HDR TRIPODS: Data Science Principles of the Human-Machine Convergence

该项目将开发一个新的跨学科研究所，用于智能系统和人员交互的数据科学，称为DATA-INSPIRE。该研究所坚信，需要数据科学原理的进步来影响智能机器的新兴范式及其与人类社会的融合，特别是进一步提高性能，更好地解释这些机器的操作，这些机器可以完成各种现实世界的任务，并与人有效地互动。数据科学的基本概念，可以促进智能机器的发展，可以影响我们星球面临的紧迫问题：DATA-INSPIRE将汇集数学家，统计学家和计算机科学家，进行跨学科研究项目，新的教育计划，研讨会，以及其他旨在促进新的基础数据科学社区的努力，该社区专注于智能交互式机器的开发。它将为学生在数据科学的跨学科基础工作做好准备，帮助课程开发，并让政府和工业合作伙伴参与合作，并帮助理解使用智能机器所产生的劳动力问题。智能机器，如机器人，正在从简单的自动机发展到复杂的自动机，在高度结构化和封闭的机器人中执行重复性任务，闭环系统能够在包括人的动态环境中满足人的规范。为了管理和掌握复杂机器的操作及其与人的交互，有必要更好地理解和适应驱动控制它们的算法的数据。DATA-INSPIRE将应对以下挑战。(1)自动驾驶或机器人手术等任务的失败可能会带来毁灭性的后果。数据驱动的解决方案通常是不透明的计算工具，无法验证正确性或解释失败。需要与数据集成的形式化工具来消除导致智能机器以某些方式执行的模糊性。(2)数据驱动的解决方案通常严重依赖于大量准确标记的训练实例，这些实例很难为物理操作收集。需要工具来减少机器学习方法对大量特定任务监督的依赖。(3)大多数智能机器需要在关键的截止日期内对传感数据做出反应，如果不满足，可能会危及操作。需要对学习和控制的动态进行数学和统计分析，以帮助更有效的实时决策。该项目是美国国家科学基金会利用数据革命（HDR）大创意活动的一部分。该奖项反映了NSF的法定使命，并被认为值得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来支持。

This project will develop a new transdisciplinary Institute on Data Science for Intelligent Systems and People Interaction referred to as DATA-INSPIRE. This institute is premised on the belief that advances in data science principles are needed to impact the emerging paradigm of intelligent machines and their convergence with human society, and in particular to further improve the performance and better explain the operation of such machines that can accomplish diverse, real-world tasks and interact effectively with people. Fundamental notions of data science that can enhance development of intelligent machines can impact pressing problems facing our planet: healthcare, transportation, urban systems, etc. DATA-INSPIRE will bring together mathematicians, statisticians, and computer scientists for transdisciplinary research projects, new educational initiatives, workshops, and other efforts designed to catalyze a new foundational data science community focused on the development of intelligent, interactive machines. It will prepare students for transdisciplinary foundational work in data science, aid curriculum development, and involve government and industrial partners in collaborations and to aid in understanding of workforce issues resulting from use of intelligent machines.Intelligent machines, such as robots, are evolving from simple automata performing repetitive tasks in highly structured and enclosed workspaces to sophisticated, closed-loop systems capable of satisfying human specifications in dynamic environments that include people. To manage and master the operations of complex machines and their interactions with people, it is necessary to better understand and adapt the data that drive the algorithms that control them. DATA-INSPIRE will address the following challenges. (1) Failures in tasks such as autonomous driving or robotic surgery can have devastating consequences. Data-driven solutions are often opaque computational tools for which it is impossible to verify correctness or explain failures. Formal tools, integrated with data, are needed to remove ambiguity about what causes intelligent machines to perform in certain ways. (2) Data-driven solutions frequently depend critically on vast corpora of accurately labeled training instances, which can be difficult to collect for physical operations. Tools are needed to reduce machine learning methods' dependence on large amounts of task-specific supervision. (3) Most intelligent machines need to react to sensing data under critical deadlines, which, if not met, can jeopardize operations. Mathematical and statistical analyses of the dynamics of learning and control are needed to assist with more effective real-time decision making.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.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.