Fundamental emerges from stealth with first major foundation model trained for tabular data

Emerging from stealth, the company is debuting NEXUS, a Large Tabular Model (LTM) designed to treat business data not as a ...

MXenes for energy storage: More versatile than expected

MXene materials are promising candidates for a new energy storage technology. However, the processes by which the charge ...

Simulations and experiments meet: Machine learning predicts gold nanocluster structures

Researchers at University of Jyväskylä (Finland) advance understanding of gold nanocluster behavior at elevated temperatures ...
Frontiers

Editorial: Optimization for low-rank data analysis: theory, algorithms and applications

Low-rank data analysis has emerged as a powerful paradigm across applied mathematics, statistics, and data science. With the rapid growth of modern datasets in size, dimensionality, and complexity, ...
IEEE

Machine Learning and Data Science in Social Sciences: Methods, Applications, and Future Directions

Abstract: Artificial intelligence (AI) is transforming social science research by enabling scalable data analysis, predictive modeling, and causal inference, thereby reshaping the methodological ...
IEEE

Self-Supervised Learning Powered by Synthetic Data From Diffusion Models: Application to X-Ray Images

Abstract: Synthetic data offers a compelling solution to the challenges associated with acquiring high-quality medical data, which is often constrained by privacy concerns and limited accessibility.
MIT Technology Review

Using unstructured data to fuel enterprise AI success

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results. In ...