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  • CO_COMSI-2018-05-0087.R1_Gu

    Rights statement: ©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.

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Towards Anthropomorphic Machine Learning

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

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Towards Anthropomorphic Machine Learning. / Angelov, Plamen Parvanov; Gu, Xiaowei.
In: IEEE Computer, Vol. 51, No. 9, 09.2018, p. 18-27.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Angelov, PP & Gu, X 2018, 'Towards Anthropomorphic Machine Learning', IEEE Computer, vol. 51, no. 9, pp. 18-27. https://doi.org/10.1109/MC.2018.3620973

APA

Angelov, P. P., & Gu, X. (2018). Towards Anthropomorphic Machine Learning. IEEE Computer, 51(9), 18-27. https://doi.org/10.1109/MC.2018.3620973

Vancouver

Angelov PP, Gu X. Towards Anthropomorphic Machine Learning. IEEE Computer. 2018 Sept;51(9):18-27. doi: 10.1109/MC.2018.3620973

Author

Angelov, Plamen Parvanov ; Gu, Xiaowei. / Towards Anthropomorphic Machine Learning. In: IEEE Computer. 2018 ; Vol. 51, No. 9. pp. 18-27.

Bibtex

@article{5a0144f2404a40df8beb033b1f990837,
title = "Towards Anthropomorphic Machine Learning",
abstract = "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. ",
author = "Angelov, {Plamen Parvanov} and Xiaowei Gu",
note = "{\textcopyright}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.",
year = "2018",
month = sep,
doi = "10.1109/MC.2018.3620973",
language = "English",
volume = "51",
pages = "18--27",
journal = "IEEE Computer",
issn = "0018-9162",
publisher = "IEEE COMPUTER SOC",
number = "9",

}

RIS

TY - JOUR

T1 - Towards Anthropomorphic Machine Learning

AU - Angelov, Plamen Parvanov

AU - Gu, Xiaowei

N1 - ©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.

PY - 2018/9

Y1 - 2018/9

N2 - 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.

AB - 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.

U2 - 10.1109/MC.2018.3620973

DO - 10.1109/MC.2018.3620973

M3 - Journal article

VL - 51

SP - 18

EP - 27

JO - IEEE Computer

JF - IEEE Computer

SN - 0018-9162

IS - 9

ER -