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DeepMind Invents Faster Algorithms to Solve Tough Math Puzzles

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matrix multiplication representation, illustration

Previous algorithms required 80 individual multiplications to multiply a 4x5 matrix by a 5x5. AlphaTensor needed 76.

Credit: DeepMind

Researchers at artificial intelligence (AI) laboratory DeepMind have created an algorithm that can solve tough mathematical calculations with improved computing efficiency.

The AlphaTensor algorithm, described in the journal Nature, was designed to execute matrix multiplication, which entails multiplying numbers arranged in grids that might represent data. AlphaTensor incorporates reinforcement learning as well as tree search, a game-playing approach in which the AI probes the outcomes of branching possibilities while planning its next action.

AlphaTensor was tested on input matrices up to 5 x 5. In some cases it rediscovered shortcuts previously formulated by mathematicians, while in others it found new shortcuts of its own.

From Nature
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