The increasing automation of modern systems—across industry, healthcare, mobility, and beyond—has raised the demand for human reasoning and expertise, while alleviating the burden of repetitive tasks. This transformation is driving us toward Automation 5.0, a new paradigm aimed at unleashing human potential. Recently, the development of foundation models (FMs) has reinvigorated its realization, making it both urgent and critical to explore the concept of Automation 5.0 in this new era. In this article, we define Automation 5.0, discuss its significance, and emphasize its new world, thinking, and technology with the goal of achieving knowledge automation. A framework, based on business FMs, human-oriented operating systems, and scenarios engineering, is proposed, where biological, robotic, and digital humans work together in three modes: autonomous, parallel, and expert/emergency modes. Additionally, a diverse range of its scenarios and applications are summarized and discussed, such as Manufacturing 5.0, Healthcare 5.0, and Transportation 5.0. We believe that Automation 5.0 can drive the co-evolution of productivity and production relations across all domains, propelling society toward a “Safety, Security, Sustainability, Sensitivity, Service, Smartness (6S)” future.

Isolated data islands are prevalent in intelligent automated optical inspection (AOI) systems, limiting the full utilization of data resources and impeding the potential of AOI systems. Establishing a collaborative ecology involving software providers, hardware manufacturers, and factories offers an encouraging solution to build a closed-loop data flow and achieve optimal data resource utilization. However, concerns about privacy issues, rights infringement, and threats from other participants present challenges in establishing an efficient and effective community. In this paper, we propose a novel framework, AOI-OPEN, which first creates a trustworthy AOI ecology to gather related entities with decentralized autonomous organization (DAO) mechanisms. Then, a parallel data pipeline is proposed to generate large-scale virtual samples from small-scale real data for AOI systems. Finally, federated learning (FL) is adopted to use the distributed data resources among multiple entities and build privacy-preserving big models. Experiments on defect classification tasks show that, with privacy preserved, AOI-OPEN greatly strengthens the utilization of distributed data resources and improves the accuracy of inspection models.

平行交通是一种实现智能交通管理与控制的综合性范式,致力于解决人类行为和社会因素的复杂性问题。近年来,基础模型(foundational models, FMs)的崛起为平行交通的实现提供了新的可能。但这种模型固有的知识陈旧、“幻觉”现象以及“黑盒”特性削弱了其决策的可靠性和可信度。为解决这一问题,提出一种基于检索增强生成与思维链提示(chain-of-thought prompting)的平行交通框架TransRAG。该框架由紧密协作的存储层、管理层和执行层组成,旨在为用户提供个性且多样化的交通服务。其中,存储层引入的外部知识增强了管理层中基础模型的性能,以实现复杂的计算实验。执行层中人工交通系统与实际交通系统的虚实交互使得管理层的决策得到持续优化,从而实现动态知识更新和灵活的策略调整,以适应不断变化的交通环境。此外,TransRAG通过区块链、智能合约和缓存技术的集成,能够有效应对单点故障、隐私泄露以及数据访问延迟等问题,从而加速推进向“6S”交通5.0的全面迈进。

Crowdsourcing is a critical technology in social manufacturing, which leverages an extensive and boundless reservoir of human resources to handle a wide array of complex tasks. The successful execution of these complex tasks relies on task decomposition (TD) and allocation, with the former being a prerequisite for the latter. Recently, pretrained language models-based methods have garnered significant attention. However, they are constrained to handling straightforward common-sense tasks due to their inherent restrictions involving limited and difficult-to-update knowledge as well as the presence of “hallucinations.” To address these issues, we propose a retrieval-augmented generation-based crowdsourcing framework that reformulates TD as event detection from the perspective of natural language understanding. However, the existing detection methods fail to distinguish differences between event types and always depend on heuristic rules and external semantic analyzing tools. Therefore, we present a prompt-based contrastive learning framework for TD (PBCT), which incorporates a prompt-based trigger detector to overcome dependence. Additionally, trigger-attentive sentinel and masked contrastive learning are designed to provide varying attention to trigger and contextual features according to different event types. Extensive experiment results demonstrate our method is highly competitive in both supervised and zero-shot detection. A case study on printed circuit board design and manufacturing is used to validate its adaptability and scalability in unfamiliar professional domains.

