High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning. Researchers continually ...
A new research paper titled “Discovering faster matrix multiplication algorithms with reinforcement learning” was published by researchers at DeepMind. “Here we report a deep reinforcement learning ...
The Register on MSN
Berkeley boffins build better load balancing algo with AI
One way AI can improve on human work Computer scientists at UC Berkeley say that AI models show promise as a way to discover ...
With AlphaTensor, DeepMind Technologies has presented an AI system that is supposed to independently find novel, efficient and provably correct algorithms for complex mathematical tasks. AlphaTensor ...
Machine learning research is progressing at an ever-faster pace. We are likely still decades away from reaching the singularity, but AI has already become the buzzword that every tech company is ...
The 'algorithm for calculating the matrix product' that AlphaTensor worked on this time is used in various fields related to daily life, such as image processing, game graphics processing, weather ...
Rice University computer scientists have overcome a major obstacle in the burgeoning artificial intelligence industry by showing it is possible to speed up deep learning technology without specialized ...
Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. This article dives into the happens-before ...
A pair of researchers have found a more efficient way to multiply grids of numbers, beating a record set just a week ago by the artificial intelligence firm DeepMind. The company revealed on 5 October ...
Computer scientists have overcome a major obstacle in the burgeoning artificial intelligence industry. Rice University computer scientists have overcome a major obstacle in the burgeoning artificial ...
Results that may be inaccessible to you are currently showing.
Hide inaccessible results