Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data. This includes text from books and websites, images from public databases, ...

Data Dependency: Machine learning models require vast amounts of high-quality data, which can be difficult and expensive to obtain. Poor or biased data leads to poor model performance and biased ...

A new study presents a machine learning model that accurately predicts the compressive strength of high-strength concrete, ...

The Big Data Analytics, Artificial Intelligence and Machine Learning research cluster tackles important problems and develops real-life applications, harnessing technologies to extract insights and ...

Ensure data quality: Data quality is critical for accurate machine learning and AI models. Choose a database that supports data integrity constraints, data validation, and data cleansing.

Data science platform Kaggle is hosting a Wikipedia dataset that’s specifically optimized for machine learning applications by Jess Weatherbed Apr 17, 2025, 10:07 AM UTC ...

Australia-based Tyton Ecological Intelligence (Tyton EI) has launched the world’s first Machine Learning as a Service (MLaaS) ...

Machine learning is helping create stronger, more efficient encryption methods. By analyzing huge amounts of data, ML can design encryption algorithms that are tougher to crack.