The integration of machine learning (ML) into cyber-physical systems (CPS)
offers significant benefits, including enhanced efficiency, predictive
capabilities, real-time responsiveness, and the enabling of autonomous
operations. This convergence has accelerated the development and deployment of
a range of real-world applications, such as autonomous vehicles, delivery
drones, service robots, and telemedicine procedures. However, the software
development life cycle (SDLC) for AI-infused CPS diverges significantly from
traditional approaches, featuring data and learning as two critical components.
Existing verification and validation techniques are often inadequate for these
new paradigms. In this study, we pinpoint the main challenges in ensuring
formal safety for learningenabled CPS.We begin by examining testing as the most
pragmatic method for verification and validation, summarizing the current
state-of-the-art methodologies. Recognizing the limitations in current testing
approaches to provide formal safety guarantees, we propose a roadmap to
transition from foundational probabilistic testing to a more rigorous approach
capable of delivering formal assurance.

通过检查以测试为基础的验证和验证作为最实用的方法，并总结当前先进的方法学，在确保学习能力的 CPS 的形式安全方面，确定了当前测试方法的局限性，并提出了从基础概率测试过渡到更严格方法的路线图，以提供正式保证。

使用大型语言模型进行测试学习能力的网络物理系统：一种形式化方法

Testing learning-enabled cyber-physical systems with Large-Language  Models: A Formal Approach

In the last years, the raise of Artificial Intelligence (AI), and its
pervasiveness in our lives, has sparked a flourishing debate about the ethical
principles that should lead its implementation and use in society. Driven by
these concerns, we conduct a rapid review of several frameworks providing
principles, guidelines, and/or tools to help practitioners in the development
and deployment of Responsible AI (RAI) applications. We map each framework
w.r.t. the different Software Development Life Cycle (SDLC) phases discovering
that most of these frameworks fall just in the Requirements Elicitation phase,
leaving the other phases uncovered. Very few of these frameworks offer
supporting tools for practitioners, and they are mainly provided by private
companies. Our results reveal that there is not a "catching-all" framework
supporting both technical and non-technical stakeholders in the implementation
of real-world projects. Our findings highlight the lack of a comprehensive
framework encompassing all RAI principles and all (SDLC) phases that could be
navigated by users with different skill sets and with different goals.

简要介绍了几个提供负责人工智能应用程序的框架，发现大多数框架仅适用于需求调查阶段，仅有一些私人公司提供了支持工具。结果表明，还缺乏一个全面的框架，该框架包含所有负责人工智能的原则和所有 SDLC 阶段，并可由不同技能集和不同目标的用户浏览。