Towards a Smart Automated Society: Cognitive Technologies, Knowledge Production, and Economic Growth.

• Author: Udell, Mitchell
• Position: Report

1. Introduction

   Artificial intelligence (AI) can drive innovative robots that perform in open settings because of machine learnings. Enhancements in AI may bring about a replacement of human workers for machines as a lot of tasks in elaborate contexts can be designed in a state-contingent fashion. AI may not generate more tedious tasks to be operated by machines, but it might give rise to the economic circumstances that cause that. (Agrawal, Gans, and Goldfarb, 2018)

2. Conceptual Framework and Literature Review

   Cognitive technologies represent the prevailing counterpart of disruptive automation for processes, requiring a series of management arrangements and first-rate carrying out routines to earn the upsides of which they are qualified. Cognitive technologies can operate numerous tasks with a significant degree of self-determination (Balcerzak et al., 2018; Drugau-Constantin, 2018a; Kliestik et al., 2018a; Mihaila et al., 2018), leading to massive turmoil in job markets. Augmentation constitutes a more feasible aftereffect than wideranging job loss from automation. Jobs and skills may alter seriously as smart machines are embraced in the workplace. When machine learning programs perform satisfactorily, they can intensely enhance both the process and the outcomes over human option, providing the likelihood of a more unbiased and data-driven advances. Innovative work processes from cutting-edge technology have generally gained from redesign endeavors entailing individuals who perform the work. Organizations should harmonize the prospects from automating and making processes sounder (Drugau-Constantin, 2018b; Kliestik et al., 2018b; Nica et al., 2018; Popescu Ljungholm, 2018) with the effect on the personnel in their companies and their jobs. Cognitive technologies are imaginably transformative of business approaches and processes. The demanding aspect of AI models and algorithms is incorporating them in current systems and processes, and altering distinct habits and organizational cultures. (Davenport, 2018)

3. Methodology and Empirical Analysis

   Using and replicating data from Deloitte, Ericsson ConsumerLab, HBR.org, Statista, and Tractica, we performed analyses and made estimates regarding forecasted cumulative global AI revenue (2016-2025), by use case, percentage of executives who mention numerous benefits of AI, business organizations' reasons for adopting AI worldwide, and percentage who think AI help would be good in certain areas.

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