In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that
can be leveraged by Artificial Intelligence (AI) for applications including
predictive maintenance and production planning. However, despite the
demonstrated potential of AI, its widespread adoption in sectors like
manufacturing remains limited. Our comprehensive review of recent literature,
including standards and reports, pinpoints key challenges: system integration,
data-related issues, managing workforce-related concerns and ensuring
trustworthy AI. A quantitative analysis highlights particular challenges and
topics that are important for practitioners but still need to be sufficiently
investigated by academics. The paper briefly discusses existing solutions to
these challenges and proposes avenues for future research. We hope that this
survey serves as a resource for practitioners evaluating the cost-benefit
implications of AI in CPS and for researchers aiming to address these urgent
challenges.

在工业 4.0 中，网络物理系统 (CPS) 产生大量数据，可以被人工智能 (AI) 用于预测维护和生产计划等应用。然而，尽管已经证明了 AI 的潜力，但其在制造业等领域的广泛应用仍受限。本文通过综合审查最近的文献、标准和报告，确定了一些关键挑战：系统集成、数据相关问题、管理与劳动力有关的问题以及确保可信 AI。定量分析突出了实践者需要重视但尚需学术界进一步研究的特定挑战和主题。本文简要讨论了这些挑战的现有解决方案，并提出了未来研究的方向。我们希望这项调查能够成为评估 CPS 中应用 AI 的成本效益影响的从业者资源，也能够帮助研究人员解决这些紧迫问题。

工业 4.0 中的人工智能：对工业系统集成挑战的综述

Artificial Intelligence in Industry 4.0: A Review of Integration  Challenges for Industrial Systems

ML platforms help enable intelligent data-driven applications and maintain
them with limited engineering effort. Upon sufficiently broad adoption, such
platforms reach economies of scale that bring greater component reuse while
improving efficiency of system development and maintenance. For an end-to-end
ML platform with broad adoption, scaling relies on pervasive ML automation and
system integration to reach the quality we term self-serve that we define with
ten requirements and six optional capabilities. With this in mind, we identify
long-term goals for platform development, discuss related tradeoffs and future
work. Our reasoning is illustrated on two commercially-deployed end-to-end ML
platforms that host hundreds of real-time use cases -- one general-purpose and
one specialized.

研究探讨了机器学习平台的发展、可伸缩性以及实现自助服务质量的要求和能力，并结合两个商业化的端到端机器学习平台来说明其推广应用及未来发展的目标。