Nature

Optimizing machine learning for network inference through comparative analysis of model performance in synthetic and real-world networks

In an era marked by exponential data growth and pervasive digital connectivity, understanding the intricate structure of networks has become a critical challenge across various domains. Whether ...
Nature

High computational density nanophotonic media for machine learning inference

Efficient machine learning inference is essential for the rapid adoption of artificial intelligence (AI) across various domains. On-chip optical computing has emerged as a transformative solution due ...
VentureBeat

Together AI's ATLAS adaptive speculator delivers 400% inference speedup by learning from workloads in real-time

Enterprises expanding AI deployments are hitting an invisible performance wall. The culprit? Static speculators that can't keep up with shifting workloads. Speculators are smaller AI models that work ...
CMS Wire

Artificial General Intelligence: Jumping to the New Inference Market S-Curve

Historically, we have used the Turing test as the measurement to determine if a system has reached artificial general intelligence. Created by Alan Turing in 1950 and originally called the “Imitation ...
Forbes

The Inference Difference: Why Clunky Data Engineering Unhinges AI

Forbes contributors publish independent expert analyses and insights. I track enterprise software application development & data management. AI has a shiny front end. As everyone who’s used an ...
SDxCentral

Nvidia, hyperscaler-backed open standard for AI inference torch passed to Linux Foundation

An open standard for AI inference backed by Google Cloud, IBM, Red Hat, Nvidia and more was given to the Linux Foundation for stewardship in further proof training has been superseded by inference in ...