Large Numerical and Mathematical Models for AI

The podcast explores the need for Large Numerical Models (LNMs) and Large Mathematics Models (LMMs) to complement Large Language Models (LLMs) in solving complex mathematical problems. LNMs would focus on precise numerical computation and simulations, while LMMs would handle symbolic reasoning and formal proofs. The pod examines existing tools and models that partially address these needs, discusses the feasibility of training such models given the availability of data and the structured nature of mathematics, and proposes an architecture where LLMs act as an interface between humans and the specialized mathematical models (LNMs and LMMs). The episode concludes that creating these specialized models would require further AI research breakthroughs beyond the current Transformer architecture.