Artificial intelligence tools are complicated computer programs that suck in vast amounts of data, search for patterns or trajectories, and make a prediction or recommendation to help guide a decision. Patients don't need to understand these algorithms at a data-scientist level, but it's still useful for people to have a general idea of how AI-based healthcare tools work, says Suresh Balu, program director at the Duke Institute for Health Innovation. Some patients can get a little jumpy when they hear algorithms are being used in their care, says Mark Sendak, a data scientist at the Duke Institute for Health Innovation. We picked an algorithm that flags patients in the early stages of sepsis -- a life-threatening complication from an infection that results in widespread inflammation through the body. The algorithm we're looking at underpins a program called Sepsis Watch, which Sendak and Balu helped develop at Duke University.

Recent efforts include working with government cyber-security agencies and insurance and financial industry experts to develop principles of responsible private sector response against attackers; collaborating with G-20 finance ministries and central banks, international financial institutions such as SWIFT, and global banks and insurers to develop practical norms to protect the integrity of financial data and transactions; initiatives in Silicon Valley and China to develop compatible approaches to promote Artificial Intelligence safety; and an effort to map how diverse stakeholders in China, India, and the United States assess risks associated with bioengineering techniques such as gene-editing.

Why you should listen Noriko Arai is the program director of an AI challenge, Todai Robot Project, which asks the question: Can AI get into the University of Tokyo? The project aims to visualize both the possibilities and the limitation of current AI by setting a concrete goal: a software system that can pass university entrance exams. In 2015 and 2016, Todai Robot achieved top 20 percent in the exams, and passed more than 70 percent of the universities in Japan. The inventor of Reading Skill Test, in 2017 Arai conducted a large-scale survey on reading skills of high and junior high school students with Japan's Ministry of Education. The results revealed that more than half of junior high school students fail to comprehend sentences sampled from their textbooks.

The premise of the wildly successful Raiders of the Lost Ark film franchise is discovery. In his adventures, fictional archaeologist Dr. Henry "Indiana" Jones seeks hidden artifacts that hold sought after secrets--objects as elusive as fossilized alien heads and the Holy Grail--to investigate and preserve the clues they contain. Similarly, IBM Watson and Discovery Service can unearth secrets buried deep in your data. Without question, data is one of your organization's most valuable resources: mining the untapped potential in that resource demands powerful, cognitive solutions. Historically, cognitive solutions came with steep learning curves that demanded more staff expertise, financial resources and time.

Armonk, N.Y. - 07 Apr 2017: IBM (NYSE: IBM) today announced that its scientists have been granted a patent on machine learning models to predict therapeutic indications and side effects from various drug information sources. IBM Research has implemented a cognitive association engine to identify significant linkages between predicted therapeutic indications and side effects, and a visual analytics system to support the interactive exploration of these associations. IBM Research staff member Ping Zhang (left) and Program Director, Center for Computational Health Jianying Hu (right) with their newly patented invention to help drug discovery researchers identify which drug indications are typically linked to which side effects. IBM was granted U.S. Patent 9,536,194: Method and system for exploring the associations between drug side-effects and therapeutic indications for this invention. Lack of efficacy and adverse side effects are two of the primary reasons a drug fails clinical trials, each accounting for around 30 percent of failures.

The Master of Science in Data Science (MSDS) is an 11?month professional masters program, designed to meet the increasingly data?intensive needs of industry and government. The program starts near the beginning of July and ends the next year in mid-May. Core program courses will be taught by faculty from the Departments of Computer Science, Statistics, and Systems and Information Engineering. Three key features of this program are (a) an integrated curriculum and data experience; (b) the compressed duration; and (c) a cohort experience. To achieve these, the curriculum is tightly prescribed with about 80% common to all students.