The generative AI revolution faces a multi-trillion-dollar challenge: the soaring cost of inference, or running AI models.

A team of researchers developed “parallel optical matrix-matrix multiplication” (POMMM), which could revolutionize tensor ...

Analog computers are systems that perform computations by manipulating physical quantities such as electrical current, that map math variables, instead of representing information using abstraction ...

Analogue computers that rapidly solve a key type of equation used in training artificial intelligence models could offer a potential solution to the growing energy consumption in data centres caused ...

As AI automates more knowledge work, the organizations that thrive will be those that master human relationships. Matrix organizations present well-known challenges: difficulty influencing across ...

This test was disabled because it is failing in CI. See recent examples and the most recent trunk workflow logs. Over the past 3 hours, it has been determined flaky in 7 workflow(s) with 14 failures ...

AlphaEvolve uses large language models to find new algorithms that outperform the best human-made solutions for data center management, chip design, and more. Google DeepMind has once again used large ...

Abstract: The computation of matrix pseudoinverses is a recurrent requirement across various scientific computing and engineering domains. The prevailing models for matrix pseudoinverse typically ...

Abstract: The article discusses the development and study of a new matrix-based hybrid genetic algorithm (MBHGA) for solving an agent-based model of firms’ behavior with controlled trade interactions.

On the test, American fourth and eighth graders posted results similar to scores from 1995. It was a sign of notable stagnation, even as other countries saw improvements. By Dana Goldstein American ...