Abstract
To achieve the artificial general intelligence (AGI), imitate the intelligence? or imitate the brain? This is the question! Most artificial intelligence (AI) approaches set the understanding of the intelligence principle as their premise. This may be correct to implement specific intelligence such as computing, symbolic logic, or what the AlphaGo could do. However, this is not correct for AGI, because to understand the principle of the brain intelligence is one of the most difficult challenges for our human beings. It is not wise to set such a question as the premise of the AGI mission. To achieve AGI, a practical approach is to build the so-called neurocomputer, which could be trained to produce autonomous intelligence and AGI. A neurocomputer imitates the biological neural network with neuromorphic devices which emulate the bio-neurons, synapses and other essential neural components. The neurocomputer could perceive the environment via sensors and interact with other entities via a physical body. The philosophy under the “new” approach, so-called as imitationalism in this paper, is the engineering methodology which has been practiced for thousands of years, and for many cases, such as the invention of the first airplane, succeeded. This paper compares the neurocomputer with the conventional computer. The major progress about neurocomputer is also reviewed.
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This work was supported by the Natural Science Foundation of China (Nos. 61425025 and 61390515).
Recommended by Associate Editor Hong Qiao
Tie-Jun Huang received the B. Sc. and M. Sc. degrees in computer science from the Wuhan University of Technology, China in 1992 and 1995, respectively. He received the Ph. D. degree in pattern recognition and intelligent system from the Huazhong (Central China) University of Science and Technology, China in 1998. He received the National Science Fund for Distinguished Young Scholars of China in 2014, and was awarded the Distinguished Professor of the Chang Jiang Scholars Program by the Ministry of Education in 2015. He is a member of the Board of the Chinese Institute of Electronics and the Advisory Board of IEEE Computing Now. He is a professor at the School of Electronic Engineering and Computer Science, head of Department of Computer Science, Peking University.
His research interests include video coding and image understanding, especially neural coding inspired information coding theory in last years.
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Huang, TJ. Imitating the brain with neurocomputer a “new” way towards artificial general intelligence. Int. J. Autom. Comput. 14, 520–531 (2017). https://doi.org/10.1007/s11633-017-1082-y
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DOI: https://doi.org/10.1007/s11633-017-1082-y