Synthetic data generation has emerged as a crucial technique for addressing various challenges, including data privacy, scarcity and bias. By creating artificial data that mimics real-world datasets, ...

When developing machine learning models to find patterns in data, researchers across fields typically use separate data sets for model training and testing, which allows them to measure how well their ...

AI is all about data, and the representation of the data matters strongly. But after focusing primarily on 8-bit integers and 32‑bit floating-point numbers, the industry is now looking at new formats.

In today’s data-driven world, enterprises face an ever-growing demand for data to fuel their operations, from testing to machine learning and AI. Yet, collecting high-quality, diverse and ...

Data modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. Data analysts can help build ...

Organizations that want to harness generative artificial intelligence (AI) more effectively should use their own data to train AI systems, using foundation models as a starting point. Doing so can ...

Artificial intelligence (AI) is transforming a variety of industries, including finance, manufacturing, advertising, and healthcare. IDC predicts global spending on AI will exceed $300 billion by 2026 ...

The new ImageBind model combines text, audio, visual, movement, thermal, and depth data. It’s only a research project but shows how future AI models could be able to generate multisensory content. The ...