[1]ZHONG Yixin.Leading of paradigm shift and undertaking of information conversion: theoretical essence of mechanism-based general AI[J].CAAI Transactions on Intelligent Systems,2020,15(3):615-622.[doi:10.11992/tis.202002019]
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Leading of paradigm shift and undertaking of information conversion: theoretical essence of mechanism-based general AI

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 3 Page number: 615-622 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2020-05-05
Title:
Leading of paradigm shift and undertaking of information conversion: theoretical essence of mechanism-based general AI
Author(s):
ZHONG Yixin
AI School, Beijing University of Posts and Telecommunications, Beijing 100876, China
Keywords:
artificial intelligence (AI); paradigm shift; information conversion; global theory; universal theory; mechanism for intelligence growth; explicit factor; implicit factor
CLC:
TP18
DOI:
10.11992/tis.202002019
Abstract:
Because of the employment of scientific paradigm (i.e., scientific outlook and methodology) in the field of materials science, the global research on artificial intelligence (AI) has been divided into three branches, namely, structuralism-based AI (e.g., artificial neural networks), functionalism-based AI (e.g., physical symbol systems and expert systems), and behaviorism-based AI (e.g., sensorimotor systems and intelligent robots), which are independent from each other and mutually unharmonious. A decent number of results from each of the three branches have been achieved; however, no progress has been made in the global theory of AI, let alone the universal theory of AI, and this has become the biggest pain point in the research and development of AI. Presently, the universal and global theories of AI have gradually become urgent social demands. Accordingly, the article titled “The theory of mechanistic general artificial intelligence” has been presented on the basis of the research experiences of the author during the past four decades; the work mainly focuses on “paradigm shift” and “information conversion.” The author hopes that the views and results presented in the article could draw discussions and criticisms from the readers of the AI academic circle.
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