Libratus, an artificial intelligence developed by Carnegie Mellon University, made history by defeating four of the world's best professional poker players in a marathon 20-day poker competition, called "Brains Vs. Once the last of 120,000 hands of Heads-up, No-Limit Texas Hold'em were played on Jan. 30, Libratus led the pros by a collective $1,766,250 in chips. The developers of Libratus -- Tuomas Sandholm, professor of computer science, and Noam Brown, a Ph.D. student in computer science -- said the sizable victory is statistically significant and not simply a matter of luck. "The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans," Sandholm said. This new milestone in artificial intelligence has implications for any realm in which information is incomplete and opponents sow misinformation, said Frank Pfenning, head of the Computer Science Department in CMU's School of Computer Science. Business negotiation, military strategy, cybersecurity and medical treatment planning could all benefit from automated decision-making using a Libratus-like AI. "The computer can't win at poker if it can't bluff," Pfenning said. "Developing an AI that can do that successfully is a tremendous step forward scientifically and has numerous applications.

Has a tumor shrunk during the course of treatment over several months, or have new tumors developed? To answer questions like these, physicians often perform CT and MRI scans. Tumors are usually evaluated only visually, and new tumors are often over- looked. "Our program package increases confidence during tumor measurement and follow-up," explains Mark Schenk from the Fraunhofer Institute for Medical Image Computing MEVIS in Bremen, Germany. "The software can, for example, determine how the volume of a tumor changes over time and supports the detection of new tumors."

There's no human instinct more basic than speech, and yet, for many people, talking can be taxing. One in 14 working-age Americans suffer from voice disorders that are often associated with abnormal vocal behaviors -- some of which can cause damage to vocal cord tissue and lead to the formation of nodules or polyps that interfere with normal speech production. Unfortunately, many behaviorally-based voice disorders are not well understood. In particular, patients with muscle tension dysphonia (MTD) often experience deteriorating voice quality and vocal fatigue ("tired voice") in the absence of any clear vocal cord damage or other medical problems, which makes the condition both hard to diagnose and hard to treat. But a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) believes that better understanding of conditions like MTD is possible through machine learning.