Open AccessDeep Neural Networks in the View of Ecological Holism
Author(s) -
Qiang Yue,
Xun Yue
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/646/1/012022
Subject(s) - holism , cognition , artificial intelligence , perception , cognitive science , computer science , learnability , artificial neural network , representation (politics) , psychology , machine learning , ecology , neuroscience , politics , political science , law , biology
In the view point of ecological holism, deep neural networks can be view as a machine cognitive agent with overall multi-level nested structure, the representation learning and problem-solving ability of machine cognitive agent with multi-modal perception function is also the results from the perception and judgment of ambient situation environment. In the paper, a machine cognitive method based on ecological holism was deconstructed. Then, two core questions to characterize learnability of AlphaGo were answered, one is how to explain the characteristics of the hierarchical gradient and update parameters between different layers of networks? Two is how to understand the effective ability to choose the drop sampling at the strongest level of game of Go. These core controlling structural and functional roles proposed in this study may be further improved to a new type of artificial intelligence law of machine cognitive system.