Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

One would imagine that an AI capable of solving the hardest Olympiad problems would naturally produce novel scientific ...

AI systems are beginning to produce proof ideas that experts take seriously, even when final acceptance is still pending.

Researchers have significantly enhanced an artificial intelligence tool used to rapidly detect bacterial contamination in ...

The small and complicated features of TSVs give rise to different defect types. Defects can form during any of the TSV ...

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, ...

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

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

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

SCAN project aims to build European GNSS-based and AI-driven technologies to detect and assess roadway pavement problems.

Although there’s broad agreement that AI demands high-quality, well-governed data, the research indicates that AI inference data is often poorly governed, inadequately classified and rarely backed up.