These luminous beetles use light to flirt and fend off predators. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . Growing up, Clyde Sorenson loved catching bugs, especially fireflies.

Business transformation has emerged as one of the most critical endeavors in today's enterprise, a key undertaking that, done properly, can ensure the livelihood of a business for the foreseeable future. Done wrong, business transformation can leave an enterprise in even worse shape than before, facing the prospect of having to spend big to fix it. Today, many business transformations are based on cloud technologies and a host of other technologies such as robotic process automation, machine learning and artificial intelligence, industrial IoT sensors, and more. The common thread that links all of these technologies, though, is data. None of these technologies is useful unless it has the ability to access and produce high-quality, consistent data which can, in turn, be used to make business decisions.

Using machine-learning to forecast which accused perpetrators of domestic violence -- particularly those whose crimes result in injuries -- will be re-arrested on similar charges can cut such recidivism in half, according to a recent report. Machine learning used during the arraignment process prevented "well over" 1,000 domestic violence incidents annually in at least one large metropolitan area, according to authors Richard Berk, a professor of criminology and statistics in the School of Arts & Sciences and the Wharton School, and Susan B. Sorenson, director of the Evelyn Jacobs Ortner Center on Family Violence. For their study, "Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions," Berk and Sorenson analyzed 28,646 domestic violence arraignments that led to official charges and the corresponding releases. "Under current practice, about 20 percent of the individuals released after arraignment are arrested for domestic violence within two years. If magistrates only released offenders our forecasts identified as good bets… [f]ailures could be cut in half."

Computers are getting pretty good at predicting the future. In many cases they do it better than people. That's why Amazon uses them to figure out what you're likely to buy, how Netflix knows what you might want to watch, the way meteorologists come up with accurate 10-day forecasts. Now a team of scientists has demonstrated that a computer can outperform human judges in predicting who will commit a violent crime. In a paper published last month, they described how they built a system that started with people already arrested for domestic violence, then figured out which of them would be most likely to commit the same crime again.