Conference: NSF Meta-Workshop on AI to Accelerate Scientific and Engineering Discovery (AI2ASED)

会议：NSF 人工智能加速科学和工程发现元研讨会 (AI2ASED)

• 批准号: 2337647
• 负责人: Shih-Chieh Hsu
• 金额: $ 10万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2337647&HistoricalAwards=false
• 关键词: Conference; NSF; Meta; Workshop; AI

In recent years, foundational research in artificial intelligence (AI), machine learning (ML) and data science, have drawn incredible interest. Some of the research has focused on using these data-driven approaches to enhance scientific and engineering discoveries in other disciplines. While workshops and meetings have been held around the world by the to discuss cutting-edge breakthroughs and emerging trends, the majority of events are structured for siloed scientific fields. A paradigm-shifting scientific revolution might be made possible by integrating important discoveries across these events (i.e., a meta-workshop) to develop cogent novel insights. This project supports a meta-workshop on AI to accelerate science and engineering discovery. The workshop will provide the scientific community with information on the state of complementary research in AI, machine learning and other data analytic approaches across the science and engineering domains. The topics of interest include identifying specific subject areas with high potential for data-driven approaches to discovery.This meta-workshop will bring together leaders of AI, ML, data science and science and engineering (e.g., biology, biological engineering, chemistry, chemical engineering, physics, material science, etc). The majority of attendees will be core organizers from recent workshops and meetings around the heart of data-intensive discoveries. They will include the most important findings from their events and collaborate with other participants to generate new insights. The ultimate goal of the meta-workshop is to work with the leaders of diverse disciplines to develop a coherent and far-reaching research program agenda. Data-intensive AI/ML techniques with broad applicability across domains can lead to transformative discovery in domain sciences and engineering and address the biggest global societal challenges (such as promoting human health, using renewable energy, containing and managing climate change, feeding the expanding population without damaging the environment, and establishing a just and sustainable society). For the economic growth and general well-being of our nation, this workshop will also target ways to create the industries and workforce of the future.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

近年来，人工智能（AI）、机器学习（ML）和数据科学的基础研究引起了人们极大的兴趣。一些研究集中在使用这些数据驱动的方法来增强其他学科的科学和工程发现。虽然研讨会和会议在世界各地举行，讨论前沿突破和新兴趋势，但大多数活动都是针对孤立的科学领域。通过整合这些事件中的重要发现（即，一个元研讨会），以发展令人信服的新见解。该项目支持人工智能元研讨会，以加速科学和工程发现。该研讨会将为科学界提供有关人工智能，机器学习和其他科学和工程领域数据分析方法的补充研究状况的信息。感兴趣的主题包括确定具有数据驱动发现方法高潜力的特定主题领域。这个元研讨会将汇集人工智能，机器学习，数据科学和科学与工程的领导者（例如，生物学、生物工程、化学、化学工程、物理学、材料科学等）。大多数与会者将是最近围绕数据密集型发现核心的研讨会和会议的核心组织者。他们将包括他们的活动中最重要的发现，并与其他参与者合作，以产生新的见解。元研讨会的最终目标是与不同学科的领导者合作，制定一个连贯和深远的研究计划议程。数据密集型AI/ML技术在各个领域具有广泛的适用性，可以在领域科学和工程领域带来变革性的发现，并解决最大的全球社会挑战（例如促进人类健康，使用可再生能源，遏制和管理气候变化，在不破坏环境的情况下养活不断增长的人口，以及建立公正和可持续的社会）。为了我们国家的经济增长和整体福祉，该研讨会还将着眼于创造未来产业和劳动力的方法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。