Adaptive behavioural analytics software reduces'genuine transactions declined' by over 70% and incidence of undetected fraud by 25% Oakhall, the London based analysis firm, estimates that global financial services firms could save at least 12 billion annually by employing adaptive, machine learning fraud management systems according to a study published in conjunction with Featurespace. For the full study see http://www.featurespace.co.uk/cost-of-card-fraud. By employing adaptive behavioural analytics software to both identify actual fraudulent transactions, and reduce the number of'genuine transactions declined' - as well as reducing the costs associated with managing blocked customers - the industry could reduce the 31 billion total annual cost of card fraud by over 12 billion annually. Featurespace is a world leader in adaptive behavioural analytics software. Its services and products are employed in over 180 countries via a wide range of customers, including the leading US payments processor, TSYS, as well as Vocalink/Zapp, William Hill and Betfair.

This program offers dual career track such that the candidates enrolling this program have the option of choosing to become a data scientist or a data engineer. This program relishes an amazing support of industry stalwarts. The class size happens to be relatively small which allows the instructor to pay attention to every candidate.

Take part in this CrowdChat, hosted by @IBMBigData, to explore Apache Spark's powerful machine learning capabilities while taking a look at the future of Spark. When you do, you'll be able to interact with subject matter experts as they focus on Spark-related technologies and trends, including SystemML and structured streaming. If this event will be your first experience with CrowdChat, don't worry--a CrowdChat merely organizes tweets into streams of conversation. We'll start a conversation thread, and everyone who wants to participate can do so simply by commenting, allowing us all to enjoy an engaging discussion. Vote for your favorite comments so that they can be featured prominently in the conversation.

Google's strategic move into selling own branded Mobile phones is another step in the merging of "Software plus Hardware" that Apple, Microsoft, Amazon and recently Facebook have realized at the making of the "Internet of Things" Era. This is the critical issue of not just providing the software and operating system but increasing the value in the devices that become the Interface to the Customer: the smart phone, the smart tablet/laptop of Microsoft Surface, the Smart Speaker of Amazon Echo and Alexa, and the Facebook Oculus Rift and Microsoft Hololens that are the new foundations of Natural Language speech recognition services and the VR Virtual Reality and AR Augmented Reality breaking now and into 2017 and onward. Google's long-term market is changing, the advertising revenue from search engines while still strong is now seeing new ways to search via speech or Virtual image recognition and virtual interaction Google has been late to realizing perhaps the shift to software hardware is where the Internet of Things may be shaping the market with the Connected Home, Connected Car and Connected Work through these devices. It's all about "market marking" beyond just the big cloud data centers and big data analytics to how to build out the edge of the cloud network with all these potentially billions of connected sensors and devices. If the Mobile phone is becoming the "remote control to this world" and platforms the "fabric of social networks and connected experiences" then Google like others is rushing to get into this space with stronger software and hardware offerings

Human beings are social animals. As part of our neural wiring, humans assess each other's emotional cues on a subconscious level without realizing it. However, some people, such as individuals with autism, have more trouble interpreting these cues. Some cues are difficult for everyone to read. Other cues are even intentionally misleading.

Microsoft has made no secret of its grand plans for chat bots, and this week it rolled out five new ones for Skype. Surely the most fun is "Spock," a bot that promises to help you "learn the ways of Vulcan logic." Back in April, Microsoft debuted a preview of Skype bots, the artificial intelligence-based helpers it hopes will make it easier for users to get things done. Today, more than 30,000 developers are building bots for Skype, it says. The five new ones introduced this week were all inspired by partners and developers, and they're all now part of the Skype Bot directory in the service's Android, Windows, iOS, Mac, and web apps.

Organizations are moving more and more of their analytics workloads to the cloud in a bid to reduce operating expenses. One of the vendors trying to capitalize on the trend is Microsoft Corp., which last week rolled out a set of major improvements to its managed Hadoop service in an effort to attract more enterprise customers. Arguably the most important addition is support for the new LLAP (Live Long And Process), a method of caching data in memory to speed up database analytics in the Apache Hive data warehouse. It enables analysts to run database queries up to 25 times faster than before. It's joined by a set of security features that make it possible to regulate who accesses what data, monitor activity and use a company's internal cryptographic keys for encryption.

For better or worse, airlines have treated social media as a critical customer service tool, catering to cranky travelers who tweet their gripes. That willingness has led to a deluge of issues for customer service to address -- and created the need to automate it. In a typical week, KLM has to respond to 15,000 social conversations in a dozen different languages. There is no programmatic solution here: KLM has a 235-person social media team. But KLM is exploring ways to combine artificial intelligence and humans in providing customer service that's somewhat automated but still has a personal touch.

But is that a problem for consumers, the economy, and labor? Traditional economic thought says yes, it's a huge problem, and that seems to be the argument The Atlantic's headline writer is grasping at here. Yet the actual contents of Derek Thompson's piece paint a more muddled picture. People generally love tech giants like Amazon, Apple, and Google, all of which still innovate plenty. Yet people hate borderline monopolistic firms in other industries, such as finance (see: Occupy Wall Street), retail (Walmart), and cable companies (Comcast, Time Warner).

If you are NOT using Model Evaluation Library you may skip this release. CNTK (http://www.cntk.ai/), the Computational Network Toolkit by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. This project has adopted the Microsoft Open Source Code of Conduct.