We’re just a few years into the AI revolution, but AI systems are already improving decades-old computer science algorithms. Google’s AlphaEvolve AI, its latest coding agent for algorithm discovery, ...

Matrix multiplication (MatMul) is a fundamental operation in most neural networks, primarily because GPUs are highly optimized for these computations. Despite its critical role in deep learning, ...

Engheta and colleagues have now set their sights on vector–matrix multiplication, which is a vital operation for the artificial neural networks used in some artificial intelligence systems. The team ...

Lots of practical matrix multipliers end at a hard-wired implementation of small matrix multiplications. Having a more efficient "last mile" solution improves a whole range of other algorithms.

Learn how to simulate Markov chains using different methods, such as Monte Carlo, matrix multiplication, Gibbs sampling, and Metropolis-Hastings. Compare their pros and cons, and find out how to ...

Currently, when implementing matrix chain multiplication in Java and Python, there is no standardized library or built-in functionality specifically dedicated to efficiently solving this problem.

Video: DeepMind researchers trained an AI system called AlphaTensor to find new, faster algorithms for matrix multiplication. AlphaTensor quickly rediscovered — and surpassed, for some cases — the ...

What do encrypted messages, recognizing speech commands and running simulations to predict the weather have in common? They all rely on matrix multiplication for accurate calculations. DeepMind, an ...

DeepMind breaks 50-year math record using AI; new record falls a week later AlphaTensor discovers better algorithms for matrix math, inspiring another improvement from afar.