PNAS Nexus 2025Representation & contrastive learning U Colorado Boulder The University of Texas at Austi Carnegie Mellon Vanderbilt Johns Hopkins Johns Hopkins University Applied

Neurosymbolic AI as an antithesis to scaling laws

Neurosymbolic AI challenges scaling laws by promoting affordable, heterogeneous methods that reduce data and compute demands.

Alvaro Velasquez, Neel Pratik Bhatt, Ufuk Topcu, Zhangyang Wang, Katia P. Sycara, Simon Stepputtis, Sandeep K. Neema, Gautam K. Vallabha

Published Apr 30, 20255 citationsPaper ↗

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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.