Users running a quantized 7B model on a laptop expect 40+ tokens per second. A 30B MoE model on a high-end mobile device ...

In machine learning, privacy risks often emerge from inference-based attacks. Model inversion techniques can reconstruct sensitive training data from model outputs. Membership inference attacks allow ...

Adding big blocks of SRAM to collections of AI tensor engines, or better still, a waferscale collection of such engines, ...

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.

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 ...

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

Mr. Conte is the co-founder and chief executive of Patreon. The World Wide Web was introduced some three decades ago. Describing in 2019 the hopes that had been projected onto the internet at its ...

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 ...