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Exploring the Convergence of AI, Machine Learning, and Deep Learning: A Canadian Perspective Through Citation Networks

In: Technology Management for Intelligent, Open and Responsible Organizations and Ecosystems

Author

Listed:
  • Fatemeh Naghavi Olya

    (Concordia University, CIISE)

  • Andrea Schiffauerova

    (Concordia University, CIISE)

  • Ashkan Ebadi

    (Concordia University, CIISE
    National Research Council Canada, Digital Technologies)

Abstract

The convergence of ideas, methods, and discoveries from various scientific disciplines and technological fields has led to the emergence of new technologies. This study analyses the convergence of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) with other technologies from 2000 to 2023, using published papers and citations. We explore how AI’s rapid development has fostered new technical subfields, focusing on Canada’s AI ecosystem. By examining the emergence, growth, and decline of these converged technologies, we aim to identify key contributing factors to highlight trends in technological advancement, inform strategic investments, and predict future innovations within Canada’s dynamic AI landscape.

Suggested Citation

  • Fatemeh Naghavi Olya & Andrea Schiffauerova & Ashkan Ebadi, 2026. "Exploring the Convergence of AI, Machine Learning, and Deep Learning: A Canadian Perspective Through Citation Networks," Springer Proceedings in Business and Economics, in: Fabiano Armellini & Syrine Njah & Elaine Mosconi & Breno Nunes (ed.), Technology Management for Intelligent, Open and Responsible Organizations and Ecosystems, pages 330-338, Springer.
    • Handle: RePEc:spr:prbchp:978-3-032-23282-3_40
    DOI: 10.1007/978-3-032-23282-3_40
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