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Updated: May 23, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Neurosymbolic AI as an antithesis to scaling laws
Alvaro Velasquez1, Neel Bhatt2, Ufuk Topcu2
1Department of Computer Science, University of Colorado Boulder, 430 UCB, 1111 Engineering Dr, Boulder, CO 80309, USA.
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.
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