Convolutional neural networks are good at recognizing disease symptoms in medical scans of patients who were injected with iodine-based dye, that makes their organs more visible. But some patients can’t take the dye. Now synthetic scans from a GAN are helping CNNs learn to analyze undyed images.

New research teaches robots to anticipate what’s coming rather than focusing on what’s right in front of them. Researchers developed Occupancy Anticipation (OA), a navigation system that predicts unseen obstacles in addition to observing those in its field of view.

Large models pretrained in an unsupervised fashion and then fine-tuned on a smaller corpus of labeled data have achieved spectacular results in natural language processing. New research pushes forward with a similar approach to computer vision.

A machine learning model is scouring the cosmos for undiscovered planets. Astronomers from the University of Warwick developed a system that learned to identify faraway worlds in a dataset of thousands of candidates.

The technique known as dropout discourages neural networks from overfitting by deterring them from reliance on particular features. A new approach reorganizes the process to run efficiently on the chips that typically run neural network calculations.

The White House called for new funding for AI research including a constellation of research centers. Nonetheless, the U.S. government’s annual spending on the technology still would lag behind that of several other...

Marketers are using computer vision to parse customers by skin color and other perceived racial characteristics. A number of companies are pitching race classification as a way for businesses to understand the buying habits of different groups.

AI is helping avert traffic accidents by assessing the risk of car crashes at specific intersections. MicroTraffic, a Canadian video analytics company, predicts the odds that accidents will occur at intersections that traditional methods overlook.

Semi-supervised learning — a set of training techniques that use a small number of labeled examples and a large number of unlabeled examples — typically treats all unlabeled examples the same way. But some examples are more useful for learning than others.

A strategy manifesto from one of China’s biggest tech companies declares, amid familiar visions of ubiquitous AI, that deepfakes are more boon than bane.

People who take driving lessons during daytime don’t need instruction in driving at night. They recognize that the difference doesn’t disturb their knowledge of how to drive. Similarly, a new reinforcement learning method manages superficial variations in the environment without re-training.

With the rise of AI-driven surveillance, anonymity is in fashion. Researchers are working on clothing that evades face recognition systems and designed a t-shirt that tricks a variety of object detection models into failing to spot people.

Language models like Bert, Ernie, and Elmo have achieved spectacular results based on clever pre-training approaches. New research applies some of those Sesame Street lessons into image processing.

Computer vision is helping sushi lovers enjoy top-quality maguro. Japanese restaurant chain Kura Sushi is using a smartphone app called Tuna Scope to grade its suppliers’ offerings, according to the news outlet The Asahi Shimbun.

We’d all love to be able to find similar examples of our favorite cat videos. But few of us want to label thousands of similar videos of even the cutest kitties. New research makes headway in video classification when training examples are scarce.