IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v7y2020i3d10.1007_s40745-020-00285-x.html
   My bibliography  Save this article

Investigating Laws of Intelligence Based on AI IQ Research

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)

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-020-00285-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-020-00285-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hui Sun & Fanhui Zeng & Yang Yang, 2022. "Covert Factor’s Exploiting and Factor Planning," Annals of Data Science, Springer, vol. 9(3), pages 449-467, June.
    2. Xingsen Li & Junlin Zeng & Haitao Liu & Peizhuang Wang, 2022. "Intelligent Problem Solving Model and its Cross Research Directions Based on Factor Space and Extenics," Annals of Data Science, Springer, vol. 9(3), pages 469-484, June.
    3. Pejman Gholami-Dastgerdi & Mohammad-Reza Feizi-Derakhshi, 2023. "Part of Speech Tagging Using Part of Speech Sequence Graph," Annals of Data Science, Springer, vol. 10(5), pages 1301-1328, October.
    4. 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.
    5. Anda Tang & Pei Quan & Lingfeng Niu & Yong Shi, 2022. "A Survey for Sparse Regularization Based Compression Methods," Annals of Data Science, Springer, vol. 9(4), pages 695-722, August.
    6. Binxiang Jiang, 2022. "Research on Factor Space Engineering and Application of Evidence Factor Mining in Evidence-based Reconstruction," Annals of Data Science, Springer, vol. 9(3), pages 503-537, June.
    7. Xiangfu Meng & Jing Wen & Jiasheng Shi & Zihan Li & Jinxia Zhu & Peizhuang Wang, 2022. "Factor Query Language (FQL): A Fundamental Language for the Next Generation of Intelligent Database," Annals of Data Science, Springer, vol. 9(3), pages 539-554, June.
    8. 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.
    9. 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.
    10. Xueyan Xu & Fusheng Yu & Runjun Wan, 2023. "A Determining Degree-Based Method for Classification Problems with Interval-Valued Attributes," Annals of Data Science, Springer, vol. 10(2), pages 393-413, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Charles Ayoubi & Boris Thurm, 2023. "Knowledge diffusion and morality: Why do we freely share valuable information with Strangers?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 32(1), pages 75-99, January.
    2. Kai Yuan & Xumin Ni & Chang Liu & Yuwen Pan & Lian Deng & Rui Zhang & Yang Gao & Xueling Ge & Jiaojiao Liu & Xixian Ma & Haiyi Lou & Taoyang Wu & Shuhua Xu, 2021. "Refining models of archaic admixture in Eurasia with ArchaicSeeker 2.0," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    3. Vostrovský, V. & Tyrychtr, J. & Ulman, M., 2015. "Potential of Open Data in the Agricultural eGovernment," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 7(2), pages 1-11, June.
    4. Aven, Terje, 2013. "A conceptual framework for linking risk and the elements of the data–information–knowledge–wisdom (DIKW) hierarchy," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 30-36.
    5. Maymin, Philip Z., 2019. "Wage against the machine: A generalized deep-learning market test of dataset value," International Journal of Forecasting, Elsevier, vol. 35(2), pages 776-782.
    6. Liubertė Irina, 2019. "On Social Knowledge and Its Empirical Investigation in Contemporary Organisations," Management of Organizations: Systematic Research, Sciendo, vol. 81(1), pages 21-37, June.
    7. Darrin Baines & Robert J R Elliott, 2020. "Defining misinformation, disinformation and malinformation: An urgent need for clarity during the COVID-19 infodemic," Discussion Papers 20-06, Department of Economics, University of Birmingham.
    8. Tommaso Luzzati & Ilaria Tucci & Pietro Guarnieri, 2022. "Information overload and environmental degradation: learning from H.A. Simon and W. Wenders," Papers 2209.01039, arXiv.org.
    9. Raphaël Gellert, 2022. "Comparing definitions of data and information in data protection law and machine learning: A useful way forward to meaningfully regulate algorithms?," Regulation & Governance, John Wiley & Sons, vol. 16(1), pages 156-176, January.
    10. Luzzati, Tommaso & Tucci, Ilaria & Guarnieri, Pietro, 2022. "Information overload and environmental degradation: Learning from H.A. Simon and W. Wenders," Ecological Economics, Elsevier, vol. 202(C).
    11. 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.
    12. Pop Ioan G. & Talpos Mihai-Florin & Prisac Igor, 2015. "A Transdisciplinary Approach on the Advanced Sustainable Knowledge Integration," Balkan Region Conference on Engineering and Business Education, Sciendo, vol. 1(1), pages 1-11, November.
    13. László Barna Iantovics, 2021. "Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring," Mathematics, MDPI, vol. 9(6), pages 1-21, March.
    14. Darin Freeburg, 2019. "The Knowing Model: Facilitating Behaviour Change in Organisations," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1-22, December.
    15. Yury Nurulin & Inga Skvortsova & Iosif Tukkel & Marko Torkkeli, 2019. "Role of Knowledge in Management of Innovation," Resources, MDPI, vol. 8(2), pages 1-12, May.
    16. Alles, Michael & Gray, Glen L., 2016. "Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors," International Journal of Accounting Information Systems, Elsevier, vol. 22(C), pages 44-59.
    17. Rashid, Mehvish & Clarke, Paul M. & O’Connor, Rory V., 2019. "A systematic examination of knowledge loss in open source software projects," International Journal of Information Management, Elsevier, vol. 46(C), pages 104-123.
    18. Ljiljana Poljak Bilić & Kristina Posavec, 2024. "FAIRness of Research Data in the European Humanities Landscape," Publications, MDPI, vol. 12(1), pages 1-12, March.
    19. Gugissa, Desalegn A. & Ingenbleek, Paul T.M. & van Trijp, Hans C.M., 2021. "Market knowledge as a driver of sustainable use of common-pool resources: A lab-in-the-field study among pastoralists in Ethiopia," Ecological Economics, Elsevier, vol. 185(C).
    20. Ralph Hippe & Roger Fouquet, 2018. "The Knowledge Economy in Historical Perspective," World Economics, World Economics, 1 Ivory Square, Plantation Wharf, London, United Kingdom, SW11 3UE, vol. 19(1), pages 75-108, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aodasc:v:7:y:2020:i:3:d:10.1007_s40745-020-00285-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.