Extracting and analyzing relevant medical information from large-scale databases such as biobanks poses considerable challenges. To exploit such "big data," attempts have focused on large sampling ...

After years of rapid advancement in cloud‑centric AI training and inference, the industry is reaching an edge AI tipping point.

New algorithm maps hidden global and genetic diversity in vaginal microbiomes, offering precision tools for reproductive health research.

A new technical paper titled “Advances in You Only Look Once (YOLO) algorithms for lane and object detection in autonomous ...

A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...

ABSTRACT: A new conceptual framework is presented that unifies Gödel’s incompleteness theorems with practical physical modeling through information-theoretic analysis. The method of variables with ...

Google launched four official and confirmed algorithmic updates in 2025, three core updates and one spam update. This is in comparison to last year, in 2024, where we had seven confirmed updates, then ...

Abstract: This article introduces a scalable distributed probabilistic inference algorithm for intelligent sensor networks, tackling challenges of continuous variables, intractable posteriors, and ...

Abstract: Although AI has been extensively adopted and has profoundly transformed our lives, it is not feasible to directly deploy large AI models on edge devices with limited resources. To enhance ...

Just because the center of gravity for GenAI compute and other kinds of machine learning and data analytics has shifted from the CPU to the XPU accelerator – generally a GPU these days, but not ...

Artificial intelligence (AI) involves intense computing and tons of data. The computing may be performed by CPUs, GPUs, or dedicated accelerators, and while the data travels through DRAM on its way to ...