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Intelligence Quotient and Intelligence Grade of Artificial Intelligence

Author

Listed:
  • Feng Liu

    (The Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Yong Shi

    (The Chinese Academy of Sciences
    Chinese Academy of Sciences
    University of Nebraska at Omaha
    University of Chinese Academy of Sciences)

  • Ying Liu

    (University of Chinese Academy of Sciences)

Abstract

Although artificial intelligence (AI) is currently one of the most interesting areas in scientific research, the potential threats posed by emerging AI systems remain a source of persistent controversy. To address the issue of AI threat,this study proposes a “standard intelligence model” that unifies AI and human characteristics in terms of four aspects of knowledge, i.e., input, output, mastery, and creation. Using this model, we observe three challenges, namely, expanding of the von Neumann architecture; testing and ranking the intelligence quotient (IQ) of naturally and artificially intelligent systems, including humans, Google, Microsoft’s Bing, Baidu, and Siri; and finally, the dividing of artificially intelligent systems into seven grades from robots to Google Brain. Based on this, we conclude that Google’s AlphaGo belongs to the third grade.

Suggested Citation

  • Feng Liu & Yong Shi & Ying Liu, 2017. "Intelligence Quotient and Intelligence Grade of Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 179-191, June.
    • Handle: RePEc:spr:aodasc:v:4:y:2017:i:2:d:10.1007_s40745-017-0109-0
    DOI: 10.1007/s40745-017-0109-0
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    References listed on IDEAS

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    1. Feng Liu & Yong Shi & Bo Wang, 2015. "World Search Engine IQ Test Based on the Internet IQ Evaluation Algorithms," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(02), pages 221-237.
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    Cited by:

    1. Feng Liu & Yong Shi, 2020. "Investigating Laws of Intelligence Based on AI IQ Research," Annals of Data Science, Springer, vol. 7(3), pages 399-416, September.
    2. László Barna Iantovics, 2021. "Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring," Mathematics, MDPI, vol. 9(6), pages 1-21, March.

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    1. Feng Liu & Yong Shi, 2020. "Investigating Laws of Intelligence Based on AI IQ Research," Annals of Data Science, Springer, vol. 7(3), pages 399-416, September.

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