I-Corps: A Software Platform to Customize, Inspect and Improve Artificial Intelligence (AI) Systems

I-Corps：用于定制、检查和改进人工智能 (AI) 系统的软件平台

• 批准号: 2341135
• 负责人: Soheil Feizi
• 金额: $ 5万
• 依托单位: University of Maryland, College Park
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2341135&HistoricalAwards=false
• 关键词: Corps; Software; Platform; Customize; Inspect

The broader impact/commercial potential of this I-Corps project is the development of a software platform to make Artificial Intelligence (AI) models more reliable. Artificial intelligence is rapidly becoming a part of everyday businesses and organizations. However, key concerns in using AI systems are their lack of reliability and explainability, and their lack of transparency with respect to internal workings where output inferences and predictions are not interpretable. This makes the process of developing AI models and inspecting and mitigating their failure modes time-consuming and challenging. The proposed technology is designed to automate developing, inspecting and improving AI models using another AI system that uses human feedback in its optimization. Understanding and mitigating reliability issues of AI models may mitigate the risks of their deployment in practice. In addition, these efforts may democratize the reliable use of AI systems by non-experts and increase human trust in these systems. This I-Corps project is based on the development of an automated and unified software platform that provides multi-modal interpretability and reliability analysis and monitoring tools to design, train, inspect, and improve Artificial Intelligence (AI) systems. The proposed technology is designed to automatically uncover and address hidden reliability issues within AI models employing the user’s unique data. It simplifies the complex process of identifying and mitigating potential reliability risks and explainability challenges, which may help to ensure AI models deliver trustworthy and accurate results. In addition, users may compare hundreds of AI models and select the ones with the maximum efficiency and reliability for their specific applications. It also interactively incorporates user feedback in its optimization to improve reliability and explainability of AI models while reliability becomes transparent and manageable, empowering users to make informed decisions with increased confidence.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.

该 I-Corps 项目更广泛的影响/商业潜力是开发一个软件平台，使人工智能 (AI) 模型更加可靠。人工智能正在迅速成为日常企业和组织的一部分。然而，使用人工智能的关键问题。系统缺乏可靠性和可解释性，并且内部工作缺乏透明度，输出推断和预测无法解释，这使得开发人工智能模型以及检查和减轻其故障模式的过程既耗时又具有挑战性。该技术旨在使用另一个在优化中使用人类反馈的人工智能系统来自动开发、检查和改进人工智能模型，了解和减轻人工智能模型的可靠性问题可能会降低其在实践中部署的风险。该 I-Corps 项目基于自动化和统一软件平台的开发，该平台提供多模式可解释性和可靠性，用于设计、培训的可靠分析和监控工具。 、检查、改进人工智能（AI）系统旨在利用用户的独特数据自动发现和解决人工智能模型中隐藏的可靠性问题，它简化了识别和减轻潜在可靠性风险和可解释性挑战的复杂过程，这可能有助于确保安全。此外，用户可以比较数百个人工智能模型，并选择最适合其特定应用的效率和可靠性最高的人工智能模型，并在优化过程中交互式地纳入用户反馈，以提高人工智能模型的可靠性和可解释性。而可靠性则变成透明且易于管理，使用户能够更有信心地做出明智的决策。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。