Researchers claim to have developed a new way to run AI language models more efficiently by eliminating matrix multiplication from the process. This fundamentally redesigns neural network operations ...

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 ...

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.

Algorithms have been used throughout the world’s civilizations to perform fundamental operations for thousands of years. However, discovering algorithms is highly challenging. Matrix multiplication is ...

There has been an ever-growing demand for artificial intelligence and fifth-generation communications globally, resulting in very large computing power and memory requirements. The slowing down or ...

With AlphaTensor, DeepMind Technologies has presented an AI system that is supposed to independently find novel, efficient and provably correct algorithms for complex mathematical tasks. AlphaTensor ...

Microsoft's research team has announced that they have succeeded in drastically reducing the computational cost of large-scale language models by setting the model weights to only three values: ``-1'' ...

The new version of AlphaZero discovered a faster way to do matrix multiplication, a core problem in computing that affects thousands of everyday computer tasks. DeepMind has used its board-game ...

A new technical paper titled “Scalable MatMul-free Language Modeling” was published by UC Santa Cruz, Soochow University, UC Davis, and LuxiTech. “Matrix multiplication (MatMul) typically dominates ...

Matrix multiplication is at the heart of many machine learning breakthroughs, and it just got faster—twice. Last week, DeepMind announced it discovered a more efficient way to perform matrix ...