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Investigating Laws of Intelligence Based on AI IQ Research

Author

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  • 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)

Abstract

Based on the authors’ previous work on the research on the evaluation of artificial intelligence (AI) intelligence quotient and the Standard Intelligent Model, this paper proposes three laws of intelligence for interpreting the concepts of intelligence, wisdom, consciousness, life and non-life. The first law is called “M Law of Intelligence” where any Agent can be regarded as a system that has the abilities to input, output, storage (master) and creative (innovate) knowledge. The second law is called “Ω Law of Intelligence” in which any Agent is affected by new “forces” FΩ and Fα derived in this paper, which evolve to Ωpoint whose knowledge processing ability is infinite, or converge to αpoint whose knowledge processing ability is zero. The third law is called “Α Law of Intelligence”, that is, when an Agent changes surrounding αpoint, the entire Universe relative to the Agent will also change between infinity and zero. The Three Laws need to be validated by a biochemical experiment method, an AI system intelligence evaluation experiment method or the computer program simulation experiment method.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:aodasc:v:7:y:2020:i:3:d:10.1007_s40745-020-00285-x
    DOI: 10.1007/s40745-020-00285-x
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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.
    2. Chaim Zins, 2007. "Conceptual approaches for defining data, information, and knowledge," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(4), pages 479-493, February.
    3. Wolfgang Enard & Molly Przeworski & Simon E. Fisher & Cecilia S. L. Lai & Victor Wiebe & Takashi Kitano & Anthony P. Monaco & Svante Pääbo, 2002. "Molecular evolution of FOXP2, a gene involved in speech and language," Nature, Nature, vol. 418(6900), pages 869-872, August.
    4. 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.
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    Cited by:

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    2. Yundong Gu & Dongfen Ma & Jiawei Cui & Zhenhua Li & Yaqi Chen, 2022. "Variable-Weighted Ensemble Forecasting of Short-Term Power Load Based on Factor Space Theory," Annals of Data Science, Springer, vol. 9(3), pages 485-501, June.
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    4. Elton G. Aráujo & Julio C. S. Vasconcelos & Denize P. Santos & Edwin M. M. Ortega & Dalton Souza & João P. F. Zanetoni, 2023. "The Zero-Inflated Negative Binomial Semiparametric Regression Model: Application to Number of Failing Grades Data," Annals of Data Science, Springer, vol. 10(4), pages 991-1006, August.
    5. Atabek Atabekov, 2023. "Artificial Intelligence in Contemporary Societies: Legal Status and Definition, Implementation in Public Sector across Various Countries," Social Sciences, MDPI, vol. 12(3), pages 1-21, March.
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