New York City's firefighters have a new ally that could help keep them safe: drones. They've begun testing a small, fire-engine red unmanned aerial vehicle that can take both standard and infrared images and videos. The 85,000 machine will send footage to commanders in real-time, giving them the information they need to quickly come up with the best possible response. The fire department typically sends out tactical personnel equipped with gadgets to take photos of a scene before conjuring up a course of action. However, fire department director Timothy E. Herlocker realized the advantages of deploying drones for the job after a gas explosion in 2014.

A recent study shows how artificial intelligence (machine learning) could help improve the accuracy of lung diseases diagnosis. The presence of artificial intelligence (AI) in the broad and complex field of medicine has continually improved over time. As a matter of fact, AI's potential to enhance the accuracy and efficiency when it comes to disease diagnostics is simply undeniable. Following the influences of artificial intelligence in the various subspecialties of medicine, a team of researchers developed an AI-based algorithm process that can learn and perform predictive data analysis. According to Tech 2, Belgium's University of Leuven professor and senior study author Wim Janssens explained that the AI algorithm process can provide experts more accurate lung diseases diagnosis by simulating physicians' complex reasoning in a "more standardized and objective way." Based on the study, researchers successively probed how the concept of machine learning could assist them in analyzing the complete lung function tests.

Prominent researcher Andrew Ng has stated that "AI is the new electricity. " Indeed, AI is already a silent force behind the scenes in many of our interactions with the digital world -- from the news in your feed to the next show to suggest after a binge session of Stranger Things (seriously, where did my eight hours go?). Tensorflow, Scikit), AI is on an unprecedented path towards democratization. However, democratization is the first step towards commoditization. So if AI does not automatically make a product amazing, what is it for?

Chanda Kochhar, managing director & chief executive officer, ICICI Bank, said by the end of FY17, the bank would more than double the number of transactions cleared by the bots. "Currently, about 10 per cent of our internal transactions are being carried out via the software robots and by the end of this year, we believe, this will go up to 20 per cent of our transactions. This has helped us in improving productivity and efficiency and will help us in handling larger volumes as we continue to grow." The lender started experimenting with software bots at the beginning of this financial year. Now, over a million banking transactions across 200 business processes are carried out by the technology on a daily basis.

By Shray Ambe This post was written as part of a class assignment from students who took a neuroethics course with Dr. Rommelfanger in Paris of Summer 2016. My name is Shray Ambe and I am a rising senior at Emory University. I am a Neuroscience and Behavioral Biology major who is pursuing a career in the medical field. Outside of the classroom, I am involved in organizing the booth for Emory's Center for The Study of Human Health at the Atlanta Science Festival Expo every year and also enjoy volunteering at the Emory Autism Center and the Radiology Department at Emory University Hospital. At the 2016 Neuroethics Network in Paris, France, bioethicist and philosopher John Harris gave a lecture titled "How Smart Do We Want Machines to Be?" During his lecture, Harris discussed the potential impacts of artificial intelligence (AI) and stated "it doesn't matter how smart they are; obviously the smarter the better."

While serving a nine-month stint at Google, Sergey Levine watched as the company's AlphaGo program defeated the world's best human player of the ancient Chinese game Go in March. Levine, a robotics specialist at the University of California, Berkeley, admired the sophisticated feat of machine learning but couldn't help focusing on a notable shortcoming of the powerful Go-playing algorithms. "They never picked up any of the pieces themselves," he jokes. One way that the creators of AlphaGo trained the program was by feeding 160,000 previous games of Go to a powerful algorithm called a neural network, much the way similar algorithms have been shown countless labeled pictures of cats and dogs until they learn to recognize the animals in unlabeled photos. So roboticists have instead turned to a different technique: the scientist gives a robot a goal, such as screwing a cap onto a bottle, but relies on the machine to figure out the specifics itself.

While innovation can be categorized in familiar terms -- such as a new product, service, or business model -- we're finding companies are applying analytics and artificial intelligence techniques to the concept of innovation. Organizations we work with are turning to tools such as machine learning to pursue a fast, effective route to innovative results as they seek to stay competitive and top-of-mind for consumers. For our Accenture Technology Vision 2016 report, we surveyed 3,100 business and IT executives in 11 countries. We found that 70% of respondents are making significantly more investments in artificial intelligence technologies than they did in 2013, with 55% stating that they plan on using machine learning and embedded AI solutions extensively. An analytics-driven approach to innovation involves a blend of technological experimentation and cultural collaboration -- bringing together fresh minds and analytics techniques to uncover insights and ideas which can inspire agility, industry disruption and customer loyalty.

IBM (NYSE: IBM) today revealed a series of new servers designed to help propel cognitive workloads and to drive greater data center efficiency. Featuring a new chip, the Linux-based lineup incorporates innovations from the OpenPOWER community that deliver higher levels of performance and greater computing efficiency than available on any x86-based server. Collaboratively developed with some of the world's leading technology companies, the new Power Systems are uniquely designed to propel artificial intelligence, deep learning, high performance data analytics and other compute-heavy workloads, which can help businesses and cloud service providers save money on data center costs. The three new systems are an expansion of IBM's Linux server portfolio comprised of IBM's specialized line of servers co-developed with fellow members of the OpenPOWER Foundation. The new servers join the Power Systems LC lineup that is designed to outperform x86-based servers on a variety of data-intensive workloads.

On a summer day in 1968, professor Julian Stanley met a brilliant but bored 12-year-old named Joseph Bates. The Baltimore student was so far ahead of his classmates in mathematics that his parents had arranged for him to take a computer-science course at Johns Hopkins University, where Stanley taught. Having leapfrogged ahead of the adults in the class, the child kept himself busy by teaching the FORTRAN programming language to graduate students. Unsure of what to do with Bates, his computer instructor introduced him to Stanley, a researcher well known for his work in psychometrics -- the study of cognitive performance. To discover more about the young prodigy's talent, Stanley gave Bates a battery of tests that included the SAT college-admissions exam, normally taken by university-bound 16- to 18-year-olds in the United States. Bates's score was well above the threshold for admission to Johns Hopkins, and prompted Stanley to search for a local high school that would let the child take advanced mathematics and science classes.

LinkedIn has changed the way it generates search results to remove prompts that had asked people who searched for some common female names if they meant to look for similar-sounding male names instead. The professional social networking site has rolled out a change to its search algorithm designed to recognize when a person searches for another user's full name, and doesn't try to prompt them to search for another one, spokeswoman Suzi Owens said in an email. The changes follow a Seattle Times report that found that, in searches for at least a dozen of the most common female names in the U.S., LinkedIn's results included a note asking if users had meant to look for a predominantly male name instead. A search for "Stephanie Williams" brought up a prompt asking if the searcher meant "Stephen Williams." The site similarly suggested changing Andrea to Andrew, Danielle to Daniel, and Michaela to Michael, among others.