The focus of artificial-intelligence spending has gone from training models to using them. Here’s how to understand the ...
Machine-learning inference started out as a data-center activity, but tremendous effort is being put into inference at the edge. At this point, the “edge” is not a well-defined concept, and future ...
This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we ...
Artificial intelligence (AI) is a powerful force for innovation, transforming the way we interact with digital information. At the core of this change is AI inference. This is the stage when a trained ...
Post by Panayiota (Pani) Kendeou & Kristen McMaster, University of Minnesota Making Inferences: The Cornerstone of Reading Comprehension Despite the persistent efforts of researchers, policy makers, ...
The world of artificial intelligence and machine learning (AI/ML) is fragmented into different domains. Two of these domains represent splits between training and inference, and cloud versus edge.
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