People in Texas sounded off on AI job displacement, with half of the people who spoke to Fox News convinced that the tech will rob them of work. Artificial intelligence algorithms can help predict a person's political ideology based on their facial characteristics, a study conducted in Denmark found. The tech found right-wing politicians were more likely to have happy facial expressions in photos while people pictured with neutral facial expressions were more likely to identify as left-wing, the study said. The study, "Using deep learning to predict ideology from facial photographs: expressions, beauty, and extra-facial information," found that AI can predict a person's political ideology with 61% accuracy when analyzing a photo of a person. Deep learning, a method in AI where computer scientists teach computers to learn and process information similar to humans, can be used to make predictions about people based on photographs alone, the researchers explained in their paper, which was published in Scientific Reports.

Imagine a stadium where ultra-high-resolution video feeds and camera-carrying drones track how individual players' joints flex during a game, how high they jump or fast they run--and, using AI, precisely identify athletes' risk of injury in real time. Coaches and elite athletes are betting on new technologies that combine artificial intelligence with video to predict injuries before they happen and provide highly tailored prescriptions for workouts and practice drills to reduce the risk of getting hurt. In coming years, computer-vision technologies similar to those used in facial-recognition systems at airport checkpoints will take such analysis to a new level, making the wearable sensors in wide use by athletes today unnecessary, sports-analytics experts predict.

The character of conflict between nations has fundamentally changed. Governments and militaries now fight on our behalf in the "gray zone," where the boundaries between peace and war are blurred. They must navigate a complex web of ambiguous and deeply interconnected challenges, ranging from political destabilization and disinformation campaigns to cyberattacks, assassinations, proxy operations, election meddling, or perhaps even human-made pandemics. Add to this list the existential threat of climate change (and its geopolitical ramifications) and it is clear that the description of what now constitutes a national security issue has broadened, each crisis straining or degrading the fabric of national resilience. Traditional analysis tools are poorly equipped to predict and respond to these blurred and intertwined threats.

The pandemic has put immense strain on ICUs, resulting in shortages of staff, beds, personal protective equipment and ventilators. It has also exposed the limitations of traditional predictive algorithms used to predict patient outcomes, manage capacity, and inform triage decisions. Use of artificial intelligence can help refine the raw data and present more useful knowledge, especially in the ICU setting, by separating the clinically-relevant information from the noise in a data rich environment. "This results in earlier recognition of changes in patient conditions and their evolving risks," said Dr. John Frownfelter, chief medical information officer at Jvion. "It also allows you to see patients holistically by bringing in data on behavioral risk factors that you wouldn't be able to see from the clinical data in the EHR."

The auto manufacturing supply chain is a complex web of suppliers, parts, specialized production lines, tools, and more. It's not an easy task to create a sales forecast and then plan out exactly the materials, parts, supplies, and tools needed to produce automobiles. It gets even more difficult when you throw in an unexpected highly disruptive event such as the COVID-19 pandemic. That's the position Jaguar Land Rover found itself in recently. The company needed to respond quickly when one of its suppliers failed. The company used graph technology to re-sequence how vehicle orders were to be built in the factory.

A team of researchers showed that artificial intelligence (AI) could help predict the type of bacteria that caused the infection in patients with pneumonia. The research is presented at ASM Microbe Online, the annual meeting of the American Society for Microbiology. "This research highlights the potential of AI as a supplementary tool for physicians in identifying causal pathogens of pneumonia, even before sputum culture results are available," said Joowhan Sung, M.D., hospitalist at MedStar Southern Maryland Hospital. "We demonstrated that physicians could be assisted by AI to decide appropriate antibiotics." In the study, investigators showed that AI could use the information available in the emergency room and predict if the patient has MRSA or pseudomonas so that physicians can immediately prescribe specific antibiotics targeting specific bacteria.

Imagine mental health care professionals having the ability to reliably predict how a person suffering from depression will respond to certain antidepressant medication based on symptoms and brain wave data, rather than trial and error. Recently, researchers developed an artificial intelligence (AI) algorithm that may accelerate precision medicine for depression. In a new study published on June 22 in JAMA Network Open, researchers used machine learning with pretreatment symptom scores and brain wave data to predict which depressive symptoms would improve with antidepressants. Major depressive disorder (MDD) is a common mental health condition, affecting an estimated 16 million adults in the United States each year. Fortunately, depression can be treated; unfortunately, however, depression is deeply variegated, with a myriad of possible combinations of symptoms, making pinpointing the right course of treatment a challenge.

You are free to share this article under the Attribution 4.0 International license. An algorithm that can predict whether a given blood test will come back "normal" could help cut needless medical tests, researchers report. Being thorough in medicine is a must--but doctors concerned about over-testing are raising a new question: Is it possible to be too thorough? Jonathan Chen, assistant professor of medicine at Stanford University, says the answer is yes, particularly in the context of diagnostic blood testing. Blood testing is a cornerstone of diagnostic medicine, but there's an increasing recognition that too much blood testing--such as repeated tests--yields diminishing results.

A new model developed at MIT can help predict if patients at risk for Alzheimer's disease will experience clinically significant cognitive decline due to the disease, by predicting their cognition test scores up to two years in the future. The model could be used to improve the selection of candidate drugs and participant cohorts for clinical trials, which have been notoriously unsuccessful thus far. It would also let patients know they may experience rapid cognitive decline in the coming months and years, so they and their loved ones can prepare. Pharmaceutical firms over the past two decades have injected hundreds of billions of dollars into Alzheimer's research. Yet the field has been plagued with failure: Between 1998 and 2017, there were 146 unsuccessful attempts to develop drugs to treat or prevent the disease, according to a 2018 report from the Pharmaceutical Research and Manufacturers of America.

Using machine learning - a type of artificial intelligence (AI) - could help develop better predictions of why children struggle at school, scientists say. The researchers from the University of Cambridge in the UK used AI and data from hundreds of children who struggle at school to identify clusters of learning difficulties which did not match the previous diagnosis the children had been given. The finding, published in the journal Developmental Science, reinforces the need for children to receive detailed assessments of their cognitive skills to identify the best type of support. The researchers recruited 550 children who were referred to a clinic because they were struggling at school. Much of the previous research into learning difficulties has focussed on children who had already been given a particular diagnosis, such as attention deficit hyperactivity disorder (ADHD), an autism spectrum disorder, or dyslexia, they said.