Every major economy that is not the United States or China, which has a disproportionate share of HPC national labs as well ...

In this article, we propose a novel spectral tensor layer for communication-free distributed deep learning. The overall framework is as follows: first, we represent the data in tensor form (instead of ...

April 17, 2025: OpenAI has released o3 and 04-mini, two reasoning AI models designed to be extra good at programming, math, ...

We always enjoy [FloatHeadPhysics] explaining any math or physics topic. We don’t know if he’s acting or not, but he seems genuinely excited about every topic he covers, and it is ...

Reconfigurable architectures may help reduce the risk of stranded assets in the fast-moving AI space. Despite its potential, flexible AI hardware still faces technical and economic challenges in ...

Robust tensor completion, which aims to recover a tensor from partial observations corrupted by Gaussian noise and sparse noise simultaneously, has a wide range of applications in visual data recovery ...

Zodiacs & Astrology News: Discover which zodiac signs possess exceptional mathematical skills. Learn how Capricorn, Virgo, Scorpio, Aquarius, and Gemini are adept at math and the unique traits ...

The Little-Known Chip Powering the Very Future of AI” was previously published in June 2025 with the title, “How Google’s TPU Is Powering the Very Future of AI.” It has since been updated to include ...

Both the Pixel 10 Pro and Pixel 10 Pro XL will be powered by Google’s new Tensor G5 system-on-chip and feature identical camera configurations across all models.

TPU v3, for example, can train BERT, Google’s game-changing language model, roughly eight times quicker than Nvidia’s older V100 GPU. TPU v4 took things up a notch, squeezing ...

A classic puzzle conundrum goes like this: You’re in a room with two ropes and a box of matches. Each rope takes exactly an hour to burn all the way across, but it might burn faster through some ...

We investigate a novel approach to approximate tensor-network contraction via the exact, matrix-free decomposition of full tensor-networks. We study this method as a means to eliminate the propagat ...