One of the most frustrating elements of the current healthcare environment is the administrative burden of prior authorizations for medications and procedures. It is a frustration for providers, for patients, and for payers. Is there any way to solve this dilemma? For physicians, an estimated 20 hours per week is spent in prior authorization activities, costing an average of 83,000 in excess annual overhead per physician. Is there an actual benefit for this effort? Most physicians say that payers (commercial, Medicare, Medicaid, and pharmacy benefit managers (PBMs)) use prior authorizations to keep costs down.

The safety of troops in the very near future will rely on Artificial Intelligence-assisted tablets and small screens, networked to drones in the air that feed data back down to ground personnel equipped with information-rich HUD visors. "Robots are going to help humans in dangerous situations contain and control a region," said Dave Bossert, DARPA Program Manager and Senior Engineering Fellow at Raytheon. "Maintaining advantage, communicating, and understanding an area is as good as or better than being aggressive." In a recent interview Deputy Defense Secretary Bob Work expanded on the idea of how AI will power robots in hazardous situations. The networked soldier, Bossert said, will rely heavily on custom-built Android tablets and several wearable devices.

Rembrandt may have died in 1669 but his artistic legacy lives on thanks in part to a new piece of work created with computer data, instead of the artist's hands. Revealed in the Netherlands, 'The Next Rembrandt' is the result of an 18-month project undertaken by a group of art historians, along with software developers, scientists, engineers and data analysts – not the usual team of artists for portraits work. This year's edition of TNW Conference in Amsterdam includes some of the biggest names in tech. The team worked tirelessly to anyalyze all known works of Rembrandt, which amounts to over 300 paintings, using high resolution 3D scans to capture every little detail and create an algorithm that would eventually be able to accurately recreate his style. That data was then fed to a 3D printer, which recreated the painting using 13 layers of paint-based UV ink.

Every 20 seconds, a new scholarly article is published in biomedicine, resulting in more than 1.5 million per year. While this surge in global research output is exciting for scientists, it also presents them with a very real challenge – researchers cannot keep up with the current output of literature with the tools that are available to them now. With citation search engines, you enter a keyword, retrieve sets of papers, and rank and filter those papers in various ways. Advanced search products use semantic analyses to provide more powerful filters or improved rankings, but they still require you to at least suspect that a particular paper exists. What about the ones you don't yet know exist?

The lighting in a theatrical production can often be as important as the sets and performances themselves. A new dance production, Pattern Recognition, will make use of artificial intelligence to drive the latest motion sensing technology to respond directly to the movements of two dancers on stage. UK choreographer Alexander Whitley and digital artist Memo Akten created Pattern Recognition using code and a system of moving lights and Microsoft Kinect sensors to intelligently change the pattern of lighting to match their movements. BBC Click's Jen Copestake went to find out more about the technology behind the show.

One of the most important tasks in Machine Learning are the Classification tasks (a.k.a. Classification is used to make an accurate prediction of the class of entries in the test set (a dataset of which the entries have not been labelled yet) with the model which was constructed from a training set. You could think of classifying crime in the field of Pre-Policing, classifying patients in the Health sector, classifying houses in the Real-Estate sector. Another field in which classification is big, is Natural Lanuage Processing (NLP). This is the field of science with the goal to makes machines (computers) understand (written) human language.

Salesforce has quietly been amassing talent in the artificial intelligence domain, most recently with the acquisition of MetaMind this week, a company working on deep learning for automated image recognition. Salesforce CEO Marc Benioff has backed the Palo Alto, California-based startup almost from the beginning, participating in an 8 million venture round in December 2014 along with Kholsa Ventures. The deal follows a number of other buys by Salesforce of companies including machine learning startup PredictionIO, data science for enterprises company MinHash, and a "smart" iPhone calendar app called Tempo AI that automatically added context such as contacts and documents to calendar items. Salesforce has also hired away some of LinkedIn's data science talent. Forrester Research principal analyst Mike Gualtieri told InformationWeek in an interview that Salesforce is keeping pace with consumer-focused Internet giants like Google and Facebook with these acquisitions.

I recently left my job to be an independent software engineer again. One of my objectives for my newly acquired free time was to increase my understanding of Natural Language Processing--the art and science of using computers to manipulate text--since human words are a thing I'm interested in. When I'm successful at learning a particular NLP technique, I'll release a project that uses it, probably in the form of a small Twitter bot or online toy. When I'm unsuccessful or hit a dead end, I'll write up a postmortem, which I hope will accomplish two goals: The cool kids are using deep learning/neural nets for NLP, as a lot of the traditional approaches seem to have reached their limit of effectiveness. But neural nets are their own thing with their own math, and while I'll get there eventually, I decided to start with pre-existing language models and statistical machine learning, both of which are better documented right now.

When people imagine machine learning, they tend to think about talking cars or humanoid robots--the stuff of sci-fi fantasy, or else dystopian fiction. But machine learning is neither, and it's already changing what computers can do. In the near future anyway, it's going to transform the way we work--starting with that smartphone in your pocket. Programs that can learn how to accomplish tasks are already at play in the workplace, where they're taking on an ever greater share of our most energy-sapping, mundane chores so we humans have more time for the important stuff. That's nowhere more evident that on mobile, where our smartphones are transforming into personal assistants.

Bob, CFO of ABC Inc is about to get on an earnings call after just reporting a 20% miss on earnings due to slower revenue growth than forecasted. Company ABC's stock price is plummeting, down 25% in extended hour trading. The board is furious and investors demand answers on the discrepancies. Inaccurate revenue forecast remains one of the biggest risks for CFOs. In a recent study, more than 50% of companies feel their pipeline forecast is only about 50% accurate.