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

工程系统中对能效、安全性、稳健性和其他标准的改进优化提供了不可估量的社会效益的前景。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范围。国家人工智能(AI)大规模学习优化研究所(TILOS)的使命是在规模和实践中使不可能的优化成为可能。该研究所(加州大学圣地亚哥分校、麻省理工学院、国立大学、宾夕法尼亚大学、德克萨斯大学奥斯汀分校和耶鲁大学的合作伙伴)将开创基于学习的优化，改变芯片设计、机器人、通信网络和其他对我们国家的健康、繁荣和福利至关重要的使用领域。在Tilos，研究、教育、推广和翻译是由使AI/ML和优化的关系在实践的前沿具有独特挑战性的整体驱动的。行业合作伙伴将在基础研究及其用途领域应用方面与Tilos密切互动。Tilos将建立一个开放的继续教育计划，以长期、终身学习和技能更新为核心宗旨。该研究所还将扩大参与，在其伙伴机构取得明显成功的基础上，这些机构从K-12开始接触到服务不足的人口。通过这些努力，Tilos将发现、教育并转化为现实世界实践中人工智能、优化和使用的新纽带。Tilos围绕着多个良性循环进行组织，这些良性循环将人工智能和优化统一起来，使用域，并将人工智能优化突破转化为实践。人工智能和优化的第一个良性循环是Tilos的核心，其中每个人都能启用并放大另一个人。基础研究将追求五个主要支柱：(I)连接离散和连续优化；(Ii)分布式、并行和联合优化；(Iii)流形上的优化；(Iv)不确定性下的动态决策；以及(V)深度学习中的非凸优化。挑战、灵感和数据验证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，但激发了具有共性的共享解决方案，例如物理嵌入性、分层系统上下文、底层图形模型、作为第一级关注的安全性和健壮性，以及人引导的系统和自主系统的桥梁。第三个良性循环是翻译，与实践前沿的联系越来越紧密。Tilos将利用行业伙伴关系，通过开放标准、数据集和“数据虚拟现实”以及开放源码来加速影响，这些开放源码使研究成果的获取更加大众化。优化配方和进展指标的路线图将把研究人员聚集在一起，朝着共同的研究目标迈进。与工业界和机构合作伙伴的第四个良性循环既包括劳动力发展，也包括扩大参与。劳动力发展将确定和传授在学习、优化和实践的结合点所需的技能和心态，以便为现有劳动力提供技能更新，并为服务不足的人口结构，如退伍军人或那些看到职业转变的人提供平台。将通过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，通过涉及暴露、经验和环境的层次参与来扩大参与范围。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。

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.