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Zhao, L., Zhang, L., Wu, Z., Chen, Y., Dai, H., & Yu, X., et al. (2023). When brain-inspired AI meets AGI. Meta-Radiology, 1(1), 100005. Added by: alexb44 (26/08/2026, 12:00) Last edited by: alexb44 (26/08/2026, 12:01) |
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| Resource type: Journal Article Published BibTeX citation key: Zhao2023 Email resource to friend View all bibliographic details  |
Categories: AI/Machine Learning Keywords: Artificial General Intelligence, Artificial Intelligence Creators: Chen, Dai, Hu, Jiang, Liu, others, Wu, Yu, Zhang, Zhang, Zhao Collection: Meta-Radiology |
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| 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.
Added by: alexb44 |