing and supporting the transition from raw EO data to usable EO-based information.
We cover the impact of (i) Computer vision; (ii) Machine learning; (iii) Advanced processing and computing; (iv) Knowledge-based AI; (v) Explainable AI and causal inference; (vi) Physics-aware models; (vii) User-centric approaches; and (viii) the much-needed discussion of ethical and societal issues related to the massive use of ML technologies in EO.
arXiv Detail & Related papers (2023-05-15T07:47:24Z) - Artificial Intelligence in Material Engineering: A review on
applications of AI in Material Engineering [0.0]
High-performance computing has made it possible to test deep learning (DL) models with significant parameters.
generative adversarial networks (GANs) have facilitated the generation of chemical compositions of inorganic materials.
The use of AI to analyze the results from existing analytical instruments is also discussed.
arXiv Detail & Related papers (2022-09-15T04:21:07Z) - Measuring Ethics in AI with AI: A Methodology and Dataset Construction [1.6861004263551447]
We propose to use such newfound capabilities of AI technologies to augment our AI measuring capabilities.
We do so by training a model to classify publications related to ethical issues and concerns.
We highlight the implications of AI metrics, in particular their contribution towards developing trustful and fair AI-based tools and technologies.
arXiv Detail & Related papers (2021-07-26T00:26:12Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Quality Management of Machine Learning Systems [0.0]
Artificial Intelligence (AI) has become a part of our daily lives due to major advances in Machine Learning (ML) techniques.
For business/mission-critical systems, serious concerns about reliability and maintainability of AI applications remain.
This paper presents a view of a holistic quality management framework for ML applications based on the current advances.
arXiv Detail & Related papers (2020-06-16T21:34:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.