A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.

The models are designed to predict someone’s risk of diabetes or stroke. A few might already have been used on patients.

When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...

Researchers find large language models process diverse types of data, like different languages, audio inputs, images, etc., similarly to how humans reason about complex problems. Like humans, LLMs ...

By combining the efficiency of a Mixture-of-Experts architecture with the openness of an Apache 2.0 license, OpenAI is ...

The more useful AI becomes, the more data it must touch. And the more data it touches, the higher the stakes for security, ...

A new crowd-trained way to develop LLMs over the internet could shake up the AI industry with a giant 100 billion-parameter model later this year. Flower AI and Vana, two startups pursuing ...

In the rapidly evolving landscape of modern manufacturing and engineering, a new technology is emerging as a crucial enabler-Data-Model Fusion (DMF). A recent review paper published in Engineering ...

Public health systems sit on mountains of data — yet insight remains scarce. The organizations closing that gap aren’t just investing in better dashboards. They’re fundamentally rethinking who gets to ...

However, a new study warns that the same capabilities driving their adoption are also creating a broad and evolving landscape of security, privacy, and ethical risks that existing safeguards are ...