Open Access

ARTICLE

Analyzing Some Elements of Technological Singularity Using Regression Methods

by Ishaani Priyadarshini^1,*, Pinaki Ranjan Mohanty², Chase Cotton¹

1 Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, 19716, USA
2 Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA

* Corresponding Author: Ishaani Priyadarshini. Email:

(This article belongs to the Special Issue: Emerging Computational Intelligence Technologies for Software Engineering: Paradigms, Principles and Applications)

Computers, Materials & Continua 2021, 67(3), 3229-3247. https://doi.org/10.32604/cmc.2021.015250

Received 12 November 2020; Accepted 02 January 2021; Issue published 01 March 2021

Abstract

Technological advancement has contributed immensely to human life and society. Technologies like industrial robots, artificial intelligence, and machine learning are advancing at a rapid pace. While the evolution of Artificial Intelligence has contributed significantly to the development of personal assistants, automated drones, smart home devices, etc., it has also raised questions about the much-anticipated point in the future where machines may develop intelligence that may be equal to or greater than humans, a term that is popularly known as Technological Singularity. Although technological singularity promises great benefits, past research works on Artificial Intelligence (AI) systems going rogue highlight the downside of Technological Singularity and assert that it may lead to catastrophic effects. Thus, there is a need to identify factors that contribute to technological advancement and may ultimately lead to Technological Singularity in the future. In this paper, we identify factors such as Number of scientific publications in Artificial Intelligence, Number of scientific publications in Machine Learning, Dynamic RAM (Random Access Memory) Price, Number of Transistors, and Speed of Computers’ Processors, and analyze their effects on Technological Singularity using Regression methods (Multiple Linear Regression and Simple Linear Regression). The predictive ability of the models has been validated using PRESS and k-fold cross-validation. Our study shows that academic advancement in AI and ML and Dynamic RAM prices contribute significantly to Technological Singularity. Investigating the factors would help researchers and industry experts comprehend what leads to Technological Singularity and, if needed, how to prevent undesirable outcomes.

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