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
基本信息
- 批准号:2341135
- 负责人:
- 金额:$ 5万
- 依托单位:
- 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:
- 关键词:
项目摘要
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.
这个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业务和组织的一部分。然而,使用人工智能系统的关键问题是它们缺乏可靠性和可解释性,以及它们在输出推断和预测不可解释的内部工作方面缺乏透明度。 这使得开发人工智能模型以及检查和缓解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人工智能系统自动开发,检查和改进人工智能模型,该系统在优化过程中使用人类反馈。 理解和缓解AI模型的可靠性问题可能会降低其在实践中部署的风险。此外,这些努力可能会使非专家对人工智能系统的可靠使用民主化,并增加人类对这些系统的信任。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分析和监控工具,以设计,培训,检查和改进人工智能(AI)系统。该技术旨在自动发现和解决使用用户独特数据的AI模型中隐藏的可靠性问题。 它简化了识别和缓解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AI模型提供可靠和准确的结果。 此外,用户可以比较数百个AI模型,并选择具有最高效率和可靠性的模型。 它还以交互方式将用户反馈纳入其优化中,以提高人工智能模型的可靠性和可解释性,同时可靠性变得透明和可管理,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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Soheil Feizi其他文献
Soheil Feizi的其他文献
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{{ truncateString('Soheil Feizi', 18)}}的其他基金
Collaborative Research: CIF: Medium: Understanding Robustness via Parsimonious Structures.
合作研究:CIF:中:通过简约结构了解鲁棒性。
- 批准号:
2212458 - 财政年份:2022
- 资助金额:
$ 5万 - 项目类别:
Standard Grant
CAREER: Information-Theoretic and Statistical Foundations of Generative Models
职业:生成模型的信息理论和统计基础
- 批准号:
1942230 - 财政年份:2020
- 资助金额:
$ 5万 - 项目类别:
Continuing Grant
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