Introduction

The paper introduces AlphaTensor, the first AI system for discovering novel and efficient algorithms for matrix multiplication. The authors highlight the impact of even minor improvements in matrix multiplication efficiency, as it is a core component in many computational tasks.

Background

The authors discuss the history of algorithmic discovery, from the ancient Egyptians to present-day AI systems. They highlight the challenges of discovering efficient matrix multiplication algorithms, despite decades of research following Strassen's breakthrough.

The Process and Progress of Automating Algorithmic Discovery

The authors explain how they converted the problem of finding efficient matrix multiplication algorithms into a single-player game. They highlight the challenge of exploring the vast space of possible algorithms and the superior performance of AlphaTensor compared to previous approaches.

Leveraging Diversity for Hardware-Specific Acceleration

The authors discuss how AlphaTensor can be adapted to find algorithms fast on specific hardware, such as Nvidia V100 GPU and Google TPU v2. They highlight the potential impact of AlphaTensor-discovered algorithms on various computational tasks, such as neural network training and scientific computing.

Conclusion

The authors conclude by highlighting the potential of AI systems such as AlphaTensor to guide algorithmic discovery for fundamental computational tasks beyond matrix multiplication. They also emphasize the power of AlphaZero algorithms, which can be extended beyond traditional games to solve open problems in mathematics.