Artificial Intelligence

Neural networks excel at learning patterns from data. Symbolic AI excels at logical reasoning and interpretability. For decades, researchers have tried to combine them — with limited success. A new paper proposes an elegant mathematical framework that unifies both approaches: tensor networks. The key insight? Both neural and symbolic computations can be expressed as tensor decompositions, and inference in both reduces to tensor contractions. The Problem: Two Worlds That Don’t Talk Modern AI is split into two camps: ...

Imagine you have an army of helpers — several different Large Language Models (LLMs), each capable of handling tasks from simple queries to complex reasoning. But each helper costs something: time, compute, or actual money if you’re using an API. So the question is: Can we orchestrate these models wisely — starting from the cheapest one that might do the job, escalating only when needed — without exceeding a cost budget? ...