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PARTICIPATING IN A HIGHLY AUTOMATED SOCIETY: HOW ARTIFICIAL INTELLIGENCE DISRUPTS THE JOB MARKET.

1. Introduction

The feasibility of artificial intelligence is curbed by the absence of interoperability criteria. (Axel Montes and Goertzel, 2018) Artificial intelligence will make unequaled expected advance in automating narrow (as it is advisable to assess the pattern on a training exemplification that encompasses almost all of the issues the software may deal with) and structured (as machine learning is based on discerning an intrinsic model of data processing) tasks. (Levy, 2018)

2. Literature Review

Cutting-edge technology frequently entails that old jobs are suppressed and employees are desperate to identify new ones. In an operational economy, technological advance generates increased revenue (Alpopi and Silvestru (Bere), 2016; Kliestik et al., 2018a; Popescu et al., 2018), which consecutively can back more jobs. The first type of technological unemployment takes place when earnings do not adjust for structural grounds, whereas the second one is as an impermanent episode, that is when technological change makes personnel out of work at a swifter rate than they can identify new jobs or such occupations are brought about. Admitting eventually employees adjust to artificial intelligence, the shift may be strenuous. Artificial intelligence will affect some industries more than others (Anderson and Kantarelis, 2016; Kliestik et al., 2018b; Popescu Ljungholm, 2017), and relevant job dislocation will occur. (Korinek and Stiglitz, 2019)

3. Methodology

Building our argument by drawing on data collected from Accenture, The Economist, Frontier Economics, PitchBook, Tractica, we performed analyses and made estimates regarding the impact of artificial intelligence on industry output: real gross value added in 2035 (US$ trillions), number of AI use cases by industry with high job impact, global merger-and-acquisition activity related to artificial intelligence (number of deals and value, $bn), and the economic impact of AI on countries: annual growth rates by 2035 of gross value added (a close approximation of GDP).

4. Results and Discussion

The main firms in diverse sectors have capitalized on automation and are upgrading the subsequent bringing forth of processes and abilities to maximize human-machine joint efforts, reconceptualizing operations as vigorous and flexible by employing human and mass inputs and instantaneous information. Artificial intelligence technologies will be decisive in assisting firms in bring labor more convenient to the markets they serve (Balcerzak et al., 2018; Means, 2017; Skordoulis, 2016), enhancing awareness to customer demand. The advent of artificial intelligence is bringing about innovative roles and cutting-edge opportunities for workers populating the industrial value chain. A lot of jobs and tasks will persevere in shifting to robots contingent on their correlative advantages in dealing with repetition and information processing capacity. (Daugherty and Wilson, 2018) (Figures 1-4)

5. Conclusions

As cognitive technologies are advanced, companies should consider how work will be performed with a particular innovative application, addressing in detail the division of labor between personnel and the artificial intelligence. Numerous organizations employ cognitive technologies to upgrade cuttingedge or improved manufactured article and service offerings. Cognitive technologies can reinforce or stimulate strategic alterations in business patterns (Gutu, 2018; Popescu and Creager, 2017; Tulloch, 2016), and, in their present configuration, back or automate tasks, and not complete jobs or processes, having certain effect on organizational structures and occupations. As they chiefly augment human workers instead of automating their jobs thoroughly, cognitive technologies will not allow drastic alterations to organizational structures. (Davenport, 2018)

doi: 10.22381/EMFM13420185

Received 22 April 2018 * Received in revised form 3 August 2018

Accepted 14 August 2018 * Available online 10 December 2018

Acknowledgments

This paper was supported by Grant GE-1427701 from the American Association for Economic Research.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

REFERENCES

Alpopi, C., and R. Silvestru (Bere) (2016). "Urban Development towards Smart City --A Case Study," Administratie si Management Public 27: 107-122.

Anderson, E., and D. Kantarelis (2016). "Incentive-Compatibility and Coasean Invariance in Property Affairs," Contemporary Readings in Law and Social Justice 8(2): 96-114.

