AuthorBolton, Charlynne
  1. Introduction

    Current innovations in artificial intelligence, machine learning, robotics, and sensors allow devices to automate operations that in times past were safe from disruption. (Wright and Schultz, 2018) There is a prevalent demand for artificial intelligence services by companies who have insufficient access to capital to advance their own such equipment, and autonomous artificial intelligence developers do not have prominence and sustenation. (Axel Montes and Goertzel, 2018)

  2. Literature Review

    The extent of labor market disruptions will require the pace and the factor bias of advance in artificial intelligence. The gaps brought about by artificial intelligence-related cutting edges (Alpopi and Silvestru (Bere), 2016; Gutu, 2018; Hanappi, Ryser, and Bernardi, 2016) are contingent on whether, at a certain wage, the newest technologies generate more or less need for labor. Advance in artificial intelligence may replace human labor, or even substitute for workforce completely. Technological developments may alter the allocation of resources and lead to imbalance (Balcerzak et al., 2018; Kliestik et al., 2018a; Nica and Taylor, 2017) via the surplus collected by groundbreakers and via repercussions on other economic participants. If artificial intelligence shortly substitutes for human labor, the need for the latter and wages will decrease. (Korinek and Stiglitz, 2019)

  3. Methodology

    Using and replicating data from Accenture, BBC, CellStrat, eMarketer, Frontier Economics, MIT Research Report, Morar Consulting, PwC, and Squiz, we performed analyses and made estimates regarding the impact of artificial intelligence (AI) on industry growth: real annual GVA growth by 2035 (%), how AI could change the job market: estimated net job creation by industry sector (2017-2037), reasons given by global companies for AI adoption, and leading advantages of AI for international organizations.

  4. Results and Discussion

    Recent machine-learning patterns have important consequences for the labor market. A computer can automate a task if it is able to develop the demanded data processing employing a series of instructions. If modeling were restricted to inferential guidance (modeling tasks where the data processing structure can be integrated) automation would have a rather irrelevant reach. Machine learning enables computers to develop tasks where a portion or all of the data processing is mechanical (Chessell, 2018; Kliestik et al., 2018b; Lazaroiu...

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