The intrinsically linked future for human and Artificial Intelligence interaction

Miller, Anthony

doi:10.1186/s40537-019-0202-7

Cited by 49 publications

(27 citation statements)

References 9 publications

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“…Perc et al (2019) study the social and legal problems of AI that are connected to the uncertainty as to who will be responsible for its decisionsin particular, its mistakes (e.g., in case of arguable decisions). Miller (2019) thinks that future interactions between human and AI envisage their close interconnection and cooperation. Daugherty et al (2019) point out that AI allows diversifying the activities of companies to a larger extent than human intellectual capital, which creative abilities are higher, but require complex management (motivation and stimulation of labor, time for leisure, etc.).…”

Section: Literature Reviewmentioning

confidence: 99%

Human capital and AI in industry 4.0. Convergence and divergence in social entrepreneurship in Russia

Popkova

Sergi

2020

JIC

199

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PurposeThe purpose of this article is to determine the future proportion and variants of usage of human intellect and artificial intelligence (AI) in entrepreneurship of industry 4.0 that fits social entrepreneurship the most. It could be convergence (simultaneous utilization during the same entrepreneurial processes with the emphasis on unique features by the terms of the competition) or divergence (usage during different business processes by the terms of labor division).Design/methodology/approachThe authors determine the influence of usage of human capital and AI on the efficiency of social entrepreneurship. The authors identify the perspective directions of usage of AI in social entrepreneurship and evaluate the readiness and interest in the implementation of these directions of concerned parties. The authors also model the optimal proportions and the variant of usage of human intellect and AI in social entrepreneurship in the conditions of Industry 4.0 in the future (until 2030).FindingsIt is found that social entrepreneurship will use the opportunities of Industry 4.0 for optimization of its activities until 2030, but will refuse from full automatization, using human intellect and AI at the same time.Originality/valueThe most perspective directions of application of AI at social companies are a collection of social goods and services, marketing studies and promotion of social goods and services. Neither convergence nor divergence of human and artificial intellectual capital does not fully conform to the interests of concerned parties. The most preferable (optimal) variant of usage of human intellect and AI in social entrepreneurship in the Industry 4.0 is human intelligent decision support.

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Section: Literature Reviewmentioning

confidence: 99%

Human capital and AI in industry 4.0. Convergence and divergence in social entrepreneurship in Russia

Popkova

Sergi

2020

JIC

199

View full text Add to dashboard Cite

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“…Thirdly, the theoretical basis of the research consists of the works of experts devoted to consequences of dissemination of AI. Miller (2019) writes of internally connected future for human and AI. Bader and Kaiser (2019) express doubts regarding the algorithmic decision-making.…”

Section: Literature Reviewmentioning

confidence: 99%

Perspective directions of state regulation of competition between human and artificial intellectual capital in Industry 4.0

Bogoviz

2020

JIC

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PurposeThe purpose of the research is to determine the perspective directions of state regulation of competition between human and artificial intellectual capital in Industry 4.0 and to develop scientific and methodological recommendations for their implementation. For this, the directions of state regulation of competition between human and artificial intellectual capital are described, monitoring of competition between human and artificial intellectual capital by the example of modern Russia (2019) is performed and scientific and practical recommendations for state regulation of competition between human and artificial intellectual capital are developed, with their approbation by the example of modern Russia (2019).Design/methodology/approachA method of expert evaluation is used for collection of the information and empirical data. The method of comparative analysis is used for comparing the successfulness of implementing the distinguished directions of state regulation of competition between human and artificial intellectual capital (4.1) according to the official statistics to their current evaluation according to the interested parties. Also, future evaluation (forecasts) according to the interested parties (until 2045) is determined.FindingsIt is substantiated that during evaluation of state regulation of competition between human and artificial intellectual capital in Industry 4.0, one cannot use only the official statistics, as these data are fragmentary and indirect. Fuller and more precise data are provided by assessment according to the interested parties. They allow determining the current and the future state of affairs and, based on it, compiling a forecast and developing a long-term strategy of state regulation of competition between human and artificial intellectual capital in Industry 4.0.Originality/valueThe perspective directions of state regulation of competition between human and artificial intellectual capital in Industry 4.0 are as follows: stimulation of competition in the market of intellectual capital, social risk management of the market of intellectual capital, managing international competition in the market of intellectual capital and ecological risk management of the intellectual capital market. As the experience of modern Russia shows, even at the initial stage of transition to Industry 4.0, the measures of state regulation of competition between human and artificial intellectual capital are not enough, but their deficit is moderate. In the course of development of Industry 4.0, the necessity for the measures of regulation will grow, and their deficit will increase. That's why there's a need for strategic approach to their implementation, which envisages their systemic reconsideration and supplementing. An author's algorithm is offered for this.

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“…[ 7 ] Over the past few years, two factors have led to the skyrocketing of AI worldwide, i.e., data availability and a faster processing capacity. [ 8 ] The amount of data being generated is growing exponentially, which can be seen from the fact that 90% of the data globally has been generated over the past 2 years alone. [ 9 ] With high processing speeds, computers can process all of this information more quickly and effectively, thus steadily rendering AI more real than artificial, and significantly more intelligent.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction

Singh

Rosenkranz

Ansari

et al. 2020

Advanced Intelligent Systems

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Daniel Rosenkranz received his B.Sc. degree in 2011 and his M.Sc. degree in 2014 from the University of Oldenburg. He later worked as a junior researcher at the University of Oldenburg till 2015, funded by the foundation of the metal industry in the northwest Germany. Since 2016, he is working as a Ph.D. scholar at the German Federal Institute of Risk Assessment and the German Federal Institute for Material Research and Testing. His research interests include nanomaterial characterization, fundamentals in analytics, matrix matched measurement strategies, low volume injection systems, protein-nanomaterial interactions.

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The intrinsically linked future for human and Artificial Intelligence interaction

Cited by 49 publications

References 9 publications

Human capital and AI in industry 4.0. Convergence and divergence in social entrepreneurship in Russia

Human capital and AI in industry 4.0. Convergence and divergence in social entrepreneurship in Russia

Perspective directions of state regulation of competition between human and artificial intellectual capital in Industry 4.0

Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction

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