How DeepMind's AlphaTensor AI Devised a Faster Matrix Multiplication

After developing an artificial intelligence that can achieve superhuman mastery of games like chess and go, in addition to another AI that can predict how proteins fold themselves in three-dimensional space, the researchers over at DeepMind have done it again -- this time using a deep learning AI model to efficiently solve a fundamental mathematics problem, while beating a 50-year-old record to boot. In a blog post from earlier this month, the DeepMind team introduces AlphaTensor, an AI system that is designed for discovering new and more efficient algorithms for solving crucial mathematical operations -- in this case, matrix multiplication. Whether they are used to process or compress images or video, recognizing spoken commands, or running simulations to predict the weather, matrix multiplication underpins much of modern computing. So it's little wonder that experts and companies all over the world are constantly looking for more efficient ways to improve the algorithms for solving these mathematical operations behind such tasks. Matrix multiplication is one of the simplest mathematical operations in algebra, where individual numbers that are arranged in grids -- or matrices -- are multiplied together and then added in specific way in order to generate a new matrix.