MIT computer scientists have developed a system that learns to identify objects within an image, based on a spoken description of the image. Given an image and an audio caption, the model will highlight in real-time the relevant regions of the image being described.
“Cannot get asleep all night”, “a little giddy” and other complaints in social networks can now be translated into formal medical terms, such as insomnia or vertigo, after a Russian-led study involving neural networks.READ MORE
Researchers find AI-generated reviews and comments pose a significant threat to consumers, but machine learning can help detect the fakes.
Scientists have used machine learning to train computers to see parts of the cell the human eye cannot easily distinguish. Using 3D images of fluorescently labeled cells, the research team taught computers to find structures inside living cells without fluorescent labels, using only black and white images generated by an inexpensive technique known as brightfield microscopy.READ MORE
Hateful text and comments are an ever-increasing problem in online environments, yet addressing the rampant issue relies on being able to identify toxic content. A new study by the Aalto University Secure Systems research group has discovered weaknesses in many machine learning detectors currently used to recognize and keep hate speech at bay.
Nonalcoholic fatty liver disease produces no noticeable symptoms, but one out of every five people with it will go on to develop a more serious conditions such as nonalcoholic steatohepatosis and cirrhosis. Three new studies investigate how mitochondrial energy production is altered by the progress of fatty liver disease.READ MORE
Computer programmed to identify people with dementia by detecting differences in speech, language and facial expressions.READ MORE