Venturebeat * note that "Although the big data market will be nearly 50B by 2019 according to analysts, what's most exciting is that the disruptive power of machine data analytics is only in its infancy. Machine analytics will be the fastest growing area of big data, which will have CAGR greater than 1000%." The move towards cloud based solutions opens up opportunities and it is not going to reverse. Following on from the trend in recent years yet more and more companies are increasing their use of cloud based solutions and along with this the opportunity to extract and collect data provides a potential for gleaning some information and knowledge from that data. Suhale Kapoor, Co-Founder and Executive Vice President, Absolutdata Analytics * highlights "The fast shift to the cloud: The cloud has become a preferred information storage place. Its rapid adoption is likely to continue even in 2016. According to Technology Business Research, big data will lead to tremendous cloud growth; Revenues for top 50 public cloud providers shot up from 47% in the last quarter of 2013 to 6.2 billion" Applications will be designed to discover self improvement strategies as a new breed of log and machine data analytics, at the cloud layer, using predictive algorithms, enables; continuous improvement, continuous integration and continuous deployment.

Business agility is becoming a strategic necessary. Companies cannot be competitive if they not staying ahead of their customers' expectations. You can see the effect of this when Apple introduced Siri in 2011 with the release of iPhone 4S, changing the customer experience. Since then, Google, Microsoft, and Amazon have all come up with their own A.I. concierge services to assure that they are meeting the customer expectations. Look at the current robotic interactive voice response (IVR) systems that require you to navigate through layers of menus to retrieve a simply answer: "Has my claim been paid?"

Microsoft has introduced a remarkable new Skype update aiming to hoist itself to greater heights by altering the face of the chat app into something far more sophisticated. The software giant has recently unveiled a game-changing Skype update that will transform it into a commanding platform for interacting with other apps. According to reports, the new update will enable Microsoft's virtual assistant Cortana and other third-party apps to seamlessly integrate with Skype. With a huge surge in mobile apps in recent times, downloading, setting up, and managing a large number of newly introduced apps can be an exasperating prospect for many phone users, owing to many accessibility constraints. As a consequence, users end up confining themselves to a mere handful of user apps.

She was supposed to come off as a normal teenage girl. But less than a day after her debut on Twitter, Microsoft's chatbot--an AI system called "Tay.ai"--unexpectedly turned into a Hitler-loving, feminist-bashing troll. TechRepublic turns to the AI experts for insight into what happened and how we can learn from it. In 2015, GE inaugurated a new, Multi-Modal manufacturing facility in Chakan, India. If the company's ambitions for the space are realized, it could drive a massive change in global manufacturing.

Many of the jobs humans would like robots to perform, such as packing items in warehouses, assisting bedridden patients, or aiding soldiers on the front lines, aren't yet possible because robots still don't recognize and easily handle common objects. People generally have no trouble folding socks or picking up water glasses, because we've gone through "a big data collection process" called childhood, says Stefanie Tellex, a computer science professor at Brown University. For robots to do the same types of routine tasks, they also need access to reams of data on how to grasp and manipulate objects. Where does that data come from? Typically it has come from painstaking programming.

In The Social Express, a cast of animated characters help kids with autism learn helpful social skills. Both Katie and her teacher look like they'd be right at home in a Pixar film, and at first their conversation seems like it would fit in one too. The ponytailed and pink-clad Katie really wants to sharpen her pencil, but her teacher won't let her until the other kids in the class finish taking a test. Katie asks again, but the teacher offers the same, frustrating answer. "Katie seems upset that her teacher said'no.'

Second in a series of two articles about the history of signature-based detections and how the methodology has evolved to identify different types of cybersecurity threats. Many security vendors are now applying increasingly sophisticated machine learning elements into their cloud-based analysis and classification systems, and into their products. All of these techniques have already proven their value in Internet search, targeted advertising and social networking business arenas. For example, supervised learning models lie at the heart of ensuring that the best and most applicable results are returned when searching for the phrase "never going to give you up." In the information security world, supervised learning models are a natural progression of the one, two, and multi-dimensional signature systems discussed in my earlier article.

Everyone on the internet had a great time with Tay, Microsoft's Twitter robot that became a racist Holocaust denier in a matter of a few hours (then came back and did it again). The company had created a public relations flap -- more incident than a disaster -- while giving the public an object lesson on the pros and cons of machine learning: Automation can harness patterns to fascinating effect at speed, but the results will be predictably hard to predict. As is often the case, the military is an early adopter of automation technology. It is -- at one time -- leading the charge toward machine learning and also trying desperately to keep up. One of the main areas of focus for the Pentagon is autonomous robots and how they will team with humans – a R2D2-style robot wingman, for instance. But this week, Deputy Secretary of Defense Robert Work outlined another task for A.I.: open-source data crunching.

Advances in cognitive computing herald a disruptive new era in business intelligence and customer engagement, opening the door to a more intuitive computing experience. A recent survey of top CEOs showed that half of those questioned believe it will revolutionise their business. But what does it all mean and how can today's businesses stay ahead of the curve? IBM Watson is at the forefront of the cognitive revolution and is already in use today. We spoke to Duncan Anderson, European CTO for Watson at IBM to find out more about the machine learning revolution and how businesses of all sizes can harness it's power.

R has been the gold standard in applied machine learning for a long time. Surveys show that it is the most popular platform used by professional data scientists. It is also preferred by the best data scientists in the world on the competitive machine learning site Kaggle.com. In this mega Ebook written in the friendly Machine Learning Mastery style that you're used to, learn how to get started, practice and apply machine learning using the R platform. As a developer you know how to pick up a new programming language quickly.