Artificial Intelligence for the Electron Ion Collider (AI4EIC)
- URL: http://arxiv.org/abs/2307.08593v1
- Date: Mon, 17 Jul 2023 16:03:35 GMT
- Title: Artificial Intelligence for the Electron Ion Collider (AI4EIC)
- Authors: C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, M.
Battaglieri, J. Bernauer, M. Bond\`i, N. Branson, T. Britton, A. Butter, I.
Chahrour, P. Chatagnon, E. Cisbani, E. W. Cline, S. Dash, C. Dean, W.
Deconinck, A. Deshpande, M. Diefenthaler, R. Ent, C. Fanelli, M. Finger, M.
Finger, Jr., E. Fol, S. Furletov, Y. Gao, J. Giroux, N. C. Gunawardhana
Waduge, R. Harish, O. Hassan, P. L. Hegde, R. J. Hern\'andez-Pinto, A. Hiller
Blin, T. Horn, J. Huang, D. Jayakodige, B. Joo, M. Junaid, P. Karande, B.
Kriesten, R. Kunnawalkam Elayavalli, M. Lin, F. Liu, S. Liuti, G. Matousek,
M. McEneaney, D. McSpadden, T. Menzo, T. Miceli, V. Mikuni, R. Montgomery, B.
Nachman, R. R. Nair, J. Niestroy, S. A. Ochoa Oregon, J. Oleniacz, J. D.
Osborn, C. Paudel, C. Pecar, C. Peng, G. N. Perdue, W. Phelps, M. L.
Purschke, K. Rajput, Y. Ren, D. F. Renteria-Estrada, D. Richford, B. J. Roy,
D. Roy, N. Sato, T. Satogata, G. Sborlini, M. Schram, D. Shih, J. Singh, R.
Singh, A. Siodmok, P. Stone, J. Stevens, L. Suarez, K. Suresh, A.-N. Tawfik,
F. Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, R. Tyson, S.
Volkova, A. Vossen, E. Walter, D. Whiteson, M. Williams, S. Wu, N. Zachariou,
P. Zurita
- Abstract summary: Second annual workshop organized by the AI4EIC working group centered on exploring all current and prospective application areas of AI for the EIC.
This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the
strong force, is expected to begin commissioning its first experiments in 2028.
This is an opportune time for artificial intelligence (AI) to be included from
the start at this facility and in all phases that lead up to the experiments.
The second annual workshop organized by the AI4EIC working group, which
recently took place, centered on exploring all current and prospective
application areas of AI for the EIC. This workshop is not only beneficial for
the EIC, but also provides valuable insights for the newly established ePIC
collaboration at EIC. This paper summarizes the different activities and R&D
projects covered across the sessions of the workshop and provides an overview
of the goals, approaches and strategies regarding AI/ML in the EIC community,
as well as cutting-edge techniques currently studied in other experiments.
Related papers
- Nuclear Medicine Artificial Intelligence in Action: The Bethesda Report (AI Summit 2024) [11.935724441133708]
The 2nd SNMMI Artificial Intelligence (AI) Summit took place in Bethesda, MD, on February 29 - March 1, 2024.
Bringing together various community members and stakeholders, the summit theme was: AI in Action.
Six key topics included (i) an overview of prior and ongoing efforts by the AI task force, (ii) emerging needs and tools for computational nuclear oncology, (iii) new frontiers in large language and generative models, (iv) defining the value proposition for the use of AI in nuclear medicine, (v) open science including efforts for data and model repositories, and (vi) issues
arXiv Detail & Related papers (2024-06-03T06:54:38Z) - The Key Artificial Intelligence Technologies in Early Childhood
Education: A Review [0.0]
Artificial Intelligence (AI) technologies have been applied in various domains, including early childhood education (ECE)
This paper provides an up-to-date and in-depth overview of the key AI technologies in ECE.
We mainly discuss the studies that apply AI-based robots and AI technologies to ECE, including improving the social interaction of children with an autism spectrum disorder.
arXiv Detail & Related papers (2023-12-20T10:36:39Z) - The Participatory Turn in AI Design: Theoretical Foundations and the
Current State of Practice [64.29355073494125]
This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation.
We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
arXiv Detail & Related papers (2023-10-02T05:30:42Z) - FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare [73.78776682247187]
Concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI.
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
arXiv Detail & Related papers (2023-08-11T10:49:05Z) - An Uncommon Task: Participatory Design in Legal AI [64.54460979588075]
We examine a notable yet understudied AI design process in the legal domain that took place over a decade ago.
We show how an interactive simulation methodology allowed computer scientists and lawyers to become co-designers.
arXiv Detail & Related papers (2022-03-08T15:46:52Z) - Trust in AI and Implications for the AEC Research: A Literature Analysis [0.0]
The architecture, engineering, and construction (AEC) research community has been harnessing advanced solutions offered by artificial intelligence (AI) to improve project.
Despite the unique characteristics of work, workers, and workplaces in the AEC industry, the concept of trust in AI has received very little attention in the literature.
This paper presents a comprehensive analysis of the academic literature in two main areas of trust in AI and AI in the AEC, to explore the interplay between AEC projects unique aspects and the sociotechnical concepts that lead to trust in AI.
arXiv Detail & Related papers (2022-03-08T04:38:34Z) - Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and
Stir" [76.44130385507894]
This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices.
Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
arXiv Detail & Related papers (2021-11-01T17:57:04Z) - Explaining decisions made with AI: A workbook (Use case 1: AI-assisted
recruitment tool) [0.0]
The Alan Turing Institute and the Information Commissioner's Office have been working together to tackle the difficult issues surrounding explainable AI.
The ultimate product of this joint endeavour, Explaining decisions made with AI, published in May 2020, is the most comprehensive practical guidance on AI explanation produced anywhere to date.
The goal of the workbook is to summarise some of main themes from Explaining decisions made with AI and then to provide the materials for a workshop exercise.
arXiv Detail & Related papers (2021-03-20T17:03:50Z) - Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations.
Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
arXiv Detail & Related papers (2021-01-15T10:43:10Z) - EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies
on Signal Sensing Technologies and Computational Intelligence Approaches and
their Applications [65.32004302942218]
Brain-Computer Interface (BCI) is a powerful communication tool between users and systems.
Recent technological advances have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications.
arXiv Detail & Related papers (2020-01-28T10:36:26Z)
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.