Large language models such as ChaptGPT have proven to be able to produce remarkably intelligent results, but the energy and monetary costs associated with running these massive algorithms is sky high.

AI training time is at a point in an exponential where more throughput isn't going to advance functionality much at all. The underlying problem, problem solving by training, is computationally ...

A team of researchers developed “parallel optical matrix-matrix multiplication” (POMMM), which could revolutionize tensor ...

Researchers from the USA and China have presented a new method for optimizing AI language models. The aim is for large language models (LLMs) to require significantly less memory and computing power ...

New lower values for p get discovered all the time (maybe once a year). It is conjectured that they will approach 2.0 without ever getting quite to it. Somehow Quanta Mag heard about the new result ...