Many enterprises are struggling with data silos, regulatory complexities and the rapid evolution of AI and analytics.

Data science is one of the few fields resilient to the current federal budget pauses and reductions, says data scientist ...

For AI to truly improve and standardize healthcare delivery, we must confront a fundamental issue—the limited and often ...

Enter small language models (SLMs). These language models are trained on specific data sets, rather than the entirety of ...

Bria, a startup offering AI image-generating models trained exclusively on licensed content, has closed a new fundraising ...

Powering artificial intelligence comes with a massive energy bill attached. Professor Wolfgang Maaß and his research team at ...

Chipmaker Nvidia is leaning further into producing tools for generative AI developers with the acquisition of synthetic data ...

New York-based Carbon Arc is working to ease the data procurement process. The company operates a cloud service, Insights ...

In comparison, traditional AI models rely on enormous swaths of prelabeled data sets in a process known as supervised training. The prelabeling is done by humans and is expensive and time-consuming.

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 ...

Data on the movement of people becomes ever more detailed, but robust models explaining the observed patterns are still needed. Mapping the problem onto a 'network of networks' could be a ...