Learn how enterprises evaluate open versus closed AI models to optimize costs, security, and performance across different business use cases.

Forecasting is a fundamentally new capability that is missing from the current purview of generative AI. Here's how Kumo is changing that.

This article discusses the three most common reliability prediction techniques for the failure rates of ICs and how safety ...

This letter proposes a passive-active model identification algorithm for affine discrete-time systems that integrates active model discrimination (AMD) and model invalidation (MI). A look-up tree ...

DeepSeek didn't reveal the source of the data it used to train the updated version of its R1 reasoning AI model, but some AI researchers speculate that at least a portion came from Google's Gemini ...

Data-driven deep learning techniques have made notable advancements in modeling electromagnetic scattering problems. However, its accuracy on the testing dataset can be heavily reduced when data ...

At the AI Action Summit in Paris earlier this year, Meta's chief AI scientist, Yann LeCun, said he'd like to see a world in which "we'll train our open-source platforms in a distributed fashion ...

The block model was generated using the Python programming language. First, the FMS and geological data were combined into a data frame. Afterward, a vacant block model was constructed, with ...

AI model uses transfer learning to forecast storm flooding in data-scarce areas by Courtney Sakry, Virginia Tech edited by Gaby Clark , reviewed by Alexander Pol ...