Machine learning has moved past its initial experimental phase. In earlier years, development often focused on creating the largest possible models to see what capabilities might appear. Today, the ...

In a recent paper, SFI Complexity Postdoctoral Fellow Yuanzhao Zhang and co-author William Gilpin show that a deceptively ...

A new study published in Genome Research presents an interpretable artificial intelligence framework that improves both the accuracy and transparency of genomic prediction, a key challenge in fields ...

Protein engineering is a field primed for artificial intelligence research. Each protein is made up of amino acids; to ...

In a future perhaps not too far away, artificial intelligence and its subfield of machine learning (ML) tools and models, ...

Head and neck cancers represent a biologically heterogeneous group of malignancies requiring accurate diagnosis, staging, and risk stratification for ...

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

A number of agencies are enthusiastically working to develop tools that involve artificial intelligence and machine learning. The Department of Veterans Affairs, for instance, had the third-largest ...

Modern ERP platforms are becoming smarter, more adaptive, and far more predictive, unlocking capabilities that were nearly impossible just a few years ago. For organizations looking to stay ...

What was the rationale behind applying machine learning (ML) models to improve identification probability in the absence of ...

Machine learning is a multibillion-dollar business with seemingly endless potential, but it poses some risks. Here's how to avoid the most common machine learning mistakes. Machine learning technology ...