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Big Data Mining and Analytics 2020, 3(4): 292-299 https://doi.org/10.26599/BDMA.2020.9020009
Open Access | Issue | Published: 16 November 2020

Machine Knowledge and Human Cognition

Show Author's Information Fashen Li,  Lian Li,  Jianping Yin,  Liang Huang,  Qingguo Zhou( ),  Ning An,  Yong Zhang,  Li Liu,  Jialin Zhang,  Kun Kuang,  Lei Yang,  Zhixi Wu,  Lianchun Yu
School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
School of Cyber Science and Engineering, Dongguan University of Technology, Dongguan 523808, China
Institute of Computational Physics and Complex Systems, Lanzhou University, Lanzhou 730000, China
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China
Department of Physics, Xiamen University, Xiamen 361005, China
School of Big Data & Software Engineering, Chongqing University, Chongqing 400044, China
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
College of Computer Science and Technology, Zhejiang University, Hangzhou 310000, China
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China
Keywords:
intelligent machine, machine knowledge, human cognition, knowledge interpretation, principle of functional similarity, Probable Approximative Correction (PAC) model
Cite this article:
Li F, Li L, Yin J, et al. Machine Knowledge and Human Cognition. Big Data Mining and Analytics, 2020, 3(4): 292-299. https://doi.org/10.26599/BDMA.2020.9020009
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Abstract Full text About this article

Intelligent machines are knowledge systems with unique knowledge structure and function. In this paper, we discuss issues including the characteristics and forms of machine knowledge, the relationship between knowledge and human cognition, and the approach to acquire machine knowledge. These issues are of great significance to the development of artificial intelligence.


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About this article

Machine Knowledge and Human Cognition

Show Author's information Fashen Li, Lian Li, Jianping Yin, Liang Huang, Qingguo Zhou( ), Ning An, Yong Zhang, Li Liu, Jialin Zhang, Kun Kuang, Lei Yang, Zhixi Wu, Lianchun Yu
School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
School of Cyber Science and Engineering, Dongguan University of Technology, Dongguan 523808, China
Institute of Computational Physics and Complex Systems, Lanzhou University, Lanzhou 730000, China
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China
Department of Physics, Xiamen University, Xiamen 361005, China
School of Big Data & Software Engineering, Chongqing University, Chongqing 400044, China
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
College of Computer Science and Technology, Zhejiang University, Hangzhou 310000, China
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China

Abstract

Intelligent machines are knowledge systems with unique knowledge structure and function. In this paper, we discuss issues including the characteristics and forms of machine knowledge, the relationship between knowledge and human cognition, and the approach to acquire machine knowledge. These issues are of great significance to the development of artificial intelligence.

Keywords: intelligent machine, machine knowledge, human cognition, knowledge interpretation, principle of functional similarity, Probable Approximative Correction (PAC) model

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Publication history

Received: 24 June 2020
Accepted: 07 July 2020
Published: 16 November 2020
Issue date: December 2020

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© The authors 2020

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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