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This paper aims to challenge the fashion of ubiquitous artificial intelligence (AI) and the effects which it will have upon society. In doing so it argues that the effects of AI will be minimal but important.

This argument is based upon the Socratic method and explores the Utilitarian background in which AI is based while drawing upon classical literature and other examples to illustrate the argument.

The findings are encompassed in the argument and show that we need to be more open and careful when considering AI and its effects. We also need to be more realistic when considering potential benefits.

This argument has significant implications for the adoption of AI.

The social implications are equally profound and will impact upon our application of AI solutions to current problems and upon humanity more generally.

This is the first paper which relates AI to human successes.

This study aims to investigate the association between cognitive moral development (CMD) and unethical pro-organizational behaviour (UPB) by taking purchasing employees as research subjects. The moderating effect of perceived leader’s UPB is also explored.

Data were collected through a two-stage questionnaire survey on purchasing employees in companies across a spectrum of industries in Taiwan, and 492 purchasing employees were analysed in the study.

Research findings reveal that employees at the conventional level of CMD are more likely to conduct UPB than those at the pre-conventional and post-conventional levels. Perceived leader’s UPB will moderate the association between CMD and UPB. Employees’ UPB is strongly associated with their CMD when they perceive their leaders as being likely to perform UPB.

Although a variety of factors influencing UPB have been proposed in the literature, none of them have analysed the association between CMD and UPB. However, CMD is one important factor affecting ethical decision-making. The present study can promote further understanding of the role of CMD in UPB and contribute to a growing body of research on CMD and UPB.

This paper aims to examine the global pattern of growth and development of eHealth research based on publication headcount, and analysis of the characteristics, of the keywords used by authors and indexers to represent their research content during 1945–2019.

This study adopted a bibliometric research design and a quantitative approach. The source of the data was Elsevier’s Scopus database. The search query involved multiple search terms because researchers’ choice of keywords varies very significantly. The search for eHealth research publications was limited to conference papers and research articles published before 2020.

eHealth originated in the late 1990s, but it has become an envelope term for describing much older terms such as telemedicine, and its variants that originated much earlier. The keywords were spread through the 27 Scopus Subject Areas, with medicine (44.04%), engineering (12.84%) and computer science (11.47%) leading, while by Scopus All Science Journal Classification Health Sciences accounted for 55.83% of the keywords. Physical sciences followed with 30.62%. The classifications social sciences and life sciences made only single-digit contributions. eHealth is about meeting health needs, but the work of engineers and computer scientists is very outstanding in achieving this goal.

This study demonstrates that eHealth is an unexplored aspect of health literature and highlights the nature of the accumulated literature in the area. It further demonstrates that eHealth is a multidisciplinary area that is attractive to researchers from all disciplines because of its sensitive focus on health, and therefore requires pooling and integration of human resources and expertise, methods and approaches.

This paper aims to review in-memory computing (IMC) for machine learning (ML) applications from history, architectures and options aspects. In this review, the authors investigate different architectural aspects and collect and provide our comparative evaluations.

Collecting over 40 IMC papers related to hardware design and optimization techniques of recent years, then classify them into three optimization option categories: optimization through graphic processing unit (GPU), optimization through reduced precision and optimization through hardware accelerator. Then, the authors brief those techniques in aspects such as what kind of data set it applied, how it is designed and what is the contribution of this design.

ML algorithms are potent tools accommodated on IMC architecture. Although general-purpose hardware (central processing units and GPUs) can supply explicit solutions, their energy efficiencies have limitations because of their excessive flexibility support. On the other hand, hardware accelerators (field programmable gate arrays and application-specific integrated circuits) win on the energy efficiency aspect, but individual accelerator often adapts exclusively to ax single ML approach (family). From a long hardware evolution perspective, hardware/software collaboration heterogeneity design from hybrid platforms is an option for the researcher.

IMC’s optimization enables high-speed processing, increases performance and analyzes massive volumes of data in real-time. This work reviews IMC and its evolution. Then, the authors categorize three optimization paths for the IMC architecture to improve performance metrics.