University of Maryland

An analysis of United States job listings shows AI jobs are growing rapidly outside traditional tech hubs. Researchers at University of Maryland analyzed the distribution of AI jobs among U.S. job postings. California hosts the largest concentration...

We know that image generators create wonderful original works, but do they sometimes replicate their training data? Recent work found that replication does occur.

How can you tell when you’re reading machine-generated text? Three recent papers proposed solutions: Watermarking, classification, and a statistical method.

Hospitals across the United States are relying on AI to keep patients safe. Doctors are using a variety of machine learning systems to assess the risk that a given patient will suffer complications.

In machine learning, an easy task and a more difficult version of the same task — say, a maze that covers a smaller or larger area — often are learned separately.

A supergroup of machine learning models flags manipulated photos. Jigsaw, a tech incubator owned by Alphabet, released a system that detects digitally altered images.

Models that learn relationships between images and words are gaining a higher profile. New research shows that adversarial learning, usually a way to make models robust to deliberately misleading inputs, can boost vision-and-language performance.

A typical GAN’s output doesn’t necessarily reflect the data distribution of its training set. Instead, GANs are prone to modeling the majority of the training distribution, sometimes ignoring rare attributes — say, faces that represent minority populations.

Deepfakes threaten to undermine law and order, perhaps democracy itself. A coalition of tech companies, nonprofits, and academics joined forces to counter potential adverse impacts.