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

Large language models have made remarkable strides in natural language processing, yet they still encounter difficulties when addressing complex planning and reasoning tasks. Traditional methods often ...

Large language models (LLMs) have demonstrated remarkable performance across multiple domains, driven by scaling laws highlighting the relationship between model size, training computation, and ...