Purpose: Exoskeletons are mechanical structures that humans can wear to increase their strength and endurance. The purpose of this paper is to explain how exoskeletons can be used to improve performance across five phases of manufacturing. Design/methodology/approach: Multivocal literature review, encompassing scientific literature and the grey literature of online reports, etc., to inform comprehensive, comparative and critical analyses of the potential of exoskeletons to improve manufacturing performance. Findings: There are at least eight different types of exoskeletons that can be used to improve human strength and endurance in manual work during different phases of production. However, exoskeletons can have the unintended negative consequence of reducing human flexibility leading to new sources of musculoskeletal disorders (MSD) and accidents. Research limitations/implications: Findings are relevant to function allocation research concerned with manual production work. In particular, exoskeletons could exacerbate the traditional trade-off between human flexibility and robot consistency by making human workers less flexible. Practical implications: The introduction of exoskeletons requires careful health and safety planning if exoskeletons are to improve human strength and endurance without introducing new sources of MSD and accidents. Originality/value: The originality of this paper is that it provides detailed information about a new manufacturing technology: exoskeletons. The value of this paper is that it provides information that is comprehensive, comparative and critical about exoskeletons as a potential alternative to robotics across five phases of manufacturing.

Numerous potential barriers to the interorganizational use of building information models (BIMs) have been reported. These potential barriers are technological and organizational. Further, potential barriers are specific to building information models and general to the diffusion of innovation. In spite of potential barriers, there is interorganizational use of BIMs in Finland. The research was carried out in Finland, and investigated organizations' existing and planned interorganizational use of BIMs. The research revealed the potential for interorganizational use of BIMs to enable three types of effects. These are automational effects, informational effects and transformational effects. The generalizability of findings is restricted by at least three factors. First, the research involved only 20 organizations. Second, the research was carried out in one country. Third, the research participants had previously developed skills which can contribute to the interorganizational use of BIMs. Notwithstanding these limitations, consideration of findings suggests that there are few, if any, fundamental barriers to the achievement of automational, informational and/or transformational effects.

The need for self-expression influences individuals in their preferences for goods and how they obtain them. One way for individuals to express themselves is through the prosumption of original unique goods. It is explained how the traditional trade-off between the originality and economy of such goods can be addressed through applications of artificial intelligence. However, although applications of artificial intelligence can transcend the originality/economy trade-off, they cannot transcend differences between cultures that value scarcity and cultures that value sharing. Nonetheless, applications of artificial intelligence can expand human self-expression because they can reduce barriers, such as lack of production skills, to prosumers realizing their own original ideas. With reference to technologies’ cultural domestication, it is explained that rather than artificial intelligence being either a miracle or a monster, the potential of artificial intelligence is mediated by multiple considerations.

Information that is complicated and ambiguous entails high cognitive load. Trying to understand such information can involve a lot of cognitive effort. An alternative to expending a lot of cognitive effort is to engage in motivated cognition, which can involve selective attention to new information that matches existing beliefs. In accordance with principles of least action related to management of cognitive effort, another alternative is to give up trying to understand new information with high cognitive load. In either case, high cognitive load can limit potential for understanding of new information and learning from new information. Cognitive Load Theory (CLT) provides a framework for relating the characteristics of information to human cognitive load. Although CLT has been developed through more than three decades of scientific research, it has not been applied comprehensively to improve the explainability, transparency, interpretability, and shared interpretability (ETISI) of machine learning models and their outputs. Here, in order to illustrate the broad relevance of CLT to ETISI, it is applied to analyze a type of hybrid machine learning called Algebraic Machine Learning (AML). This is the example because AML has characteristics that offer high potential for ETISI. However, application of CLT reveals potential for high cognitive load that can limit ETISI even when AML is used in conjunction with decision trees. Following the AML example, the general relevance of CLT to machine learning ETISI is discussed with the examples of SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and the Contextual Importance and Utility (CIU) method. Overall, it is argued in this Perspective paper that CLT can provide science-based design principles that can contribute to improving the ETISI of all types of machine learning.

Some well-known scientists and technologists have expressed concern that robots may take over the world. More generally, there is concern that robots will take over human jobs and leave billions of people suffering long-term unemployment. Yet, such concerns do not take into account the potential for human beings to enhance their natural capabilities with in-the-body technologies and so become cyborgs with superior capabilities to robots. Types of cyborgs include: human beings with mass produced biomedical implants; human beings with mass imagineered body hacks; and human beings with mass customized insideables. In this paper, human enhancement with in-the-body technologies is analyzed through the theoretical frameworks of mass paradigms, technology domestication, and cultural capital. The implications of the findings of these analyses are related to debates about the future of society. In particular, opportunity versus exploitation, utopia versus dystopia, and emancipation versus extinction. It is explained that that debates about the future of society are flawed if they focus more upon robots than cyborgs. This is because cyborgs can provide more highly advanced embodied cognition, and the number of cyborgs continues to increase as enterprises introduce new in-the-body technologies while individuals seek to increase cultural capital through body projects. Accordingly, debates about the future of society should consider the potential of cyborgs, as well as robots, replacing human beings.

Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.