In a new study, researchers from the University of Copenhagen's Department of Computer Science have collaborated with the Danish Center for Sleep Medicine at the Danish hospital Rigshospitalet to develop an artificial intelligence algorithm that can improve diagnoses, treatments, and our overall understanding of sleep disorders. Difficulty sleeping, sleep apnea and narcolepsy are among a range of sleep disorders that thousands of Danes suffer from. Furthermore, it is estimated that sleep apnea is undiagnosed in as many as 200,000 Danes. "The algorithm is extraordinarily precise. We completed various tests in which its performance rivalled that of the best doctors in the field, worldwide," states Mathias Perslev, a PhD at the Department of Computer Science and lead author of the study, recently published in the journal npj Digital Medicine (link).

The field of artificial intelligence (AI) has created computers that can drive cars, synthesize chemical compounds, fold proteins and detect high-energy particles at a superhuman level. However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation. Therefore, AI researchers like me are now turning our efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand. If we can do this, I believe that AI will be able to uncover and teach people new facts about the world that have not yet been discovered, leading to new innovations.

According to a report on the website of the National Institute of Standards and Technology on November 24, a multi-institutional team from the National Institute of Standards and Technology, the University of Maryland and the Stanford Linear Accelerator Center (SLAC) of the U.S. Department of Energy has developed a closed-loop material exploration and optimization based on artificial intelligence The system (CAMEO) algorithm aims to use the self-learning characteristics of the algorithm to discover complex new materials with specific properties through fewer experiments, to help scientists minimize the time of trial and error in experiments and improve the efficiency of new material development. The research team connected the X-ray diffraction equipment to a computer equipped with the CAMEO algorithm and imported the existing material database into the algorithm. After many iterations of learning, only a small amount of routine measurement can be used to find The best material for specific properties. Using this method, researchers discovered new nanocomposite phase change memory materials among 177 possible materials. The number of test iterations required was reduced to 1/10 of the original, and the time required was shortened from 90 hours.

"We're trying to change how people run agronomic research. Instead of establishing a small field plot, running statistics and publishing the means, what we're trying to do involves the farmer far more directly. We are running experiments with farmers' machinery in their own fields. We can detect site-specific responses to different inputs. And we can see whether there's a response in different parts of the field," said Nicolas Martin, assistant professor in the U of I Department of Crop Sciences and co-author of the study.

Seventy years ago, science fiction writer Isaac Asimov imagined a world where robots would serve humans in countless ways, and he equipped them with built-in safeguards now known as Asimov's Three Laws of Robotics, to prevent them, among other goals, from ever harming a person. Guaranteeing safe and fair machine behavior is still an issue today, says machine learning researcher and lead author Philip Thomas at the University of Massachusetts Amherst. "When someone applies a machine learning algorithm, it's hard to control its behavior," he points out. This risks undesirable outcomes from algorithms that direct everything from self-driving vehicles to insulin pumps to criminal sentencing, say he and co-authors. Writing in Science, Thomas and his colleagues Yuriy Brun, Andrew Barto and graduate student Stephen Giguere at UMass Amherst, Bruno Castro da Silva at the Federal University of Rio Grande del Sol, Brazil, and Emma Brunskill at Stanford University this week introduce a new framework for designing machine learning algorithms that make it easier for users of the algorithm to specify safety and fairness constraints.