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Quantifying the progress of artificial intelligence subdomains using the patent citation network

Author

Listed:
  • Reza Rezazadegan

    (Sharif University of Technology
    Shiraz University)

  • Mahdi Sharifzadeh

    (Sharif University of Technology)

  • Christopher L. Magee

    (Massachusetts Institute of Technology (MIT))

Abstract

Even though Artificial Intelligence (AI) has been having a transformative effect on human life, there is currently no precise quantitative method for measuring and comparing the performance of different AI methods. Technology Improvement Rate (TIR) is a measure that describes a technology’s rate of performance improvement, and is represented in a generalization of Moore’s Law. Estimating TIR is important for R&D purposes to forecast which competing technologies have a higher chance of success in the future. The present contribution estimates the TIR for different subdomains of applied and industrial AI by quantifying each subdomain’s centrality in the global flow of technology, as modeled by the Patent Citation Network and shown in previous work. The estimated TIR enables us to quantify and compare the performance improvement of different AI methods. We also discuss the influencing factors behind slower or faster improvement rates. Our results highlight the importance of Rule-based Machine Learning (not to be confused with Rule-based Systems), Multi-task Learning, Meta-Learning, and Knowledge Representation in the future advancement of AI and particularly in Deep Learning.

Suggested Citation

  • Reza Rezazadegan & Mahdi Sharifzadeh & Christopher L. Magee, 2024. "Quantifying the progress of artificial intelligence subdomains using the patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(5), pages 2559-2581, May.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:5:d:10.1007_s11192-024-04996-3
    DOI: 10.1007/s11192-024-04996-3
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    References listed on IDEAS

    as
    1. Christopher L. Benson & Christopher L. Magee, 2015. "Technology structural implications from the extension of a patent search method," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 1965-1985, March.
    2. Christopher L Benson & Christopher L Magee, 2015. "Quantitative Determination of Technological Improvement from Patent Data," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-23, April.
    3. Na Liu & Philip Shapira & Xiaoxu Yue, 2021. "Tracking developments in artificial intelligence research: constructing and applying a new search strategy," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3153-3192, April.
    4. Soyea Lee & Junseok Hwang & Eunsang Cho, 2022. "Comparing technology convergence of artificial intelligence on the industrial sectors: two-way approaches on network analysis and clustering analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 407-452, January.
    5. Vilhelm Verendel, 2023. "Tracking artificial intelligence in climate inventions with patent data," Nature Climate Change, Nature, vol. 13(1), pages 40-47, January.
    6. Martin Meyer, 2000. "What is Special about Patent Citations? Differences between Scientific and Patent Citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 49(1), pages 93-123, August.
    7. Stefano Baruffaldi & Brigitte van Beuzekom & Hélène Dernis & Dietmar Harhoff & Nandan Rao & David Rosenfeld & Mariagrazia Squicciarini, 2020. "Identifying and measuring developments in artificial intelligence: Making the impossible possible," OECD Science, Technology and Industry Working Papers 2020/05, OECD Publishing.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Artificial intelligence; Technological forecasting; Moore’s law; Technology improvement rate; Complex networks; Centrality; Deep learning;
    All these keywords.

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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