When brain-inspired AI meets AGI

Lin Zhao; Lu Zhang; Zihao Wu; Yuzhong Chen; Haixing Dai; Xiaowei Yu; Zhengliang Liu; Tuo Zhang; Xintao Hu; Xi Jiang; Xiang Li; Dajiang Zhu; Dinggang Shen; Tianming Liu

Meta-Radiology (Jun 2023)

When brain-inspired AI meets AGI

Lin Zhao,
Lu Zhang,
Zihao Wu,
Yuzhong Chen,
Haixing Dai,
Xiaowei Yu,
Zhengliang Liu,
Tuo Zhang,
Xintao Hu,
Xi Jiang,
Xiang Li,
Dajiang Zhu,
Dinggang Shen,
Tianming Liu

Affiliations

Lin Zhao: School of Computing, The University of Georgia, Athens 30602, USA
Lu Zhang: Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington 76019, USA
Zihao Wu: School of Computing, The University of Georgia, Athens 30602, USA
Yuzhong Chen: MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
Haixing Dai: School of Computing, The University of Georgia, Athens 30602, USA
Xiaowei Yu: Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington 76019, USA
Zhengliang Liu: School of Computing, The University of Georgia, Athens 30602, USA
Tuo Zhang: School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
Xintao Hu: School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
Xi Jiang: MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
Xiang Li: Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston 02115, USA
Dajiang Zhu: Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington 76019, USA
Dinggang Shen: School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China; Shanghai Clinical Research and Trial Center, Shanghai, 201210, China
Tianming Liu: School of Computing, The University of Georgia, Athens 30602, USA; Corresponding author.

Journal volume & issue: Vol. 1, no. 1
p. 100005

Abstract

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Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI researchers draw inspiration from the human brain and seek to replicate its principles in intelligent machines. Brain-inspired artificial intelligence is a field that has emerged from this endeavor, combining insights from neuroscience, psychology, and computer science to develop more efficient and powerful AI systems. In this article, we provide a comprehensive overview of brain-inspired AI from the perspective of AGI. We begin with the current progress in brain-inspired AI and its extensive connection with AGI. We then cover the important characteristics for both human intelligence and AGI (e.g., scaling, multimodality, and reasoning). We discuss important technologies toward achieving AGI in current AI systems, such as in-context learning and prompt tuning. We also investigate the evolution of AGI systems from both algorithmic and infrastructural perspectives. Finally, we explore the limitations and future of AGI.

Published in Meta-Radiology

ISSN: 2950-1628 (Online)
Publisher: KeAi Communications Co., Ltd.
Country of publisher: China
LCC subjects: Medicine: Medicine (General): Medical physics. Medical radiology. Nuclear medicine
Website: https://www.keaipublishing.com/en/journals/meta-radiology/

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