AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE)

环境中具有计算学习功能的智能网络基础设施人工智能研究所 (ICICLE)

• 批准号: 2112606
• 负责人: Dhabaleswar Panda
• 金额: $ 2000万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-11-01 至 2026-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112606&HistoricalAwards=false
• 关键词: AI; Institute; Intelligent; CyberInfrastructure; Computational

Although the world is witness to the tremendous successes of Artificial Intelligence (AI) technologies in some domains, many domains have yet to reap the benefits of AI due to the lack of easily usable AI infrastructure. The NSF AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE) will develop intelligent cyberinfrastructure with transparent and high-performance execution on diverse and heterogeneous environments. It will advance plug-and-play AI that is easy to use by scientists across a wide range of domains, promoting the democratization of AI. ICICLE brings together a multidisciplinary team of scientists and engineers, led by The Ohio State University in partnership with Case Western Reserve University, IC-FOODS, Indiana University, Iowa State University, Ohio Supercomputer Center, Rensselaer Polytechnic Institute, San Diego Supercomputer Center, Texas Advanced Computing Center, University of Utah, University of California-Davis, University of California-San Diego, University of Delaware, and University of Wisconsin-Madison. Initially, complex societal challenges in three use-inspired scientific domains will drive ICICLE’s research and workforce development agenda: Smart Foodsheds, Precision Agriculture, and Animal Ecology. ICICLE’s research and development includes: (i) Empowering plug-and-play AI by advancing five foundational areas: knowledge graphs, model commons, adaptive AI, federated learning, and conversational AI. (ii) Providing a robust cyberinfrastructure capable of propelling AI-driven science (CI4AI), solving the challenges arising from heterogeneity in applications, software, and hardware, and disseminating the CI4AI innovations to use-inspired science domains. (iii) Creating new AI techniques for the adaptation/optimization of various CI components (AI4CI), enabling a virtuous cycle to advance both AI and CI. (iv) Developing novel techniques to address cross-cutting issues including privacy, accountability, and data integrity for CI and AI; and (v) Providing a geographically distributed and heterogeneous system consisting of software, data, and applications, orchestrated by a common application programming interface and execution middleware. ICICLE’s advanced and integrated edge, cloud, and high-performance computing hardware and software CI components simplify the use of AI, making it easier to address new areas of inquiry. In this way, ICICLE focuses on research in AI, innovation through AI, and accelerates the application of AI. ICICLE is building a diverse STEM workforce through innovative approaches to education, training, and broadening participation in computing that ensure sustained measurable outcomes and impact on a national scale, along the pipeline from middle/high school students to practitioners. As a nexus of collaboration, ICICLE promotes technology transfer to industry and other stakeholders, as well as data sharing and coordination across other National Science Foundation AI Institutes and Federal agencies. As a national resource for research, development, technology transfer, workforce development, and education, ICICLE is creating a widely usable, smarter, more robust and diverse, resilient, and effective CI4AI and AI4CI ecosystem.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.

尽管世界见证了人工智能（AI）技术在某些领域的巨大成功，但由于缺乏易于使用的AI基础设施，许多领域尚未获得AI的好处。NSF AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment（ICICLE）将开发智能网络基础设施，在多样化和异构环境中具有透明和高性能的执行。它将推动即插即用的人工智能，使科学家在广泛的领域中易于使用，促进人工智能的民主化。ICICLE汇集了一支由科学家和工程师组成的多学科团队，由俄亥俄州州立大学领导，与凯斯西储大学、IC-FOODS、印第安纳州大学、爱荷华州州立大学、俄亥俄州超级计算机中心、伦斯勒理工学院、圣地亚哥超级计算机中心、德克萨斯州高级计算中心、犹他州大学、加州大学戴维斯分校、加州大学圣地亚哥分校、特拉华州大学、威斯康星大学麦迪逊分校最初，三个以使用为灵感的科学领域的复杂社会挑战将推动ICICLE的研究和劳动力发展议程：智能粮仓，精准农业和动物生态学。 ICICLE的研究和开发包括：（i）通过推进五个基础领域来增强即插即用AI：知识图，模型共享，自适应AI，联邦学习和会话AI。(ii)提供一个强大的网络基础设施，能够推动人工智能驱动的科学（CI 4AI），解决应用程序，软件和硬件的异质性带来的挑战，并将CI 4AI创新传播到使用启发的科学领域。(iii)创建新的人工智能技术，用于适应/优化各种CI组件（AI 4CI），实现良性循环，以促进人工智能和CI。(iv)开发新的技术来解决跨领域的问题，包括CI和AI的隐私，问责制和数据完整性;以及（v）提供一个由软件，数据和应用程序组成的地理分布和异构系统，由通用的应用程序编程接口和执行中间件编排。ICICLE的先进和集成的边缘，云和高性能计算硬件和软件CI组件简化了AI的使用，使其更容易解决新的查询领域。通过这种方式，ICICLE专注于AI的研究，通过AI进行创新，并加速AI的应用。ICLE正在通过创新的教育方法建立一支多元化的STEM劳动力队伍，培训和扩大对计算的参与，确保在全国范围内，沿着从初中/高中学生到从业者的管道，取得可持续的可衡量成果和影响。作为合作的纽带，ICICLE促进向行业和其他利益相关者的技术转让，以及其他国家科学基金会人工智能研究所和联邦机构之间的数据共享和协调。作为研究、开发、技术转让、劳动力发展和教育的国家资源，ICICLE正在创建一个广泛可用、更智能、更强大、更多样化、更有弹性和更有效的CI 4AI和AI 4CI生态系统。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Learning Fractals by Gradient Descent

通过梯度下降学习分形

• DOI: 10.1609/aaai.v37i2.25342
• 发表时间: 2023
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Tu, Cheng-Hao;Chen, Hong-You;Carlyn, David;Chao, Wei-Lun
• 通讯作者: Chao, Wei-Lun

“Hey CAI” - Conversational AI Enabled User Interface for HPC Tools

–Hey CAI – 适用于 HPC 工具的对话式 AI 用户界面

• DOI: 10.1007/978-3-031-07312-0_5
• 发表时间: 2022
• 期刊: Proceedings International Conference on High Performance Computing
• 作者: Kousha, P.;Jain, A.;Kolli, A.;Prasanna, S.;Miriyala, S.;Subramoni, H.;Shafi, A.;Panda, DK.
• 通讯作者: Panda, DK.

Accelerating MPI All-to-All Communication with Online Compression on Modern GPU Clusters

在现代 GPU 集群上通过在线压缩加速 MPI 全面通信

• DOI: 10.1007/978-3-031-07312-0_1
• 发表时间: 2022
• 期刊: ISC HIGH PERFORMANCE
• 作者: Zhou, Q.;Kousha, P.;Anthony, Q.;Khorassani, K.;Shafi, A.;Subramoni, H.;Panda, DK.
• 通讯作者: Panda, DK.

Text-to-SQL Error Correction with Language Models of Code

• DOI: 10.48550/arxiv.2305.13073
• 发表时间: 2023-05
• 作者: Ziru Chen;Shijie Chen;Michael White;R. Mooney;Ali Payani;Jayanth Srinivasa;Yu Su;Huan Sun

An Interactive Knowledge and Learning Environment in Smart Foodsheds

• DOI: 10.1109/mcg.2023.3263960
• 发表时间: 2023-04
• 期刊: IEEE Computer Graphics and Applications
• 影响因子: 1.8
• 作者: Yamei Tu;Xiaoqi Wang;Rui Qiu;Han-Wei Shen;Michelle Miller;Jinmeng Rao;Song Gao;P. Huber