Artificial Intelligence: A Clarification of Misconceptions, Myths and Desired Status

Frank Emmert-Streib; Olli Yli-Harja; Matthias Dehmer

doi:10.3389/frai.2020.524339

Artificial Intelligence: A Clarification of Misconceptions, Myths and Desired Status

Front Artif Intell. 2020 Dec 23:3:524339. doi: 10.3389/frai.2020.524339. eCollection 2020.

Authors

Frank Emmert-Streib¹, Olli Yli-Harja^{2

3

4}, Matthias Dehmer^{5

6

7}

Affiliations

¹ Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.
² Institute of Biosciences and Medical Technology, Tampere, Finland.
³ Computational System Biology, Faculty of Medicine and Health Technology, Tampere University, Finland.
⁴ Institute for Systems Biology, Seattle, WA, United States.
⁵ Department of Mechatronics and Biomedical Computer Science, UMIT, Hall in Tyrol, IL, Austria.
⁶ Department of Computer Science, Swiss Distance University of Applied Sciences, Brig, Switzerland.
⁷ College of Artificial Intelligence, Nankai University, Tianjin, China.

Abstract

The field artificial intelligence (AI) was founded over 65 years ago. Starting with great hopes and ambitious goals the field progressed through various stages of popularity and has recently undergone a revival through the introduction of deep neural networks. Some problems of AI are that, so far, neither the "intelligence" nor the goals of AI are formally defined causing confusion when comparing AI to other fields. In this paper, we present a perspective on the desired and current status of AI in relation to machine learning and statistics and clarify common misconceptions and myths. Our discussion is intended to lift the veil of vagueness surrounding AI to reveal its true countenance.

Keywords: artificial general intelligence; artificial intelligence; data mining; data science; deep neural networks; machine learning; pattern recognition; statistics.