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There are significant challenges and opportunities facing the economies of the United States in the coming decades of the 21st century that are being driven by elements of technological unemployment. Deep learning systems, an advanced form of machine learning that is often referred to as artificial intelligence, is presently reshaping many aspects of traditional digital communication technology employment, primarily network system administration and network security system design and maintenance.
This paper focuses on the question of what impact artificial intelligence will have on the availability of employment opportunities in the field of networked digital communication systems. Current trends related to technological evolution, the rate of growth for both the adoption of these technologies and the potential for increasing automation of human activities for system management and maintenance will be reviewed as potential causal factors for AI-driven technological unemployment, allowing for an informed estimation of key risk dates.
The significance of this study is that it synthesizes concepts from experts from a number of disparate fields who comprise thought leadership in broadly related subject areas, along with recent experimental results that attempt to capture time-series trends in projected general AI capabilities. The combination of the quantity and type of qualitative data from a large number of related dimensions and applying it to this specific research question makes the undertaking of this study significant for the field of digital communication systems.
The United States as well as other developed nations around the globe is experiencing the beginning stages of a wave of technological unemployment, brought about in large measure by a technological revolution that has been made possible by a unique and serendipitous combination of significant advances in computer software, open source software package development, statistic research, computer hardware and the Internet.
Issues related to AI (broadly encompassing both machine learning and deep learning) and its effect on unemployment were recently discussed by Adamczyk, et al. (2019) when it was stated that technological unemployment may be defined as the loss of jobs that a society endures due to the replacement of workers by machines or algorithms.
Jobs related to the field of digital communication systems have been far less subject to much of technological unemployment trends thus far. According to the US Bureau of Labor Statistics (2020), the job outlook for the field of Network and Computer Systems Administrators from 2019-2029 is projected to increase at an annualized rate of 4%. However, a future comprised largely of centrally-managed cloud computing networks and autonomous learning software agents that administer and manage aspects of these sophisticated systems has the potential to change all of that, as expressed in research from Zhao, et al. (2018).
Zhao, et al. states that as networks (including high-capacity optical networks) undergo rapid evolutionary advances, increasing operating expenses related to the human costs of administration, operations and maintenance become both key driving costs and limiting factors in the scalable growth of these systems. According to the authors, advances in AI in self-optimizing optical networks can be used to substantially improve network performance while substantially lowering administration costs. The authors proposed how such self-optimizing optical networks could be used to effectively conduct tidal network traffic prediction, alarm prediction, routing and proper wavelength assignments as well as conducting anomaly detection in real-time with minimal or zero human intervention. Utilizing Zhao’s findings as a starting framework, this paper attempts to further explore this issue and attempts to quantify aspects of the impending challenge of AI/ML for network and system administration technological unemployment.