First, McKinsey went back to a previous study they did on data and analytics in 2011 and compared the economic value that they estimated could be realized by the use of data and analytics with the actual results. To get to the point, the results to date are not exactly awe-inspiring. Let's not forget that data and analytics have been a top priority for many organizations for 5 years now according to many industry analysts. So, why did organizations fail to realize the potential in these areas? Organizations will need to build a distinct competence in data management to solve the data challenges while at the same time solving the problem of rapid technology change in analytics and applications.

Taxonomies should contain domain relevant knowledge to describe aristotelian classifications helping understand "things" by describing common and different properties of relevant entities. Our "world" - so as many of us like to describe it or maybe to "feel it" nowadays is accelerating, while ressources (here mainly time and brain or "braintime") seem to get narrower, and (psichical) pressure might reduce further the same braintime (*). Most of us spend a good portion their time searching for "answers", "results" using wellknown search systems like Google, Altavista, Bing... The latters donate often useful answers in change of our search profiles, which are then "given" to marketing enterprises, intellicence companies (...) and other self useful institutions which tries to sell us costumized products. Yes, in a world of increasing pressure and scarsity (as suggested by some currently created circumstances) and diminuishing timebrain, having a real oracle at our side is maybe what many of us are secretely whishing when pressing the ENTER button on some search question... (See also Google's button "I feel lucky" to underline this piece of "reality") finally delegating the success of acquiring the needed "right" piece of knowledge to some external entity (the oracle) having more braintime (...) That answer or its main components might (already) be linked in the (automated) answering system!

Air New Zealand is dipping its toes into artificial intelligence, launching a customer service chatbot that helps passengers with common queries. The airline hopes Oscar, full name Bravo Oscar Tango, will become a "virtual travel assistant", helping passengers through every step of their journey. Oscar will initially help customers with commonly asked queries, which Air New Zealand says will save them time and offer a more personalised experience than searching a traditional Frequently Asked Questions section online. As with other artificial intelligence (AI) technology, Oscar will learn based on the conversations - verbal and text - people have with him, becoming more user-friendly and helpful the more he interacts. Air New Zealand chief digital officer Avi Golan said Oscar had been launched as a beta or early stage product allowing customers to play an active role in training him.

The truth is that even the experts don't completely understand how neural nets work. If you feed enough photos of a platypus into a neural net, it can learn to identify a platypus. If you show it enough computer malware code, it can learn to recognize a virus. If you give it enough raw language--words or phrases that people might type into a search engine--it can learn to understand search queries and help respond to them. In some cases, it can handle queries better than algorithmic rules hand-coded by human engineers.

A fruit sorting robot built by a British consulting firm may not sound like a riveting leap forward. In fact, it's one of the clearest indications yet that industrial automation is on the verge of a big change and that we may be entering a new industrial revolution. Later this month, Cambridge Consultants will demonstrate its fruit sorting robot at the AgriTechnica show in Germany. During the demonstration, fruit will be stacked randomly in a bowl. The robot will use machine vision and smart software to identify the piece on top.

AI might be a hot topic but you'll still need to justify those projects. When I was eight, I got lost in the woods on a camping trip with my mom. Rangers eventually found me and hiked me out, which is actually kind of miraculous. I was a couple miles from where they assumed I'd be, trudging haplessly through a remote backwoods area on unmarked terrain. Every year hundreds of thousand people get lost in the wild worldwide.

AI might be a hot topic but you'll still need to justify those projects. The Toyota Research Institute (TRI) just announced its technology leadership team at CES. In November Toyota announced an initial five-year, $1 billion investment in TRI, which will be a research and development enterprise designed to bridge the gap between fundamental research in robotics and artificial intelligence and product development. In other words, the mandate is to develop all the cool AI stuff happening in labs and DARPA-backed research projects and bring it to market. Comparisons have been drawn to famous industrial laboratories like Bell Labs and PARC, which are jointly responsible for an impressive chunk of silicon-age advances. Some of TRI's specific mandates are to enhance the safety of automobiles, with the ultimate goal of creating a car that is incapable of causing a crash; to increase access to cars to those who otherwise cannot drive, including the handicapped and the elderly; to help translate outdoor mobility technology into products for indoor mobility; and to accelerate scientific discovery by applying techniques from artificial intelligence and machine learning.

AI might be a hot topic but you'll still need to justify those projects. In partnership with Yamaha, robotics developer SRI has created a humanoid that can ride an unmodified motorcycle. Motorcycle racing is about the most thrilling lesson in physics and material science imaginable. In order to turn a motorcycle, riders need to lean. Thanks to some truly extraordinary tires, riders flirt with the terminal edge of physics at screaming speeds during race laps, leaning their bikes far enough to scrape knees, shoulders, and elbows.

A scanning robot from 4D Retail Technology can scan an entire grocery store in about an hour. AI might be a hot topic but you'll still need to justify those projects. The reason you're hearing more about robots these days has a lot to do with non-robotic technologies. After all, for the last fifty years mechanical engineers have been able to make some pretty snazzy machines that move on their own. It's only with the rise of complementary sensor and computing technologies that robots are starting to show their true usefulness outside of factories.

SAS on Tuesday marked the general release of SAS Factory Miner, an automated tool that uses machine learning techniques to develop, test and identify hundreds of best-fit predictive models within minutes. Announced last month, Factory Miner promises better, segment-specific predictive performance, and it also goes a long way toward easing the analytic talent shortage. See how the cloud is disrupting traditional operating models for IT departments and entire organizations. SAS Factory Miner is a response to all of these imperatives. It helps companies with find-grained segmentation by automating model building across hundreds of segments and, potentially, thousands of sub-segments.