Under the impetus of Industry 4.0, oil and gas is undergoing an unprecedented digital transformation, and many innovative ideas are proposed. However, the achieved higher efficiency comes at the expense of reduced social consideration, which necessitates more society-related research and calls for a technological paradigm shift. To meet this challenge, this article introduces parallel oil and gas within the framework of parallel intelligence-based Industry 5.0, providing a pivotal transition from cyber–physical systems (CPS) to cyber–physical–social systems (CPSS). A comprehensive review of oil and gas industrial chain that covers upstream, midstream, and downstream is first outlined. Grounded in Industry 5.0, the main principles of parallel oil and gas are then provided, where three kinds of workers (biological workers, digital workers, and robotic workers) and three operation modes (autonomous modes, parallel modes, and expert/emergency modes) collaborate to develop more human-oriented and resilient systems. To realize the desired vision, some enabling technologies, including blockchain, smart contracts, and industrial foundation models, are thereafter listed. Furthermore, computational experiments on fault diagnosis of sucker rod pumps are conducted to illustrate the feasibility and effectiveness of our proposed mechanism. Finally, the future trend toward imaginative intelligence is envisaged.

Since Manufacturing 4.0 faces various challenges, including the risks of data leakage and privacy violation, the struggle to meet the growing demand for personalization, and the limitations in harnessing human creativity, it has become crucial to embark on a transformation toward Manufacturing 5.0. To this end, we propose a DeFACT framework for parallel manufacturing and Manufacturing 5.0, which focuses on safe, efficient and personalized collaborative production. In DeFACT, different enterprises and parallel workers (i.e., digital, robotic and biological workers) are organized, coordinated and scheduled based on decentralized autonomous organizations and operations to promote mutual benefits among members, even in the context of low or zero trust. This contributes to providing customers with higher-quality personalized products and services while ensuring the confidentiality and safeguarding of data. Additionally, various advanced technologies, such as generative artificial intelligence, scenarios engineering, and blockchain, are leveraged to achieve trustworthy and adaptable decision making, user-friendly human–machine interaction, and the federated control and management of parallel workers. Finally, the effectiveness and efficiency of DeFACT are experimentally validated through the design and implementation of three case studies.

Industrial processes are typical cyber–physical–social systems (CPSSs), where the effective management of employees and the efficient control of machines play important roles. Traditional industries heavily rely on human labor and neglect the development of collection–utilization–transmission integrated information loops, thereby leading to high costs and low efficiency in operational procedures. To facilitate the natural interactions and smart operations for humans and machines, industrial foundation models (IFMs) based on metaverses are proposed in this article, serving as the operating systems of industrial parallel machines that provide sustainable data resources and scenarios for management and control experiments. On this basis, IFM comprised of vision foundation models, language foundation models, as well as operational foundation models, are constructed to manage resources in industrial parallel machines and provides comprehensive services for industrial procedures. On the one hand, IFM can efficiently manage various resources including computing power, digital assets, enterprise resources, and platform I/O via the proposed CPSS-based competing, sharing, scheduling, monitoring, allocating, and recovering mechanisms. On the other hand, imaginative intelligence, linguistic intelligence, and algorithmic intelligence can be achieved through vivid visualization of vision foundation models, natural conversations of language foundation models, and smart manipulation of operational foundation models. With the proposed IFM, cyber–physical–social intelligence (CPSI) can be achieved to enhance the efficient management and control of industrial processes.

In cyber–physical–social systems, smart manufacturing has to overcome challenges, such as uncertainty, diversity, complexity in modeling, long-delayed responses to market changes, and human engineer dependency. DeFACT is a framework of parallel manufacturing in ManuVerse where the Decentralized Autonomous Organization-based interactions between parallel workers consisting of robotic, digital, and human workers are elaborated to transform from professional division to real-virtual division. In DeFACT, human workers are only responsible for 5% physical and mental work that is complex and creative, and the robotic and digital workers can take care of the rest. The perceptual and cognitive intelligence of digital workers are intensified by a manufacturing foundation model (MF-PC), where calibration and certification (C&C), and verification and validation (V&V) guarantee not only the accuracy of task models, but also the interpretability and controllability of feature learning. As a case study, the workflow of customized shoes of SANBODY Technology Company is illustrated to show how DeFACT breaks the time and space constraints, avoids production waste caused by aesthetic discrepancies with consumers, and truly realizes flexible manufacturing.