Ageing workforce management in manufacturing systems: state of the art and future research agenda

The workforce ageing phenomenon is recently affecting most of the Organisation for Economic Co-operation and Development (OECD) member countries, due to a general ageing of their populations and a higher average retirement age of the workforce. In this paper, the topic of ageing workforce management is addressed from a production research standpoint, with the aim of understanding how older workers can be supported and involved in a manufacturing system. First, the current state of the art related to the ageing workforce in production systems is presented. This is structured according to four main topics: (1) analysis and evaluation of ageing workers’ functional capacities, (2) consideration of ageing workers’ capacities in industrial system modelling and management, (3) analysis and exploitation of ageing workers’ expertise, (4) acknowledgement, analysis, design and integration of supporting technologies. Next, the discussion on the impact of the ageing workforce on manufacturing systems’ performances leads to the comparison of some technological advances that are related to the Industry 4.0 paradigms. Finally, a future research agenda on this topic is proposed, based on the same topics classification proposed for the literature analysis. Five different research areas are derived, suggesting future directions for appropriate research concerning the employ of older workers in production environments.     

Deep learning diffusion by infusion into preexisting technologies – Implications for users and society at large

Artificial Intelligence (AI) in the form of Deep Learning (DL) technology has diffused in the consumer domain in a unique way as compared to previous general-purpose technologies. DL has often spread by infusion, i.e., by being added to preexisting technologies that are already in use. We find that DL-algorithms for recommendations or ranking have been infused into all the 15 most popular mobile applications (apps) in the U.S. (as of May 2019). DL-infusion enables fast and vast diffusion. For example, when a DL-system was infused into YouTube, it almost immediately reached a third of the world's population. We argue that existing theories of innovation diffusion and adoption have limited relevance for DL-infusion, because it is a process that is driven by enterprises rather than individuals. We also discuss its social and ethical implications. First, consumers have a limited ability to detect and evaluate an infused technology. DL-infusion may thus help to explain why AI's presence in society has not been challenged by many. Second, the DL-providers are likely to face conflicts of interest, since consumer and supplier goals are not always aligned. Third, infusion is likely to be a particularly important diffusion process for DL-technologies as compared to other innovations, because they need large data sets to function well, which can be drawn from preexisting users. Related, it seems that larger technology companies comparatively benefit more from DL-infusion, because they already have many users. This suggests that the value drawn from DL is likely to follow a Matthew Effect of accumulated advantage online: many preexisting users provide a lot of behavioral data, which bring about better DL-driven features, which attract even more users, etc. Such a self-reinforcing process could limit the possibilities for new companies to compete. This way, the notion of DL-infusion may put light on the power shift that comes with the presence of AI in society.     

数智赋能蔡氏古民居建筑基因的智能设计传承

目的 数智时代,基于大数据技术挖掘蔡氏古民居的网络用户评价,发现现存问题,提出有效设计策略并验证其可行性。方法 首先,对互联网上的热门旅游平台和社交媒体中有关蔡氏古民居建筑群的用户评论数据进行挖掘,分析总结用户需求,确定开发传承蔡氏古民居建筑基因的文旅品牌IP形象,将IP形象作为吸引物置于虚拟导览程序和公共展示空间中,以提升蔡氏古民居景区的吸引力;其次,依据建筑模因理论构建蔡氏古民居建筑基因图谱,对建筑的形式基因、空间基因及思维基因分别进行图谱绘制;再次,运用S-O-R理论模型进行实验评价,根据用户反应时长筛选出用于设计实践的建筑因子;最后,采用生成式人工智能工具辅助,将提取出的建筑因子应用于IP形象的设计方案。结果 选择智能生成IP形象中的最优方案,采用人工完成三维建模并运用到蔡氏古民居的线上线下旅游推广场景中。结论 本研究提出了一个结合大数据挖掘、建筑模因理论和人工智能辅助设计的创新框架,对于文化遗产基因的传承与推广具有重要的理论研究意义和实践应用价值。     

Are our values becoming more fit for artificial intelligence society? A longitudinal study of occupational values and occupational susceptibility to technological substitution

Advanced technologies are changing our working life in unpredictable ways. Consequently, a fear of technologically induced mass unemployment has re-emerged. The increased precarity associated with the technological substitution of work could lead to a regression towards materialist values that are more accepting of authoritarianism and xenophobia. Crucially, these values are less associated with the skills demanded in future work, which tends to be depicted as demanding higher levels of innovation, creative and social skills that are associated with post-materialist values. Current research has thus far overlooked the cultural aspects of large-scale technological substitution of work, which this study illuminates. We investigate how the relationship between occupational values and occupational automatability has developed between 2002 and 2018 in Europe. The results demonstrate that occupational values have been rather stable throughout the period. Occupational values are not becoming more or less fit for artificial intelligence society as would be expected if the context becomes increasingly precarious or innovation-driven. The paper demonstrates that a cultural adaptation to this type of society has not yet occurred.