Abstract
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.
Recommended Citation
L. Zhao and L. Zhang and Z. Wu and Y. Chen and H. Dai and X. Yu and Z. Liu and T. Zhang and X. Hu and X. Jiang and X. Li and D. Zhu and D. Shen and T. Liu, "When Brain-inspired AI Meets AGI," Meta Radiology, vol. 1, no. 1, article no. 100005, Elsevier, Jun 2023.
The definitive version is available at https://doi.org/10.1016/j.metrad.2023.100005
Department(s)
Computer Science
Publication Status
Open Access
International Standard Serial Number (ISSN)
2950-1628
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Elsevier, All rights reserved.
Publication Date
01 Jun 2023
