There's also The Book of AI, describing "How To Build Chat Bots with The Personality Forge," which is "an advanced artificial intelligence platform for creating chat bots." Still in the Early Stages Despite the aforementioned uptick in interest, Forrester Research analyst Julie Ask earlier this month provided a word of warning, listing "a few big hurdles standing in the way of bots becoming the next big thing in 2016": However, like most in the development community, Ask is generally optimistic about bots making a major impact in our lives and the lives of developers. "Bots, and the chat platforms they run on, provide an amazing opportunity for brands to deliver contextual experiences on borrowed mobile moments," Ask said. "This is the first step on a journey towards a bright future where consumers no longer orchestrate their needs through content and services, but sit back and let the technology work for them."

Talk held on Feb, 3rd 2016 "Four years ago we started the Google Brain project, a small effort to see if we could build training systems for large-scale deep neural networks and use these to make significant progress on various perceptual tasks. Since then, our software systems and algorithms have been used by dozens of different groups at Google to train state-of-the-art models for speech recognition, image recognition, various visual detection tasks, language modeling, search ranking, language translation, and various other tasks. We have recently open-sourced TensorFlow, our second generation software system for developing and deploying models. In this talk, I'll highlight some of the distributed systems and algorithms that we use in order to train large models quickly. I'll then discuss ways in which we have applied this work to a variety of problems in Google's products, usually in close collaboration with other teams."

I just couldn't help jotting this down and thought you might need an intro. The other day I was at an ML (Machine Learning) event with Google ML and a bunch of successful A.I. companies and investors in the field from London. Intimidated by the grandeur and potential of the work these guys do, I was really curious to hear their predictions, personal prospects on where tech is heading these days. As for myself, I have a feeling we are at doorsteps of a nascent era. Of course they talked bots in the end.

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).