With an aim to lead the world of smartphones with its artificial intelligence (AI)-based technology, Google on Tuesday launched much-awaited Pixel -- a new premium device completely designed by the tech giant with Google Assistant built right-in -- at a special event here. Don't Miss: Black Friday 2016: Everything You Need to Know Now The launch also ended the Nexus branding under which the company has always released phones in partnership with other original equipment manufacturers like LG (for Google Nexus 5) and Huawei (for Google Nexus 6P). Although HTC has manufactured the smartphones, the new device bears Google branding. With curved sculpted edges and a unibody made up of combination of aerospace grade aluminum and glass, the device comes in two sizes -- 5 and 5.5-inch with 2.5D Corning Gorilla Glass 4 protected super AMOLED display. Pixel is available in two -- quite black and very silver colors in India.

Google has announced its new Pixel and Pixel XL phones, as expected, which will be powered by Google's new Google Assistant. Google launched the Pixel in both a standard as well as a jumbo size, the Pixel XL. But the 5-inch Pixel and 5.5-inch Pixel XL aren't noteworthy because of their hardware, but because of the software and services attached to them: not only will users be able to launch the interactive Google Assistant from their home buttons, but the phones will come with an unlimited amount of cloud storage for photos and videos at original quality --even 4K!--compliments of Google. The Pixel will cost 649 (or 27 per month) exclusively from Verizon. Google didn't provide a price for the Pixel XL.

Google's Pixel smartphones leaks have surfaced again. This time, the devices appeared on a listing on Verizon, which has since been removed. The listing showed three color options – blue, silver and black. The storage options are shown to be 32GB and 128GB. A mysterious feature called "Google Magic" has been mentioned.

Too often, machine learning requires a massive investment of time and terabytes of data before it can deliver meaningful insights. But that doesn't have to be the case. A well-configured machine learning analytics tool can rapidly provide initial results -- a key advantage for developers who can then start using those results to create value. To understand this dynamic, companies have to start taking a data approach that embraces the fact that what is driving companies in today's connected world is not data, but insight. And the volume of data being created in today's connected world is not just what powers this insight…but also blocks you from finding it.

Still, Mark Geistfeld, Sheila Lubetsky Birnbaum Professor of Civil Litigation at The New York University School of Law, believes Apple and similar firms have a responsibility to explore ways to cut down on distracted driving. One such technological solution might be for smartphone makers to disable or limit a device's functionality when it's traveling at driving speeds. Jennifer H. Arlen, Norma Z. Paige Professor of Law at The New York University School of Law, says that makes texting-while-driving cases a "very good place for liability imposed directly on the person who misuses [a phone], and perhaps criminal statutes that enhance the penalties on people who have serious accidents while texting and driving." Apple calls its system CarPlay, for Android devices there's Android Auto.

Customer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers. Banks, telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics (along with cash flow, EBITDA, etc.) because the cost of retaining an existing customer is far less than acquiring a new one. Companies from these sectors often have customer service branches which attempt to win back defecting clients, because recovered long-term customers can be worth much more to a company than newly recruited clients. Churn prediction is one of the most popular Big Data use cases in business. It consists of detecting customers who are likely to cancel a subscription to a service.

Seven out of ten corporate executives say they are making significantly more investments in artificial intelligence (AI) than just two years ago, according to Accenture's recent Technology Vision survey. And more than half say they plan to use machine learning and embedded AI solutions extensively. The race toward a digital future has begun and within the next five years, mastering the impact of this technology on future strategy will be a critical task for every CEO. Computational speed, machine learning and natural user interfaces have all advanced to the point where computers can do jobs that, previously, only humans could do. Intelligent digital labor is set to spark a radical change in labor dynamics, with research from market analyst firm, Gartner, suggesting that by 2030, virtual talent spending will exceed 10 percent of human staff costs.

The next time you need to Google something, you may end up doing so via text message rather than typing in a search query or saying "Ok, Google." Allo includes a few capabilities that make using it feel notably different than sending texts through your phone's default SMS service. For one, it includes the company's new Google Assistant, which surfaces answers to questions and makes suggestions directly within your chat window. Allo can intelligently suggest responses to text and photo messages through a feature called Smart Reply. As other messangers like LINE and Facebook Messenger, Google's new messaging app offers an array of stickers to choose from.

Abstract--One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for specific graph signal denoising and interpolation problems. The philosophy, to design the ARMA coefficients independently from the underlying graph, renders the ARMA graph filters suitable in static and, particularly, time-varying settings. The latter occur when the graph signal and/or graph topology are changing over time. We show that in case of a time-varying graph signal our approach extends naturally to a two-dimensional filter, operating concurrently in the graph and regular time domain. We also derive the graph filter behavior, as well as sufficient conditions for filter stability when the graph and signal are time-varying. The analytical and numerical results presented in this paper illustrate that ARMA graph filters are practically appealing for static and time-varying settings, as predicted by theoretical derivations. Keywords-- distributed graph filtering, signal processing on graphs, infinite impulse response graph filters, autoregressive moving average graph filters, time-varying graph signals, time-varying graphs. Due to their ability to capture the complex relationships present in many high-dimensional datasets, graphs have emerged as a favorite tool for data analysis.

We need the following manual steps to complete the data factory.