AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (5 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Machine Knowledge and Human Cognition

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
Show Author Information

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.

References

[1]
J. Pearl, Theoretical impediments to machine learning with seven sparks from the causal revolution, arXiv preprint arXiv:1801.04016, 2018.
[2]
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, et al., Mastering the game of Go without human knowledge, Nature, vol. 550, no. 7676, pp. 354-359, 2017.
[3]
I. Newton, The Principia: Mathematical Principles of Natural Philosophy. Beijing, China: Peking University Press, 2013.
[4]
L. Valiant, Probably Approximately Correct, Natures Algorithms for Learning and Prospering in a Complex World. New York, NY, USA: Basic Books, 2013.
[5]
T. Marwala, Rubin, Pearl and Granger causality models: A unified view, https://doi.org/10.1142/9789814630870_0006, 2015
[6]
J. Pearl and D. Mackenzie, The Book of Why. London, UK: Allen Lane, 2018.
[7]
C. Molnar, Interpretable machine learning: A guide for making black box models explainable, https://christophm.github.io/interpretable-ml-book/, 2020.
Big Data Mining and Analytics
Pages 292-299
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

1276

Views

131

Downloads

3

Crossref

3

Web of Science

5

Scopus

0

CSCD

Altmetrics

Received: 24 June 2020
Accepted: 07 July 2020
Published: 16 November 2020
© The authors 2020

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/).

Return