AI discovers a Faster Matrix Multiplication Algorithm

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Here is everything you need to know in 10 points:

1. DeepMind developed AlphaTensor, an AI system that discovers new algorithms for fundamental tasks like matrix multiplication.

2. AlphaTensor builds upon AlphaZero, the AI that mastered games like chess and Go, extending its approach to algorithmic discovery in mathematics.

3. Matrix multiplication is a core computing operation, essential for graphics, simulations, machine learning, and more, with efficiency improvements having wide-ranging impact.

4. Finding the most efficient way to multiply matrices has been a long-standing challenge, with significant progress last made in 1969 when Strassen discovered a faster method.

5. AlphaTensor reframes algorithm discovery as a single-player game, where the game's "board" is a three-dimensional tensor representing the correctness of the current algorithm.

6. The AI's "moves" correspond to mathematical operations that aim to reduce this tensor to zero, indicating a correct and efficient algorithm.

7. AlphaTensor navigates an astronomical number of possibilities—far exceeding the number of atoms in the universe—even for small matrices, using reinforcement learning and a specialized neural network architecture.

8. The AI rediscovered existing algorithms like Strassen's and also found new algorithms that are more efficient than any previously known.

9. AlphaTensor can tailor algorithms to specific hardware, such as GPUs or TPUs, leading to practical speedups of 10-20% in real-world applications.

10. This breakthrough demonstrates that AI can contribute to fundamental areas like algorithm design, potentially improving computational efficiency across many domains and opening up new possibilities for tackling other complex problems.