AI tagged posts

The black and yellow robot, meant to resemble a large dog, stood waiting for directions. When they came, the instructions weren’t in code but instead in plain English: “Visit the wooden desk exactly two times; in addition, don’t go to the wooden desk before the bookshelf.”

Four metallic legs whirred into action. The robot went from where it stood in the room to a nearby bookshelf, and then, after a brief pause, shuffled to the designated wooden desk before leaving and returning for a second visit to satisfy the command.

Until recently, such an exercise would have been nearly impossible for navigation robots like this one t...

Researchers at Monash University have designed a new co-training AI algorithm for medical imaging that can effectively mimic the process of seeking a second opinion.

Published recently in Nature Machine Intelligence, the research addressed the limited availability of human annotated, or labelled, medical images by using an adversarial, or competitive, learning approach against unlabelled data.

This research, by Monash University faculties of Engineering and IT, will advance the field of medical image analysis for radiologists and other health experts.

PhD candidate Himashi Peiris of the Faculty of Engineering, said the research design had set out to create a competition between the two components...

Unveiling a groundbreaking and accurate AI-based method to classify cardiac function and disease using chest X-Rays. AI (artificial intelligence) may sound like a cold robotic system, but Osaka Metropolitan University scientists have shown that it can deliver heartwarming — or, more to the point, “heart-warning” — support. They unveiled an innovative use of AI that classifies cardiac functions and pinpoints valvular heart disease with unprecedented accuracy, demonstrating continued progress in merging the fields of medicine and technology to advance patient care. The results will be published in The Lancet Digital Health.

Valvular hea...

Why is it that artificial intelligence systems can outperform humans on some visual tasks, like facial recognition, but make egregious errors on others—such as classifying an image of an astronaut as a shovel?

Like the human brain, AI systems rely on strategies for processing and classifying images. And like the human brain, little is known about the precise nature of those processes. Scientists at Brown University’s Carney Institute for Brain Science are making strides in understanding both systems, publishing a recent paper that helps to explain computer vision in a way the researchers say is accessible as well as more useful than previous models.

“Both the human brain and the deep neural networks that power AI systems are referred to as black boxes because we don’t know exa...