AI Institute for Learning-Enabled Optimization at Scale (TILOS)

AI 大规模学习优化研究所 (TILOS)

• 批准号: 2112665
• 负责人: Yusu Wang
• 金额: $ 2000万
• 依托单位: University of California-San Diego
• 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=2112665&HistoricalAwards=false
• 关键词: AI; Institute; Learning; Enabled; Optimization

Improved optimizations of energy-efficiency, safety, robustness, and other criteria in engineered systems offer the promise of incalculable societal benefits. However, challenges of scale and complexity keep many real-world optimization needs beyond our reach. The mission of The National Artificial Intelligence (AI) Institute for Learning-enabled Optimization at Scale (TILOS) is to make impossible optimizations possible, at scale and in practice. The institute (a partnership of University of California, San Diego, Massachusetts Institute of Technology, National University, University of Pennsylvania, University of Texas at Austin and Yale University) will pioneer learning-enabled optimizations that transform chip design, robotics, communication networks, and other use domains that are vital to our nation’s health, prosperity and welfare. In TILOS, research, education, outreach and translation are holistically driven by what makes the nexus of AI/ML and optimization uniquely challenging at the leading edge of practice. Industry partners will interact closely with TILOS on both foundational research and its use-domain application. TILOS will build an openly accessible program of continuing education with long-term, lifelong learning and skills renewal as its central tenet. This institute will also broaden participation, building on the visible successes at its partner institutions that have reached underserved demographics from K-12 onward. Through these efforts, TILOS will discover, educate, and translate into real-world practice a new nexus of AI, optimization, and use. TILOS is organized around multiple virtuous cycles that unify AI and optimization, use domains, and the translation of AI-optimization breakthroughs into practice. A first virtuous cycle of AI and optimization, where each enables and amplifies the other, is at the heart of TILOS. Foundational research will pursue five main pillars: (i) bridging discrete and continuous optimization; (ii) distributed, parallel, and federated optimization; (iii) optimization on manifolds; (iv) dynamic decisions under uncertainty; and (v) nonconvex optimization in deep learning. A second virtuous cycle of challenges, inspirations and data-enabled validations connects the foundational research in AI-optimization with use-domain expertise. The initial use-domain foci bring diverse optimization challenges but inspire shared solutions with commonalities such as physical embeddedness, hierarchical-system context, underlying graphical models, safety and robustness as first-class concerns, and the bridging of human-guided and autonomous systems. A third virtuous cycle is one of translation and ever-tighter connections to the leading edge of practice. TILOS will leverage industry partnerships to accelerate impact via open standards, data sets and “data virtual reality”, and open source that democratize access to research enablement. Roadmaps of optimization formulations and progress metrics will draw researchers together and toward shared research goals. A fourth virtuous cycle with industry and the institutional partners spans both workforce development and the broadening of participation. Workforce development will identify and teach the skills and mindsets needed at the nexus of learning, optimization and practice, so as to provide skills renewal for the existing workforce as well as onramps for underserved demographics such as veterans or those seeing a career change. Broadening of participation will be pursued via the institute’s partnerships with community organizations and middle and high school educators, via tiers of engagement that span exposure, experience and environment.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.

工程系统中能源效率、安全性、稳健性和其他标准的改进有望带来不可估量的社会效益，然而，规模和复杂性的挑战使许多现实世界的优化需求超出了我们的能力。 (AI) 大规模学习优化研究所 (TILOS) 致力于在规模和实践中使不可能的优化成为可能。该研究所是加州大学圣地亚哥分校、麻省理工学院、国立大学、麻省理工大学的合作伙伴。宾夕法尼亚大学、德克萨斯大学奥斯汀分校和耶鲁大学）将率先进行基于学习的优化，以改变芯片设计、机器人技术、通信网络以及对我们国家的健康、繁荣和福利至关重要的其他使用领域。人工智能/机器学习和优化的联系在实践的前沿具有独特的挑战性，这从整体上推动了推广和翻译。 行业合作伙伴将在基础研究及其使用领域应用程序方面与 TILOS 密切互动。以长期、终身学习和技能更新为中心宗旨的继续教育计划，该学院还将在其合作机构取得的明显成功的基础上，扩大参与范围，以覆盖从 K-12 开始的服务不足的人群。 TILOS 将发现、教育并将人工智能、优化和使用的新关系转化为现实世界的实践，TILOS 围绕多个良性循环进行组织，将人工智能和优化、使用领域以及人工智能优化突破转化为统一的良性循环。人工智能和优化的第一个良性循环是 TILOS 的核心，它的核心是：（i）桥接离散和连续优化；（ii）分布式、并行、和联合优化；(iii) 流形优化；(iv) 不确定性下的动态决策；以及 (v) 深度学习中的第二个良性循环。将人工智能优化的基础研究与使用领域的专业知识联系起来，最初的使用领域焦点带来了不同的优化挑战，但激发了具有共同点的共享解决方案，例如物理嵌入性、分层系统上下文、底层图形模型、安全性和鲁棒性。第三个良性循环是与前沿实践的更紧密联系，TILOS 将利用行业合作伙伴关系通过开放标准和数据加速影响。数据集和“数据虚拟现实”以及使研究支持民主化的开源路线图将吸引研究人员聚集在一起，实现与行业和机构合作伙伴的第四个良性循环。劳动力发展的扩大将确定并传授学习、优化和实践之间所需的技能和思维方式，从而为现有劳动力提供技能更新，并为退伍军人或见识过的人等服务不足的人群提供入门机会。通过该研究所与社区组织和初中和高中教育工作者的合作伙伴关系，通过涵盖接触、经验和环境的参与层次，扩大职业转变。该奖项是 NSF 的法定使命，并被认为值得通过以下方式获得支持：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。