Machine learning tagged posts

Cornell researchers have developed a robotic feeding system that uses computer vision, machine learning and multimodal sensing to safely feed people with severe mobility limitations, including those with spinal cord injuries, cerebralpalsy and multiple sclerosis.

“Feeding individuals with severe mobility limitations with a robot is difficult, as many cannot lean forward and require food to be placed directly inside their mouths,” said Tapomayukh “Tapo” Bhattacharjee, assistant professor of computer science in the Cornell Ann S. Bowers College of Computing and Information Science and senior developer behind the system...

Imagine performing a sweep around an object with your smartphone and getting a realistic, fully editable 3D model that you can view from any angle. This is fast becoming reality, thanks to advances in AI.

Researchers at Simon Fraser University (SFU) in Canada have unveiled new AI technology for doing exactly this. Soon, rather than merely taking 2D photos, everyday consumers will be able to take 3D captures of real-life objects and edit their shapes and appearance as they wish, just as easily as they would with regular 2D photos today.

In a new paper appearing on the arXiv preprint server and presented at the 2023 Conference on Neural Information Processing Systems (NeurIPS) in New Orleans, Louisiana, researchers demonstrated a new technique called...

It takes more than a galaxy merger to make a black hole grow and new stars form: machine learning shows cold gas is needed too to initiate rapid growth — new research finds.

When they are active, supermassive black holes play a crucial role in the way galaxies evolve. Until now, growth was thought to be triggered by the violent collision of two galaxies followed by their merger, however new research led by the University of Bath suggests galaxy mergers alone are not enough to fuel a black hole — a reservoir of cold gas at the centre the host galaxy is needed too.

The new study, published this week in the journal Monthly Notices of the Royal Astronomical Society is believed to be the first to use machine learnin...

Researchers at the Icahn School of Medicine at Mount Sinai and others have harnessed the power of machine learning to identify key predictors of mortality in dementia patients.

The study, published in the February 28 online issue of Communications Medicine, addresses critical challenges in dementia care by pinpointing patients at high risk of near-term death and uncovers the factors that drive this risk.

Unlike previous studies that focused on diagnosing dementia, this research delves into predicting patient prognosis, shedding light on mortality risks and contributing factors in various kinds of dementia.

Dementia has emerged as a major cause of death in societies with increasin...