Towards Anthropomorphic Machine Learning

Angelov, Plamen Parvanov and Gu, Xiaowei (2018) Towards Anthropomorphic Machine Learning. IEEE Computer, 51 (9). pp. 18-27. ISSN 0018-9162

PDF (CO_COMSI-2018-05-0087.R1_Gu)
CO_COMSI_2018_05_0087.R1_Gu.pdf - Accepted Version

Download (1MB)


In this paper, we introduce and discuss the concept of anthropomorphic machine learning as an emerging direction for the future development in the area of artificial intelligence (AI) and data science. We start with outlining research challenges and opportunities, which the contemporary landscape offers. We focus on machine learning, statistical learning, deep learning and computational intelligence as theoretical and methodological areas of greater promise for breakthrough results and underpinning the future revolutionary changes in technology development as well as in our everyday life and societies. Our critical analysis brings us to the open problems and we formulate the paradigm shift in the understanding of machine learning. In a nutshell, our vision for the next generational machine learning methods and algorithms is anthropomorphic, which resembles the way people/humans learn from data. This concept brings machine learning from the statistics to the area of computational intelligence and AI.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Computer
Additional Information:
©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
31 Jul 2018 09:28
Last Modified:
20 Jan 2023 01:52