物理人工智能治理：跨越生命周期从理论到实践

中文摘要 AI 辅助

随着物理人工智能的出现，人工智能正从基于屏幕的应用扩展到在物理世界中感知、交互和行动的实体系统。与传统人工智能不同，物理人工智能在实时安全约束下运行，不断与动态环境交互并与人类共存，带来了现有人工智能治理框架未明确解决的治理挑战。本文从科学和操作角度对物理人工智能治理进行了全面综述。我们综合现有治理原则并将其组织成一个针对物理人工智能系统的统一治理框架。在此基础上，我们提出了一个包括研究、设计、数据、模型开发和部署的五阶段物理人工智能生命周期，并展示了如何通过具体实施实践在每个阶段实施治理。通过将治理原则与工程工作流程联系起来，本综述为研究人员、开发人员和政策制定者构建安全、可信且符合社会价值观的物理人工智能系统提供了结构化参考。

英文摘要

Physical intelligence refers to intelligence systems that understand, reason about, and act in accordance with the physical world and its underlying laws, dynamics, and constraints. Unlike conventional AI systems, physical intelligence interacts continuously with uncertain physical environments, and its actions produce consequences that are physically irreversible. As existing trustworthy AI frameworks have been developed primarily for digital AI systems, they do not fully capture the distinctive challenges of Physical Intelligence, such as physical safety, cyber-physical security, and physical manufacturing process. To address this gap, we present a survey of trustworthy physical intelligence principles. First, we characterize the core capabilities and challenges of physical intelligence. Second, we examine the role of physics in AI. Third, we trace the end-to-end physical intelligence life cycle across five core stages and introduce Trustworthy Physical Intelligence Operationalization (T-PAIO). Fourth, we develop the Trustworthy Physical Intelligence (T-PAI) framework, a theoretical framework that organizes key trustworthiness principles and provides a foundation for governing trustworthy physical intelligence systems.

发表机构

• Case Western Reserve University（凯斯西储大学）
• Shanghai Jiao Tong University（上海交通大学）
• Massachusetts Institute of Technology（麻省理工学院）
• Columbia University（哥伦比亚大学）
• University of California, Riverside（加州大学河滨分校）
• Nanyang Technological University（南洋理工大学）
• New York University（纽约大学）
• Salesforce（Salesforce公司）
• Harvard University（哈佛大学）
• Stanford University（斯坦福大学）

机构由 AI 辅助整理，请以论文原文为准。