The internet uses routing tables to determine how and where data is sent and received. Without accurate and reliable tables, the internet would be like a highway system with no signs or signals to direct the traffic to the right places. Of course, cybercriminals find a way to corrupt just about everything that makes the internet work, and routing is no exception. IP hijacking, or BGP (Border Gateway Protocol) hijacking, is a process in which hackers and cybercriminals take over groups of IP addresses by corrupting the routing tables that use BGP. The purpose is to redirect traffic on the public internet or on private business networks to the hijackers' own networks where they can intercept, view, and even modify the packets of data.

Researchers at the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory have developed a machine learning algorithm that leverages large amounts of intensive care unit (ICU) data to predict actionable interventions for patients and improve health outcomes. By tapping into an MIT database of de-identified data for 40,000 critical care patients--including demographics, laboratory tests, medications and vital signs--the research team is able to use deep learning to determine what kinds of treatments are needed for different symptoms. The approach--called ICU Intervene--was presented in a paper this past weekend at the Machine Learning for Healthcare Conference in Boston. According to the authors, their model is the first to use deep neural networks to predict both onset and weaning of interventions using all available modalities of ICU data. "The decisions that are made in the ICU are made in a particularly high-stress and high-demand environment," says Harini Suresh, a PhD student and lead author on the paper, who adds that clinicians in these situations are bombarded with different types of data for many patients and as a result it can be difficult to make real-time treatment decisions.

Don't you just hate it when the YouTube clip you're trying to watch pauses midway to buffer, or drastically lowers the resolution to a pixelated mess? A group of MIT researchers believe they've figured out a solution to those annoyances plaguing millions of people a day. Using machine learning, the Pensieve system figures out the optimal algorithm to use for delivering video at the best possible resolution while avoiding buffering breaks, no matter what connection you're on. That's kind of what YouTube and Netflix already strive to do, but the researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) say that the systems currently have to make a trade-off between the quality of the video versus how often it has to rebuffer in order to prepare the next segment of the clip for viewing. By using an AI to learn what algorithm works best in various conditions – including, for example, instances when you're heading into a tunnel where connectivity is sketchy, and when you're in a crowded area with thousands of other network users – Pensieve is said to cut rebuffering by up to 30 percent.