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"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 multiplication, conquering a 50-year-old record. This week, two Austrian researchers at Johannes Kepler University Linz claim they have bested that new record by one step."
Driven by the success of AlphaGo in defeating world champion Go players, Deep Mind researchers redesigned matrix multiplication as a board game, learnable through reinforcement learning. They trained AlphaTensor on this innovative state space. It successfully learned past matrix multiplication techniques and even designed its own, faster by 2 operations. This represents a huge breakthrough for artificial intelligence. It promises first acceleration of deep learning training as even a two step acceleration could lead to hundreds and even thousands less operations on large datasets. On a higher level, these results also demonstrate the capacity of machines to innovate beyond human learning. AlphaGo identified game playing strategies unknown to experts and for the first time GoogleAI was able to generalize this new learning to another domain. This innovation by AlphaTensor later improved on by Austrian researchers promises a future where machine and humans learn from each other.
Link to deep mind article discussing novel matrix multiplication optimization algorithm (AlphaTensor): https://www.deepmind.com/blog/discovering-novel-algorithms-with-alphatensor