QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

The small and complicated features of TSVs give rise to different defect types. Defects can form during any of the TSV process steps, which include lithography patterning followed by deep reactive ion ...

Quiq reports on the role of automation in customer service, highlighting tools like AI for questions, ticket classification, ...

For patients taking medications that don't work as expected or pharmaceutical companies struggling with clinical trial ...

WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

AI isn't a single capability, and "using AI" isn't a strategy. The strategy is to know what we're building, why it matters ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Artificial intelligence can feel like an abstract concept—something that happens inside massive data centers or behind the ...

As Slide 5 visualizes, our applications manifest as best-in-class hardware and software solutions, whereas platform manifests through connected workflows and ecosystems. By connecting the hardware and ...

TinyML sensors detect chainsaws, gunshots, and animal calls offline, offering a new way to protect wildlife in remote ...