Frameworks: Garden: A FAIR Framework for Publishing and Applying AI Models for Translational Research in Science, Engineering, Education, and Industry

框架：Garden：用于发布和应用人工智能模型进行科学、工程、教育和工业转化研究的公平框架

• 批准号: 2209892
• 负责人: Ian Foster
• 金额: $ 349.65万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209892&HistoricalAwards=false
• 关键词: Frameworks; Garden; FAIR; Framework; Publishing

Harnessing powerful new advances in machine learning (ML) and artificial intelligence (AI) is key to 1) maintaining and building national competitiveness in the sciences and engineering, 2) realizing breakthroughs in health and medicine, 3) enabling the creation of industries of the future, and 4) increasing economic growth and opportunity. Today, researchers are achieving exciting results with these new ML/AI methods in applications ranging from materials discovery, chemistry, and drug discovery to high energy physics, weather prediction, advanced manufacturing, and health. Yet, much work remains. These new methods and results are not easily applied by others due to the specialized expertise and resources needed to understand, develop, share, adapt, test, deploy, and run the resulting ML/AI models. To overcome these barriers to progress, this project seeks to develop methods and tools for constructing and creating Model Gardens, collections of curated and tested ML/AI models linked with the data and computing resources required to advance the work of a specific research community. Such new methods, software, and tools can make it simple for model producers to publish models in forms that are easily consumed by others, and for model consumers to discover published models and integrate them into their applications in academia or industry. The project connects researchers in materials science, physics, and chemistry enabling the establishment of Model Gardens for their communities and empowering key research centers to collect and provide broad access to new methods and models resulting from their work. Further, the project facilitates the connection of aspiring researchers with scientific problems, engaging hundreds of students from diverse backgrounds (including rural community college partners) in learning and contributing to software development, model publication, development of new AI/ML applications, and training of a next-generation ML/AI-empowered workforce through hosted workshops, open office hours, and development of a new engagement platform.This project overcomes the barriers to the dissemination and application of new ML/AI methods by creating a new CSSI framework—the Garden Framework to support the construction and operation of Model Gardens: collections of curated models linked with the data and computing resources required to advance the work of specific communities. By reducing the friction associated with model publication, discovery, access, and deployment; providing for the disciplined and structured organization and linking of data, models, and code; associating appropriate metadata with models to promote reuse and discoverability, and applying quality assessment measures (e.g., automated testing, uncertainty quantification) to support model comparison; supporting the development of communities around specific model classes and research challenges; and permitting easy access to models without (and with) download and installation, established Model Gardens reduce barriers to the use of ML/AI methods and promote the nucleation of communities around specific datasets, methods, and models.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.

利用机器学习 (ML) 和人工智能 (AI) 领域的强大新进展对于 1) 保持和建设科学和工程领域的国家竞争力，2) 实现健康和医学领域的突破，3) 促进创建全球产业至关重要。 4) 不断增长的经济增长和机遇。如今，研究人员利用这些新的 ML/AI 方法在材料发现、化学和药物发现、高能物理、天气预报、先进制造和健康等领域取得了令人兴奋的成果。然而，还有很多工作要做。由于理解、开发、共享、调整、测试、部署和运行生成的 ML/AI 模型所需的专业知识和资源，这些新方法和结果不易被其他人应用。为了克服这些进展障碍，该项目。致力于开发用于构建和创建模型花园的方法和工具，模型花园是经过精心策划和测试的 ML/AI 模型的集合，与推进特定研究社区的工作所需的数据和计算资源相关联，这样的新方法、软件和工具可以实现。模型制作者可以轻松地以易于使用的形式发布模型该项目将材料科学、物理和化学领域的研究人员联系起来，为他们的社区建立模型花园，并授权关键研究中心此外，该项目还促进了有抱负的研究人员与科学问题的联系，让来自不同背景的数百名学生（包括农村社区大学合作伙伴）参与学习并为软件开发做出贡献。 、模型发布、新AI/ML开发通过举办研讨会、开放办公时间和开发新的参与平台，培训下一代 ML/AI 赋能的劳动力。该项目通过创建一个新的CSSI框架——花园框架，支持模型花园的建设和运营：通过减少与模型发布、发现、访问相关的摩擦，与推进特定社区工作所需的数据和计算资源相关联的策划模型集合。和部署；数据、模型和代码的结构化组织和链接；将适当的元数据与模型相关联，以促进重用和可发现性，并应用质量评估措施（例如自动化测试、不确定性量化）来支持模型比较；课程和研究挑战；并允许在不下载和安装的情况下轻松访问模型，建立的模型花园减少了使用 ML/AI 方法的障碍，并促进围绕特定数据集、方法和模型的社区的形成。授予 NSF 的法定使命，并通过评估反映使用基金会的智力优点和更广泛的影响审查标准，被认为值得支持。