CAREER: Accelerating Scientific Data Collection through Human-in-the-Loop Artificial Intelligence

心理学、海洋科学和生态学等领域的科学家花费了大量的时间、精力和资金来设计实验和收集大型数据集。尽管人工智能(AI)最近的技术突破极大地影响了科学数据分析的过程，但这些方法尚未对科学数据收集产生类似的影响。该项目探索通过使用人工智能自动将数据收集工作引导到空间最有用的区域来提高数据收集的效率。这是一个巨大的挑战；尽管人工智能系统可以轻松地实时分析大量数据，但它们缺乏对科学领域(包括目标、背景知识和经验)的内在理解。另一方面，人类科学家和数据收集者了解这个领域，但缺乏快速处理海量数据的能力。该项目将开发实时指导科学数据收集的原则和系统，人类和人工智能系统将共同工作，发挥彼此的优势。该项目将进一步加深我们对人类-人工智能合作的理解，从而产生新的数据收集算法和交互范式，这将有助于促进许多科学领域的进步。该项目将对夏威夷岛上的研究和教育活动产生重大影响，开发新的人在环路人工智能技术，解决当地重要的问题，并利用这一技术来推动社区成员和当地本科生对科学技术的兴趣增加。该项目的技术目标分为两个方面。首先，研究人员试图了解如何构建用于数据收集的人工智能系统，以确保他们的目标功能与人类科学家很好地结合在一起。该项目将探索通过以下方式更好地协调目标函数：1)以要点和建议的形式要求和纳入更丰富的反馈；2)根据注释过程本身的人类行为推断目标函数。在第二个推力中，调查人员试图了解如何设计人工智能系统，以有效地实时指导人类努力，以优化数据收集。该项目将研究如何通过以下方式改进实时互动：1)确保人工智能系统生成的通知被安排在最大限度地发挥作用的时间；2)建立人工智能系统，通过现场观察人类行为来学习成为更好的队友。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Scientists in domains such as psychology, marine science, and ecology spend a large amount of time, effort, and funding designing experiments and collecting large datasets. Although recent technological breakthroughs in artificial intelligence (AI) have dramatically impacted the process of scientific data analysis, these approaches have not yet had a similar impact on scientific data collection. This project explores improving efficiency of data collection by using AI to automatically direct data gathering efforts towards the most useful regions of the space. This is a big challenge; although AI systems can easily analyze large amounts of data in real-time, they lack an inherent understanding of the scientific domain (including objectives, background knowledge, and experience). On the other hand, human scientists and data collectors understand the domain but lack the ability to quickly process vast amounts of data. This project will develop principles and systems for real-time direction of scientific data collection with humans and AI systems working together, leveraging each other's strengths. The project will further our understanding of human-AI collaboration, resulting in new data collection algorithms and interaction paradigms that will help promote the progress of many scientific domains. The project will have a major impact on research and educational activity on Hawaii Island, developing new human-in-the-loop AI techniques that address problems of great local importance and leveraging this to drive increased interest in science and technology among community members and local undergraduate students.The technical aims of the project are divided into two thrusts. In the first, the investigators seek to understand how to build AI systems for data collection that ensure that their objective functions are well-aligned with human scientists. The project will explore better aligning objective functions through: 1) requesting and incorporating richer feedback in the form of key points and suggestions, and 2) inferring objective functions based on human behavior during the annotation process itself. In the second thrust, the investigators seek to understand how to design AI systems that efficiently direct human effort in real-time to optimize data collection. The project will study how real-time interaction can be improved through: 1) ensuring that notifications generated by the AI system are timed for maximum usefulness, and 2) building AI systems which learn to be a better teammate by observing in situ human behavior.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.