Linear algebra is the hidden language of artificial intelligence, powering everything from neural networks to dimensionality reduction. Mastering concepts like vectors, matrices, eigenvalues, and ...

For Sarthak Dassarma ’26, mathematics isn’t a set of rules to memorize—it’s a story, and the Putnam Competition is just his ...

SALT LAKE CITY, March 26, 2026 /PRNewswire/ -- Intactis Bio Corp, a leader in biohybrid computing, announced a major milestone: in a controlled laboratory setting, living brain cells (neurons) ...

The saying “round pegs do not fit square holes” persists because it captures a deep engineering reality: inefficiency most often arises not from flawed components, but from misalignment between a ...

What’s the difference between a GPU and a TPU? It’s a wonkish question, to be sure, but one that has a lot of interesting applications to the AI arms race, where companies are trying to be the go-to ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

BUFFALO, N.Y. — Last year, onlookers observed a startling site on China’s Qiantang River: waves forming a grid-like pattern. Dubbed the “matrix tide,” this complex wave pattern was caused by the river ...

Google DeepMind today pulled the curtain back on AlphaEvolve, an artificial-intelligence agent that can invent brand-new computer algorithms — then put them straight to work inside the company's vast ...

We have said it before, and we will say it again right here: If you can make a matrix math engine that runs the PyTorch framework and the Llama large language model, both of which are open source and ...

In a move that directly challenges Nvidia in the lucrative AI training and inference markets, Intel announced its long-anticipated new Intel Gaudi 3 AI accelerator at its Intel Vision event. The new ...

Computer scientists have discovered a new way to multiply large matrices faster by eliminating a previously unknown inefficiency, leading to the largest improvement in matrix multiplication efficiency ...