IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v68y2022ics0160791x22000343.html
   My bibliography  Save this article

Is AI intelligent? An assessment of artificial intelligence, 70 years after Turing

Author

Listed:
  • Hoffmann, Christian Hugo

Abstract

70 years ago Turing (1950, 1952), showcased his famous Imitation Game, which has come to be better known as the Turing Test. It proposed an evaluation procedure of intelligence in machines. The passage of time is perhaps reason enough to prompt the broad question: where do we stand today with regards to assessing intelligence in Artificial Intelligence (AI) systems? In this paper, we first contribute to more conceptual clarity by asking ourselves what AI and intelligence in AI is, and by comparing our answers to the latter to animal and human intelligence. We then aim to grasp the gist of the matter when we revisit Turing's proposal, criticize it, and finally inject basic requirements for a more robust and valid approach to evaluate AI systems in the future. In contrast to the standard Turing Test, which is neither valid nor robust, we propose that a measure or test of (machine) intelligence ought to lead to actionable as well as thriving research. Furthermore, the measure or test should be empirical, specific, relevant, expansive (for the specified scope), repeatable, solvable by exemplars, unpredictable, non-anthropomorphic, and, last but not least, non-binary.

Suggested Citation

  • Hoffmann, Christian Hugo, 2022. "Is AI intelligent? An assessment of artificial intelligence, 70 years after Turing," Technology in Society, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:teinso:v:68:y:2022:i:c:s0160791x22000343
    DOI: 10.1016/j.techsoc.2022.101893
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X22000343
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techsoc.2022.101893?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. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    2. Boada, Júlia Pareto & Maestre, Begoña Román & Genís, Carme Torras, 2021. "The ethical issues of social assistive robotics: A critical literature review," Technology in Society, Elsevier, vol. 67(C).
    3. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    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. Tamò-Larrieux, Aurelia & Ciortea, Andrei & Mayer, Simon, 2022. "Machine Capacity of Judgment: An interdisciplinary approach for making machine intelligence transparent to end-users," Technology in Society, Elsevier, vol. 71(C).
    2. Bratanova, Alexandra & Pham, Hien & Mason, Claire & Hajkowicz, Stefan & Naughtin, Claire & Schleiger, Emma & Sanderson, Conrad & Chen, Caron & Karimi, Sarvnaz, 2022. "Differentiating artificial intelligence activity clusters in Australia," Technology in Society, Elsevier, vol. 71(C).

    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. Tironi, Martín & Rivera Lisboa, Diego Ignacio, 2023. "Artificial intelligence in the new forms of environmental governance in the Chilean State: Towards an eco-algorithmic governance," Technology in Society, Elsevier, vol. 74(C).
    2. Yuchen Zhang & Wei Yang, 2022. "Breakthrough invention and problem complexity: Evidence from a quasi‐experiment," Strategic Management Journal, Wiley Blackwell, vol. 43(12), pages 2510-2544, December.
    3. Wilson, Christopher & van der Velden, Maja, 2022. "Sustainable AI: An integrated model to guide public sector decision-making," Technology in Society, Elsevier, vol. 68(C).
    4. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
    5. Agbodoh-Falschau, Kouassi Raymond & Ravaonorohanta, Bako Harinivo, 2023. "Investigating the influence of governance determinants on reporting cybersecurity incidents to police: Evidence from Canadian organizations’ perspectives," Technology in Society, Elsevier, vol. 74(C).
    6. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    7. Ostheimer, Julia & Chowdhury, Soumitra & Iqbal, Sarfraz, 2021. "An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles," Technology in Society, Elsevier, vol. 66(C).
    8. Boute, Robert N. & Gijsbrechts, Joren & van Jaarsveld, Willem & Vanvuchelen, Nathalie, 2022. "Deep reinforcement learning for inventory control: A roadmap," European Journal of Operational Research, Elsevier, vol. 298(2), pages 401-412.
    9. Wang, Feipeng & Wong, Wing-Keung & Wang, Zheng & Albasher, Gadah & Alsultan, Nouf & Fatemah, Ambreen, 2023. "Emerging pathways to sustainable economic development: An interdisciplinary exploration of resource efficiency, technological innovation, and ecosystem resilience in resource-rich regions," Resources Policy, Elsevier, vol. 85(PA).
    10. Zhou, Yuhao & Wang, Yanwei, 2022. "An integrated framework based on deep learning algorithm for optimizing thermochemical production in heavy oil reservoirs," Energy, Elsevier, vol. 253(C).
    11. Mandal, Ankit & Tiwari, Yash & Panigrahi, Prasanta K. & Pal, Mayukha, 2022. "Physics aware analytics for accurate state prediction of dynamical systems," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    12. Metta, Matteo & Ciliberti, Stefano & Obi, Chinedu & Bartolini, Fabio & Klerkx, Laurens & Brunori, Gianluca, 2022. "An integrated socio-cyber-physical system framework to assess responsible digitalisation in agriculture: A first application with Living Labs in Europe," Agricultural Systems, Elsevier, vol. 203(C).
    13. Bossert, Leonie & Hagendorff, Thilo, 2021. "Animals and AI. The role of animals in AI research and application – An overview and ethical evaluation," Technology in Society, Elsevier, vol. 67(C).
    14. Yang, Zhengzhi & Zheng, Lei & Perc, Matjaž & Li, Yumeng, 2024. "Interaction state Q-learning promotes cooperation in the spatial prisoner's dilemma game," Applied Mathematics and Computation, Elsevier, vol. 463(C).
    15. Zhang, Yihao & Chai, Zhaojie & Lykotrafitis, George, 2021. "Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    16. Jun Xu, 2024. "AI in ESG for Financial Institutions: An Industrial Survey," Papers 2403.05541, arXiv.org.
    17. Jun Li & Wei Zhu & Jun Wang & Wenfei Li & Sheng Gong & Jian Zhang & Wei Wang, 2018. "RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-18, November.
    18. Keller, Alexander & Dahm, Ken, 2019. "Integral equations and machine learning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 161(C), pages 2-12.
    19. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
    20. Zhang, Guangming & Zhang, Chao & Wang, Wei & Cao, Huan & Chen, Zhenyu & Niu, Yuguang, 2023. "Offline reinforcement learning control for electricity and heat coordination in a supercritical CHP unit," Energy, Elsevier, vol. 266(C).

    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:eee:teinso:v:68:y:2022:i:c:s0160791x22000343. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/technology-in-society .

    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.