A World-Self Model Towards Understanding Intelligence

Yue, Yutao ORCID: 0000-0003-4532-0924 (2022) A World-Self Model Towards Understanding Intelligence. IEEE ACCESS, 10. pp. 63034-63048.

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Official URL: http://dx.doi.org/10.1109/access.2022.3182389

Abstract

The symbolism, connectionism and behaviorism approaches of artificial intelligence have achieved a lot of successes in various tasks, while we still do not have a clear definition of -intelligence- with enough consensus in the community (although there are over 70 different -versions- of definitions). The nature of intelligence is still in darkness. In this work we do not take any of these three traditional approaches, instead we try to identify certain fundamental aspects of the nature of intelligence, and construct a mathematical model to represent and potentially reproduce these fundamental aspects. We first stress the importance of defining the scope of discussion and granularity of investigation. We carefully compare human and artificial intelligence, and qualitatively demonstrate an information abstraction process, which we propose to be the key to connect perception and cognition. We then present the broader idea of -concept-, separate the idea of self model out of the world model, and construct a new model called world-self model (WSM). We show the mechanisms of creating and connecting concepts, and the flow of how the WSM receives, processes and outputs information with respect to an arbitrary type of problem to solve. We also consider and discuss the potential computer implementation issues of the proposed theoretical framework, and finally we propose a unified general framework of intelligence based on WSM.

Item Type:	Article
Uncontrolled Keywords:	Human intelligence, Neurons, Computational modeling, Task analysis, Psychology, Licenses, Face recognition, Artificial general intelligence, concept, human intelligence, information abstraction, nature of intelligence, world-self model
Divisions:	Faculty of Science and Engineering > School of Physical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	13 Sep 2022 14:11
Last Modified:	15 Mar 2024 18:36
DOI:	10.1109/ACCESS.2022.3182389
Open Access URL:	https://ieeexplore.ieee.org/document/9794640
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3164552