Enabling AI innovation via data and model sharing: An overview of the NSF Convergence Accelerator Track D
Enabling AI innovation via data and model sharing: An overview of the NSF Convergence Accelerator Track D
复制标题
DOI：
10.1002/aaai.12042
复制
发表时间：
2022-03-01
期刊：
影响因子：
0.9
通讯作者：
Zhang, Peng
中科院分区：
文献类型：
--
作者：
Baru, Chaitanya;Pozmantier, Michael;Zhang, Peng
本文简要概述了美国国家科学基金会(NSF)融合加速器(CA)计划2020年队列中由Track D资助的18个项目--数据和模型共享，以实现人工智能创新。NSF CA专注于将研究转化为实践，以产生社会影响。这里描述的项目是从2020年9月开始的项目第一阶段，为期一年的资助。他们的重点是提供工具、技术和技术来帮助共享数据，以及支持人工智能创新的数据驱动模型。资助的努力涵盖了广泛的领域，从医疗保健和医药，到气候变化和灾难，以及民用/建造的基础设施。这些项目正在解决共享公开和敏感/私人数据的问题。如本文所述，2021年9月，这里描述的18个项目中有6个项目被选为该计划的第二阶段。
This article provides a brief overview of 18 projects funded in Track D-Data and Model Sharing to Enable AI Innovation-of the 2020 Cohort of the National Science Foundation's (NSF) Convergence Accelerator (CA) program. The NSF CA is focused on transitioning research to practice for societal impact. The projects described here were funded for one year in phase I of the program, beginning September 2020. Their focus is on delivering tools, technologies, and techniques to assist in sharing data as well as data-driven models to enable AI innovation. A broad range of domain areas is covered by the funded efforts, spanning across healthcare and medicine, to climate change and disaster, and civil/built infrastructure. The projects are addressing sharing of open as well as sensitive/private data. In September 2021, six of the eighteen projects described here were selected for phase II of the program, as noted in this article.