Brain-inspired and Self-based Artificial Intelligence
Yi Zeng, Feifei Zhao, Yuxuan Zhao, Dongcheng Zhao, Enmeng Lu, Qian Zhang, Yuwei Wang, Hui Feng, Zhuoya Zhao, Jihang Wang, Qingqun Kong, Yinqian Sun, Yang Li, Guobin Shen, Bing Han, Yiting Dong, Wenxuan Pan, Xiang He, Aorigele Bao, Jin Wang

TL;DR
This paper proposes a brain-inspired, self-based AI paradigm that integrates hierarchical Self concepts to enable more human-like understanding, perception, and autonomous interaction, aiming toward artificial general intelligence.
Contribution
It introduces a novel hierarchical Self framework for AI, emphasizing self-perception, autonomous interaction, and social collaboration to advance toward human-level intelligence.
Findings
Hierarchical Self framework enhances AI perception and learning.
Self-based interactions improve environment adaptation.
Mutual promotion among Self levels supports consciousness in AI.
Abstract
The question "Can machines think?" and the Turing Test to assess whether machines could achieve human-level intelligence is one of the roots of AI. With the philosophical argument "I think, therefore I am", this paper challenge the idea of a "thinking machine" supported by current AIs since there is no sense of self in them. Current artificial intelligence is only seemingly intelligent information processing and does not truly understand or be subjectively aware of oneself and perceive the world with the self as human intelligence does. In this paper, we introduce a Brain-inspired and Self-based Artificial Intelligence (BriSe AI) paradigm. This BriSe AI paradigm is dedicated to coordinating various cognitive functions and learning strategies in a self-organized manner to build human-level AI models and robotic applications. Specifically, BriSe AI emphasizes the crucial role of the Self…
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Taxonomy
TopicsEEG and Brain-Computer Interfaces
MethodsAttentive Walk-Aggregating Graph Neural Network
