Every decade or so, a new era of computing comes along that shapes everything we do. Much of the 90s was about client-server and Windows PCs. By the aughts, the Web had taken over and every advertisement carried a URL. Then came the iPhone, and we're in the midst of a decade defined by people tapping myopically into tiny screens. So what comes next, when mobile gives way to something else?

With all the success of BN, it is amazing and disappointing at the same time that there are so many fantastic results but so little practical advice, how to actually implement the whole pipeline. No doubt, BN can be implemented pretty easy in the training part of the network, but that is not the whole story. Furthermore, there are, at least, two ways to use BN during training. First, with a running average for mean/std values per layer which can later be used for unseen data. Second, to calculate the mean/std values for each mini-batch and then run a separate step to fix the statistics for the data at the end of the training.

All credits of the framework should go to Jeff Heaton - http://www.heatonresearch.com/encog/ The example code below will build a simple XOR Neural Network, the code is included in examples\xor-network.js This will run the same XOR example mentioned above. Should work on all Node.js Credits should go to Jeff Heaton for the original Encog Machine Learning Framework - http://www.heatonresearch.com/about/

To support public health reporting, the use of computers and machine learning can better help with access to unstructured clinical data--including in cancer case detection, according to a recent study. Often, the unstructured free text data made available by electronic health records is obtained by means that are "resource intensive, inherently complex and rely on structured clinical data and dictionary-based approaches," according to the authors of the study, published in the Journal of Biomedical Informatics. The researchers, from the Regenstrief Institute and Indiana University-Purdue University in Indianapolis, used about 7,000 pathology reports from the Indiana health information exchange to attempt to detect cancer cases using already available algorithms and open source machine learning tools. "We think that its no longer necessary for humans to spend time reviewing text reports to determine if cancer is present or not," Shaun Grannis, M.D., interim director of the Regenstrief Center of Biomedical Informatics, said in an announcement. "We have come to the point in time that technology can handle this. A human's time is better spent helping other humans by providing them with better clinical care."

In this edition of HPE Business Insights Studio, Paul Muller, vice president of strategic marketing at Hewlett Packard Enterprise, meets up with independent technology analyst Theo Priestley to discuss recent developments in artificial intelligence (AI). AI is a hot topic among technologists today, but the practical application to businesses isn't widely understood. This video defines AI and gives real-world examples of how business can take advantage of the latest buzzword in technology. Keep up with HPE Business Insights' articles, blogs, videos, and more by signing up to get highlights direct to your inbox, twice per quarter.

Global logistics provider DHL believes worldwide supply chains are beginning to undergo a fundamental transformation as more "artificial intelligence" is deployed to handle both the domestic and international movement of goods According to research conducted in support of its recent 2016 Logistics Trend Radar, DHL thinks the impact of data-driven and autonomous supply chains provides an opportunity for "previously unimaginable levels of optimization" in manufacturing, logistics, warehousing and last mile delivery that could become a reality in less than half a decade, despite high set-up costs deterring early adoption within the logistics industry. Brown selects Orbcomm's dry van trailer tracking solution Matthias Heutger, senior vice president for strategy, marketing & innovation at DHL, said in a statement that 15 of the 26 "key trends" identified in the company's annual trend radar report "are likely to make an impact in under five years" and thus bear careful watching by the global logistics industry. While the "Internet of Things" or "IoT" will also play a large role in more "intelligent supply chains" as well – a trend DHL noted in its trend report last year – security concerns regarding hacking, among other issues, is slowing down its adoption. IoT offers the potential to connect virtually anything to the Internet and accelerate data-driven logistics, DHL stressed; estimating that by 2020, more than 50 billion objects will be connected to the Internet, presenting an "immense" 1.9 trillion opportunity in logistics, by its reckoning. "Only a few logistics [IoT] applications with substantial business impact have materialized so far," DHL noted in its report. "This is largely due to a shortage of standards in the industry, security concerns, and the fact that recent IoT innovations have mainly been developed for the consumer market.

A group of MIT researchers has sketched out a way to address a gap in cybersecurity that exists between human and machine. Human-made rules, which are meant to alert the system of an attack, don't work unless an attack exactly matches one of those rules. Machine-learning measures typically rely on anomaly detection. Consequently, false alarms aren't uncommon and the system starts to distrust itself. Combine these two forces - man and machine - and that's when magic can happen, according to a group of researchers out of MIT's Computer Science and Artificial Intelligence Lab (CSAIL).

CALIFORNIA: Internet giant Google has asserted that its Artificial Intelligence(AI) and cloud computing is the most lucrative and promising businesses in the tech industry. That AI type of service-based business is fast becoming the new way to reap profits in the tech industry, the California-based tech giant said. "We've always been doing cloud, it's just that we've been consuming it all internally at Google. But as we have grown, really matured in how we handle our data center investments and how we can do this at scale, we have definitely crossed over to the other side to where we can thoughtfully serve external customers," Google CEO Sundar Pichai said. "We have been investing in machine learning and AI for years, but I think we're at an exceptionally interesting tipping point where these technologies are really taking off. That is very, very applicable to businesses as well. So thoughtfully doing that externally we view as a big differentiator we have over others," Pichai added.

China's Nasdaq-listed internet search engine giant Baidu Inc. said Friday it has formed a team in Silicon Valley dedicated to its self-driving car efforts. The announcement comes as Chinese officials rush to set up a road map for incorporating highway-ready, self-driving cars within three to five years. Baidu's Silicon Valley team will grow to more than 100 researchers and engineers, focused on research, development and testing, by the end of 2016, the company said in a statement Friday. The Beijing-headquartered firm is looking to work on areas "integral to self-driving car development, including planning, perception, control and systems." The team in Silicon Valley will be part of the company's newly created Autonomous Driving Unit.

Ensemble methods are techniques that create multiple models and then combine them to produce improved results. Ensemble methods usually produces more accurate solutions than a single model would. This has been the case in a number of machine learning competitions, where the winning solutions used ensemble methods. In the popular Netflix Competition, the winner used an ensemble method to implement a powerful collaborative filtering algorithm. Another example is KDD 2009 where the winner also used ensemble methods.