Bridging the Generalization and Interpretation Gaps in Deep Neural Networks

该项目侧重于提高先进人工智能（AI）系统的可靠性和可理解性，特别是深度神经网络（dnn）——一种使用相互连接的元素或“神经元”的分层结构的人工智能，以复杂的方式处理和解释数据。目前，这些人工智能系统面临两个主要挑战。首先，他们可能很难把在一种情况下学到的东西应用到另一种情况。这个问题被称为“泛化差距”，就像一个学生为了考试而死记硬背，但却很难将知识应用到现实世界的场景中。其次，像许多人工智能系统一样，深度神经网络以人类难以理解的方式工作。这种“解释差距”就像使用一台没有用户手册的复杂机器，这可能会使纠正错误或解释做出特定决定的原因变得困难。这些挑战可能会对任何使用人工智能的行业产生影响，从医疗保健到自动驾驶。如果人工智能因为泛化差距而犯错误，或者由于解释差距而不清楚为什么要做出决定，这可能会导致重大错误，缺乏信任，甚至潜在的伤害。本项目旨在研究这些问题，提高人工智能系统的可靠性和透明度，使我们能够更自信、更有效地应用这些技术。通过这样做，它将推进我们对人工智能的科学理解，支持这一重要领域的教育，并通过确保人工智能技术更加可靠和可理解来造福社会。该项目还为研究生提供了研究机会。该项目旨在通过研究一系列明确定义的研究问题，开发一个新的框架来解决深度神经网络的泛化和解释差距。这项工作包括发展新的统计理论，以更好地理解深度神经网络中的泛化误差，创建鲁棒和计算效率高的算法，以及促进分布外泛化的创新方法。该项目将促进我们对深度神经网络的理解，开发新的方法和算法，并为不同领域的实际应用提供见解。主要研究人员将把本计划的研究成果纳入他们的教育工作中。该奖项反映了美国国家科学基金会的法定使命，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。

This project focuses on enhancing the reliability and understandability of advanced artificial intelligence (AI) systems, specifically deep neural networks (DNNs) - a type of AI that uses layered structures of interconnected elements, or "neurons," to process and interpret data in sophisticated ways. Currently, these AI systems face two main challenges. Firstly, they may struggle to apply what they've learned in one situation to a different one. This issue, known as the "generalization gap," is similar to a student who has crammed for an exam but struggles to apply the knowledge in a real-world scenario. Secondly, DNNs, like many AI systems, work in ways that can be difficult for humans to understand. This "interpretation gap" is like using a complicated machine without a user manual, which can make it hard to correct mistakes or explain why specific decisions were made. These challenges could have implications for any sector where AI is used, from healthcare to autopilot. If AI makes mistakes because of the generalization gap or if it's not clear why a decision was made due to the interpretation gap, it could lead to significant errors, lack of trust, or even potential harm. This project aims to study these issues, enhancing the reliability and transparency of AI systems, enabling us to apply these technologies more confidently and effectively. By doing so, it will advance our scientific understanding of AI, support education in this vital field, and benefit society by ensuring AI technologies are more dependable and understandable. The project also provides research opportunities for graduate students. This project aims to develop a new framework to address the generalization and interpretation gaps in DNNs by investigating a series of well-defined research problems. The work includes the development of novel statistical theories for a better understanding of generalization errors in DNNs, the creation of robust and computationally efficient algorithms, and the promotion of innovative approaches for out-of-distribution generalizations. This project will advance our understanding of DNNs, develop new methods and algorithms, and provide insights into practical applications in diverse fields. The principal investigators will incorporate the research findings of this project into their educational endeavors.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.