Neurosymbolic AI as an antithesis to scaling laws.

Journal: PNAS nexus
Published Date: May 20, 2025

Abstract

The recent progress in machine learning has shifted the trends in artificial intelligence (AI) toward an overreliance on increasing amounts of data, computing power, and model parameters. These trends have resulted in success, but have also created a monolithic perspective for AI, increased the barriers to entry outside of large tech companies, and raised concerns about computational sustainability. Neurosymbolic AI is a growing area that promotes methodological heterogeneity and aims to push the frontiers of AI through affordable data and computing power.

Authors

  • Alvaro Velasquez
    Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, United States.
  • Neel Bhatt
    Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E 24th St, Austin, TX 78712, USA.
  • Ufuk Topcu
    Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E 24th St, Austin, TX 78712, USA.
  • Zhangyang Wang
    Departments of Electrical and Computer Engineering & Computer Science and Engineering Texas A&M University, College Station, TX 77840.
  • Katia Sycara
    Department of Machine Learning, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
  • Simon Stepputtis
    Department of Machine Learning, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
  • Sandeep Neema
    Department of Computer Science, Vanderbilt University, 1400 18th Ave S, Nashville, TN 37212, USA.
  • Gautam Vallabha
    Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, MD 20723, USA.

Keywords

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