Data

As their influence grows, so do the challenges data engineers face. A serious one is coping with greater complexity, as more advanced AI models elevate the importance of managing unstructured...

are analytical structures for representing abstract concepts and organizing data. Data scientists usually use such frameworks — knowingly or unknowingly — to derive project plans, select machine learning models that balance various trade-offs,...

As costs for diagnostic and sequencing technologies have plummeted lately, researchers have...

Authors: Augusto Cerqua, Marco Letta, Gabriele Pinto learning (ML) has gained a central role in economics, the social sciences, and business decision-making. In the general public sector, ML is increasingly used for so-called prediction policy...

” .” do you see in the image below? Most individuals see 4: white, green, and two different shades of pinkish-red. In point of fact, those two shades are the exact same; there are only...

the ultimate quarter of 2025, it’s time to step back and examine the trends that may shape data and AI in 2026.  While the headlines might concentrate on the most recent model releases and...

Tabular Data! Recent advances in AI—starting from systems able to holding coherent conversations to those generating realistic video sequences—are largely attributable to artificial neural networks (ANNs). These achievements have been made possible by algorithmic...

— We’ve Been Down This Road Many who've come before have bemoaned the analytics dashboard. Dashboards may contain a variety of information but not much in the way in which of insight. They could...