Deep learning and its applications have grown in recent years. Recently, researchers from ETH Zurich used the technique to study dark matter in an industry first. Now, a team working with the University of California, Berkeley and the University of California, San Francisco (UCSF) School of Medicine have trained a convolutional neural network dubbed "PatchFCN" that detects brain hemorrhages with remarkable accuracy. In a paper titled "Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning", the team claims that: We used a single-stage, end-to-end, fully convolutional neural network to achieve accuracy levels comparable to that of highly trained radiologists, including both identification and localization of abnormalities that are missed by radiologists. The team achieved an accuracy of 99 percent, which is the highest recorded accuracy to date for detecting brain hemorrhages. Our algorithm demonstrated the highest accuracy to date for this clinical application, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.991 0.006 for identification of examinations positive for acute intracranial hemorrhage, and also exceeded the performance of 2 of 4 radiologists.

Photoshop could become a thing of the past thanks to new technology that can touch-up badly damaged photos. The Nvidia software uses AI and deep-learning algorithms to predict what a missing portion of a picture should look like and recreate it with incredible accuracy. All users need to do is click and drag over the area to be filled in and the image is instantly updated. As well as restoring old physical photos that have been damaged, the technique could also be used to fix corrupted pixels or bad edits made to digital files. Graphics specialist Nvidia, based in Santa Clara, California trained its neural network using a variety of irregular shaped holes in images.

Listen, you're kinda spooked about the rise of artificial intelligence, and I get that. It's a tremendously powerful technology that promises to transform the very nature of work, inevitably leading to the automation of certain white-collar jobs. But AI also promises to make human labor smarter and more efficient, even something as traditional as small-scale farming. To that end, researchers have developed a smartphone-based program that can automatically detect diseases in the cassava plant--the most widely grown root crop on Earth--with darn near 100 percent accuracy. The most impressive bit about the technology is that the neural network that powers it runs entirely on the smartphone, no cloud computing or hulking processors required, as the researchers detail in a preprint paper to be published in Frontiers in Plant Science.

Colin Walsh, data scientist at Vanderbilt University Medical Center, hopes his work in predicting suicide risk will give people the opportunity to ask "what can I do?" Walsh and his team gathered data on 5,167 patients from Vanderbilt University Medical Center that had been admitted with signs of self-harm or suicidal ideation. "I'd like to think it'll be fairly quick, but fairly quick in health care tends to be in the order of months," he adds. Suicide is such an intensely personal act that it seems, from a human perspective, impossible to make such accurate predictions based on a crude set of data.

When someone commits suicide, their family and friends can be left with the heartbreaking and answerless question of what they could have done differently. Colin Walsh, data scientist at Vanderbilt University Medical Center, hopes his work in predicting suicide risk will give people the opportunity to ask "what can I do?" while there's still a chance to intervene. Walsh and his colleagues have created machine-learning algorithms that predict, with unnerving accuracy, the likelihood that a patient will attempt suicide. In trials, results have been 80-90% accurate when predicting whether someone will attempt suicide within the next two years, and 92% accurate in predicting whether someone will attempt suicide within the next week. The prediction is based on data that's widely available from all hospital admissions, including age, gender, zip codes, medications, and prior diagnoses.

An artificial intelligence algorithm has predicted the outcome of human rights trials with 79 percent accuracy, according to a study published today in PeerJ Computer Science. Developed by researchers from the University College London (UCL), the University of Sheffield, and the University of Pennsylvania, the system is the first of its kind trained solely on case text from a major international court, the European Court of Human Rights (ECtHR). "Our motivation was twofold," co-author Vasileios Lampos of UCL Computer Science told Digital Trends. "It first starts with scientific curiosity." In other words, would it even be possible to create such an AI judge?