Recent advances in large foundation models promise to automate complex tasks in architecture, engineering, and construction (AEC). These models offer ...

The study offers five actionable insights for humanitarian organizations seeking to adopt AI tools. First, deep problem ...

This paper offers practical advice on how to improve statistical power in randomized experiments through choices and actions researchers can take at the design, implementation, and analysis stages. At ...

In May, Google released MedGemma, which uses both the MedQA and Afri-MedQA datasets to form a more globally accessible healthcare chatbot. MedGemma has several versions, including 4-billion and ...

For more than 10 years, a funding model has quietly done what many others have struggled to do: Funnel nature and climate ...

The Open-source AI Language Proficiency Monitor backed by the German Government ranks LLMs across languages and tasks, ...

The Qlarant Foundation is announcing a new partnership with Catchafire that will extend access to transformative ...

The search is officially on for professional fundraiser Colossal's 2025 Baby of the Year—the nationwide competition ...

A Tribune reporter and data nerd went looking for a smarter way to evaluate and draft NBA players. From Cooper Flagg to a few under-the-radar risers, here's what he found.

Scientists at Massachusetts Institute of Technology have devised a way for large language models to keep learning on the fly—a step toward building AI that continually improves itself.

The Rapid Growth of Model Portfolios and What Comes Next Model portfolio assets reach a new high, and providers continue innovating.

The National Hurricane Center will experiment with the company’s DeepMind program to enhance the work of its expert meteorologists.