Axel Montes, G., and B. Goertzel (2018). "Distributed, Decentralized, and Democratized Artificial Intelligence," Technological Forecasting and Social Change. doi: 10.1016/j.techfore.2018.11.010

Balcerzak, A. P., T. Kliestik, D. Streimikiene, and L. Smrcka (2018). "Non- Parametric Approach to Measuring the Efficiency of Banking Sectors in European Union Countries," Acta Polytechnica Hungarica 14(7): 51-70.

Daugherty, P. R., and H. J. Wilson (2018). Human + Machine: Reimagining Work in the Age of AI. Boston, MA: Harvard Business Review Press.

Davenport, T. H. (2018). The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. Cambridge, MA, and London: The MIT Press.

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Kliestik, T., M. Misankova, K. Valaskova, and L. Svabova (2018a). "Bankruptcy Prevention: New Effort to Reflect on Legal and Social Changes," Science and Engineering Ethics 24(2): 791-803.

Kliestik, T., M. Kovacova, I. Podhorska, and J. Kliestikova (2018b). "Searching for Key Sources of Goodwill Creation as New Global Managerial Challenge," Polish Journal of Management Studies 17(1): 144-154.

Korinek, A., and J. E. Stiglitz (2019). "Artificial Intelligence and Its Implications for Income Distribution and Unemployment," in A. Agarwal, A. Goldfarb, and J. Gans (eds.), The Economics of Artificial Intelligence. Chicago, IL: University of Chicago Press. Forthcoming.

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Popescu Ljungholm, D. (2017). "The Social Ontology of Institutions: A Reassessment," Contemporary Readings in Law and Social Justice 9(1): 132-138.

Skordoulis, K. (2016). "Science, Knowledge Production and Social Practice," Knowledge Cultures 4(6): 291-307.

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MAREK VOCHOZKA

vochozka@mail.vstecb.cz

The School of Expertness and Valuation, The Institute of Technology and Business in Ceske Budejovice

TOMAS KLIESTIK

tomas.kliestik@fpedas.uniza.sk

Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Zilina

JANA KLIESTIKOVA

jana.kliestikova@fpedas.uniza.sk

Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Zilina

GRATIELA SION

gratielasion@gmail.com

Spiru Haret University, Bucharest (corresponding author)

Caption: Figure 2 Number of AI use cases by industry with high job impact

Caption: Figure 3 Global merger-and-acquisition activity related to artificial intelligence
Figure 1 The impact of AI on industry output.
Real gross value added in 2035 (US$ trillions)

                          Baseline     AI steady state

Manufacturing               8.6             12.4

Professional services       7.8              9.5

Wholesale & retail          6.4              8.7

Public services             4.2              5.1

Information &
communication               3.9              4.9

Financial services          3.6              4.8

Construction                 3               3.6

Transportation &
storage                     2.4              3.1

Healthcare                  2.6              2.9

Accomodation & food
services                    1.7              2.2

Social services             1.9              1.5

Utilities                   1.1              1.5

Education                   1.2              1.3

Agriculture, forestry
& fishing                   0.7              0.9

Other services              0.7              0.6

Arts, entertainment
& recreation                0.48            0.57

Sources: Accenture; Frontier Economics; our 2018 estimates.

Note: Table made from bar graph.

Figure 4 The economic impact of AI on countries: Annual
growth rates by 2035 of gross value added (a close
approximation of GDP)

                          Baseline     AI steady state

US                          2.8              4.7

Finland                     2.2              4.3

UK                          2.7              4.1

Sweden                      1.9              3.7

Netherlands                 1.7              3.4

Germany                     1.5              3.1

Austria                     1.5              3.1

France                      1.9               3

Japan                       1.0              2.8

Belgium                     1.7              2.8

Spain                       1.9              2.6

Italy                       1.1              2.0

Sources: Accenture; Frontier Economics; our 2018 estimates.

Note: Table made from bar graph.
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Author:Vochozka, Marek; Kliestik, Tomas; Kliestikova, Jana; Sion, Gratiela
Publication:Economics, Management, and Financial Markets
Article Type:Report
Date:Dec 1, 2018
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