Qualcomm has a vested interest in smarter mobile devices: it wants to power even more of them with its own Snapdragon chips. To that end, today sees the launch of the Qualcomm Snapdragon Neural Processing Engine an SDK for the Snapdragon 820 chipset that promises a simpler path to deep-learning software and artificial intelligence on phones, tablets, wearables, and even in cars. It's all powered by Qualcomm Zeroth Machine Intelligence Platform, and while Zeroth may sound like an enemy for the Power Rangers to fight, it's actually a way to leverage formally cloud-trapped processing entirely on a local device. Traditional machine learning - the ability to assess the world and categorize elements within it - generally requires masses of processing grunt and a big database against which to check the potential results. What Zeroth does is condense that down into a single chipset, taking advantage of the various components of Snapdragon like the Hexagon DSP, Adreno graphics, and more.

Qualcomm Technologies, with the introduction of the Snapdragon Neural Processing Engine, is the first mobile SOC provider to offer a deep learning toolkit optimized for mobile. This SDK will allow OEMs to run their own neural network models on Snapdragon 820 devices such as smart phones, security cameras, automobiles and drones, all without a connection to the cloud. Common deep learning user experiences that can be realized with the SDK are scene detection, text recognition, object tracking and avoidance, gesturing, face recognition and natural language processing. The Zeroth Machine Intelligence Platform is a Snapdragon-optimized software platform designed for mobile machine learning. Zeroth technology currently drives visual intelligence software such as Snapdragon Scene Detect and advanced malware detection software found in Snapdragon Smart Protect.

Qualcomm has talked about putting "silicon brains" in mobile devices and is now providing tools to train smartphones to recognize people, objects, gestures, and even emotions. Phones like Samsung's Galaxy S7 and LG's G5 that use Qualcomm's Snapdragon 820 chips will get deep-learning capabilities with the Snapdragon Neural Processing Engine software development kit, announced on Monday. The SDK will include a run-time that will exploit chip features so smartphones can accomplish deep-learning tasks like tracking objects and recognizing sounds. The kit could also be used in self-driving cars and autonomous drones and robots. Computers can already recognize people in images, as seen on Facebook.

In late 2015, our Chief Customer Officer, Joe Gagnon, and I, met with the IT Director, and the COO of Radisson Blu Edwardian in London. A long-term customer of Aspect's, we were there to share what we have been so diligently working on over the past year: our vision for re-imagining customer service that would combine the best of all forms of consumer interaction types, and the best of what we and the industry have been able to develop in next generation CX technology. In essence, the vision for re-imagining customer service has at its core how to use Interactive Text Response (ITR), or what is also known as "bots" to provide the ability to let customers self-serve on text channels at blazing-fast speeds with a User Interface that resembles that of a natural conversation with a person. Needless to say, it didn't require much convincing that this approach would provide an opportunity to "surprise and delight" in their prestigious hotel chain. With over 2 billion people using texting and messaging solutions extensively today, offering service over these channels just makes sense. Furthermore, it has the promise of saving cost through smart automation while providing a state-of-the-art customer experience, or in the words of the COO: "I want this by Monday."

Yesterday morning I woke up, put on a pot of coffee, and checked the news. I wanted to revisit the New Hampshire primary results that had rolled in the night before. I opened Quartz's new app and was greeted with a text message: "Yep, it's really happening: Trump and Sanders won big in New Hampshire." Below it appeared side-by-side portraits of Trump's scowl and Bernie's grin. To read more, I tapped a ready-made text reply containing a donkey, an elephant, and an American flag emoji.

Effective data governance consists of protocols, practices, and the people necessary for implementation to ensure trustworthy, consistent data. Its yields include regulatory compliance, improved data quality, and data's increased valuation as a monetary asset that organizations can bank on. Nonetheless, these aspects of governance would be impossible without what is arguably its most important component: the common terminologies and definitions that are sustainable throughout an entire organization, and which comprise the foundation for the aforementioned policy and governance outcomes. When intrinsically related to the technologies used to implement governance protocols, terminology systems (containing vocabularies and taxonomies) can unify terms and definitions at a granular level. The result is a greatly increased ability to tackle the most pervasive challenges associated with big data governance including recurring issues with unstructured and semi-structured data, integration efforts (such as mergers and acquisitions), and regulatory compliance.

Fresh off its promotion at Facebook's F8 developer conference, Poncho announced today that it has raised 2 million for its personalized weather forecasting service. The round was led by Lerer Hippeau Ventures and will be earmarked for improvements to Poncho's natural language processing, in addition to building artificial intelligence and data science technology into its bots and apps. Participating investors include Greycroft Partners, Comcast Ventures LP, Venture51 Capital Partners, RRE Ventures, Betaworks, Broadway Video Ventures, Ore Ventures, and several angel investors. Started two years ago out of Betaworks, Poncho offers a weather forecast alternative to Yahoo Weather, AccuWeather, and The Weather Channel. The company seeks to dominate what CEO Sam Mandel calls "thin content," which is activity that "takes place within the notification layer and also on a messaging platform that's contextually relevant, customized, and comes at the right time, but with enough polish to be engaging and cause a happy emotion."

Everyone knows about the giant skills gap that is haunting the IT sector worldwide. According to IoT company PTC, it is estimated that in the next ten years more than two million IT and communication jobs will be unfulfilled. To address this, several universities have come up with degrees that address the different skills needed in the IoT market, including user interfaces, networks, artificial intelligence, networking, and others. CBR lists ten courses being taught in the UK institutions. Offering both a full time or part time (12 and 24 months respectively) course, University of London's Royal Holloway has built a degree based on computer science, technology and engineering.

Lots of activity among tech stocks Friday, as several companies' shares are on the move. Revenue and earnings were down for the tech giant during the third quarter, as it continues its push toward mobile and cloud computing. Microsoft reported earnings of 47 cents a share, down 25% year-over-year. All those investments in self-driving cars and broadband Internet are costing the tech giant. Operating losses from the division behind Alphabet's "moonshots" widened to more than 800 million.

Socio-economic maps contain important information regarding the population of a country. Computing these maps is critical given that policy makers often times make important decisions based upon such information. However, the compilation of socio-economic maps requires extensive resources and becomes highly expensive. On the other hand, the ubiquitous presence of cell phones, is generating large amounts of spatiotemporal data that can reveal human behavioral traits related to specific socio-economic characteristics. Traditional inference approaches have taken advantage of these datasets to infer regional socio-economic characteristics. In this paper, we propose a novel approach whereby topic models are used to infer socio-economic levels from large-scale spatio-temporal data. Instead of using a pre-determined set of features, we use latent Dirichlet Allocation (LDA) to extract latent recurring patterns of co-occurring behaviors across regions, which are then used in the prediction of socio-economic levels. We show that our approach improves state of the art prediction results by 9%.