Published:
November 11, 2025
A new study led by CU Boulder’s Alvaro Velasquez—with UT Austin researchers Neel Bhatt and Ufuk Topcu among the co-authors—proposes a “neurosymbolic” approach to make artificial intelligence more efficient and accessible. By combining neural networks with symbolic reasoning, the team shows how AI can learn faster and require far less data and computing power.
The article explores how this hybrid method could make future AI systems more transparent, adaptable, and aligned with human-like reasoning. It also emphasizes the potential for neurosymbolic AI to reduce computational demands, opening the door to more sustainable and inclusive technological progress.
Check out the full article to learn more.