Making deep learning perform real algorithms with Category Theory
Published:Dec 22, 2025 15:01
•1 min read
•ML Street Talk Pod
Analysis
This article discusses the limitations of current Large Language Models (LLMs) and proposes Category Theory as a potential solution. It highlights that LLMs struggle with basic logical operations like addition, due to their pattern-recognition based architecture. The article suggests that Category Theory, a branch of abstract mathematics, could provide a more rigorous framework for AI development, moving it beyond its current 'alchemy' phase. The discussion involves experts like Andrew Dudzik, Petar Velichkovich, and others, who explain the concepts and limitations of current AI models. The core idea is to move from trial-and-error to a more principled engineering approach for AI.
Key Takeaways
- •LLMs currently struggle with basic logical operations like addition due to their pattern-recognition based architecture.
- •Category Theory is proposed as a mathematical framework to provide a more rigorous and principled approach to AI development.
- •The goal is to move AI development beyond its current 'alchemy' phase, which relies on trial-and-error.
Reference
“When you change a single digit in a long string of numbers, the pattern breaks because the model lacks the internal "machinery" to perform a simple carry operation.”