Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data.

By automating the end-to-end machine learning pipeline, it empowers less-experienced developers to build high-quality models ...

Who needs rewrites? This metadata-powered architecture fuses AI and ETL so smoothly, it turns pipelines into self-evolving ...

VelocityEHS’ software is designed to help organizations reach their environmental, health and safety goals, offering AI-driven capabilities related to chemical inventory management, contractor safety ...

Techniques such as Raman spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR), and ...

Explore how AI predictive analytics reshapes industries by providing insights, forecasting trends, and enhancing ...

New module addresses multi-billion-dollar data quality challenge in life sciences by automating data cleansing, standardization, and transformation for AI readiness Built for scale, security, and ...

The TinyML Foundation’s work, along with that of Silicon Labs, helps users explore new use-cases and challenges that embedded ...

AI offers promise in the realm of chronic disease management, including diabetes, obesity, and PCOS, but must be approached with caution.

A recent study conducted by the University of Amsterdam (Amsterdam, Netherlands) and the University of Queensland (Queensland, Australia) developed a novel prioritization strategy that directly links ...

GIS enables spatial mapping of climate vulnerability, helping policymakers and researchers visualize hazard zones and plan targeted interventions. It supports complex simulations of land-use change ...