Matrix multiplication is at the heart of many machine learning breakthroughs, and it just got faster—twice.In 1969, a German mathematician named Volker Strassen discovered the previous-best algorithm for multiplying 4×4 matrices, which reduces the number of steps necessary to perform a matrix calculation.Going from 49 steps to 47 doesn't sound like much, but when you consider how many trillions of matrix calculations take place in a GPU every day, even incremental improvements can translate into large efficiency gains, allowing AI applications to run more quickly on existing hardware.When the player manages to do so, this results in a provably correct matrix multiplication algorithm for any pair of matrices, and its efficiency is captured by the number of steps taken to zero out the tensor.This week, Manuel Kauers and Jakob Moosbauer of Johannes Kepler University in Linz, Austria, published a paper claiming they have reduced that count by one, down to 95 multiplications."