Sling TV's line up of available channels is getting bigger. The streaming TV service is adding nine new channels from Discovery Networks that offer live and on-demand content, including the flagship Discovery Channel and MotorTrend. The best news for Sling subscribers: some of the channels will be added to your package for free. Access to the channels will be split across Sling's two separate service packages, both of which cost $25 per month. Sling Blue will get Discovery Channel, Investigation Discovery and TLC.

Spotify is finally arriving in India, according to numerous reports. The streaming music service will look to take on YouTube, the biggest such site in India at the moment which offers largely free streaming of music. Initially, Spotify will also offer its service for free, with a much longer trial than the 30 days it usually offers to new users. Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Jeffrey Katzenberg and Meg Whitman will keynote Variety's Innovate: AI and Data Science in Media summit, presented by PwC, on Dec. 5 in Los Angeles. The duo will discuss launching mobile entertainment platform Quibi. Previously CEO and co-founder of DreamWorks Animation, Katzenberg now invests in new consumer technologies as co-founder and managing partner of WndrCo. He is also founder/chairman of Quibi. Whitman, CEO of Quibi, served the same role at Hewlett Packard Enterprise until February of this year.

You know the story already. Artificial intelligence (AI) and the machine learning (ML) engines that drive it are about to reinvent every aspect of business and propel us forward into a new era of intelligent business systems, ultra-connected networks and sentient machines that can help manage and direct our workloads for us. AI is arguably already the over-talked technology subject of the decade in spite of its undeniable second era now showing us real applications for the real world -- it's first era being the fanciful stuff of Hollywood Sci-Fi movies throughout the 1970s and 1980s. One of the main stumbling blocks is business tradition, that is – all too many enterprises will do'things' the way they've always been done because those systems just kind of work. These same firms run with a'why fix it if it's not broken' mentality.

When developer Telltale Games laid off most of its staff in September, it temporarily retained a skeleton crew to finish up work on a Netflix version of Minecraft: Story Mode. The first three episodes of the "interactive adventure" are now available on the streaming platform. The adaptation is not so much a fully-fledged game as a choose-your-own-adventure experience -- you don't actually move protagonist Jesse around, for instance. Instead, you're occasionally given choices on how to move the story forward. The series starts with a character named Petra guiding you through a brief tutorial before asking whether you'd like to play as a male or female version of Jesse.

Some marketers eager for a new digital ad alternative are also conflicted about the rise of Amazon--a competing retailer with its own in-house brands to sell--setting up a new potential source of tension. Amazon's ad revenue is on pace to double this year, to $5.83 billion, according to eMarketer. Its ad sales are expected to jump $28.4 billion over the next five years, according to Cowen & Co.--more than the combined increases in ad revenue for all television networks globally, according to figures from media-buyer GroupM. The cumulative effect is an earthquake whose tremors will be felt by anyone selling ads, including digital publishers and TV networks. Retailers like Walmart Inc., Target Corp. and Kroger Co., which get paid by brands to place products in desirable locations within their stores, are already losing business to Amazon, ad executives say.

Our customers are living in a data-driven world and the volume of information they generate is growing exponentially. As a result, companies are increasingly investing in the hardware, software, and services needed to gain actionable insights from their data. By 2022, the total addressable market for artificial intelligence/machine learning (AI/ML) and big data is expected to grow to approximately $160 billion.[1] However, according to Gartner, by 2020 half of organizations will lack sufficient AI and data literacy skills needed to extract business value from their data, and they are already demanding easier-to-implement, faster-to-deploy, and more cost-effective solutions for AI/ML and big data analytics.[2] Today, HPE announced that we are acquiring BlueData, a leading provider of AI/ML and big data analytics infrastructure software, which will significantly expand our footprint in the rapidly growing artificial intelligence and big data analytics space.

Blockchain and AI, particularly machine learning, are two quite recent revolutionary technologies that are being adopted by Governments and Businesses in all sorts of ways. In a series of articles (in 4 parts) I reflect in what ways these technologies have the potential to impact the lives of ordinary citizens. Businesses such as the ORS Group are providing innovative tools that will incorporate lots of small businesses into the blockchain, and providing them with AI-based tools that will enable them massively scale up their operations. According to the ORS President Fabio Zoffi, ORS Group has created a new concept known as Hypersmart Contracts, which will serve as the backbone to connect small businesses into global players using he combination of the AI and the blockchain. HyperSmart Contracts are smart daemons with an associated Ethereum account which run second layer, AI algorithms off-chain, to find solutions for complex optimization problems.

In this paper, we propose and develop the novel idea of treating musical sheets as literary documents in the traditional text analytics parlance, to fully benefit from the vast amount of research already existing in statistical text mining and topic modelling. We specifically introduce the idea of representing any given piece of music as a collection of "musical words" that we codenamed "muselets", which are essentially musical words of various lengths. Given the novelty and therefore the extremely difficulty of properly forming a complete version of a dictionary of muselets, the present paper focuses on a simpler albeit naive version of the ultimate dictionary, which we refer to as a Naive Dictionary because of the fact that all the words are of the same length. We specifically herein construct a naive dictionary featuring a corpus made up of African American, Chinese, Japanese and Arabic music, on which we perform both topic modelling and pattern recognition. Although some of the results based on the Naive Dictionary are reasonably good, we anticipate phenomenal predictive performances once we get around to actually building a full scale complete version of our intended dictionary of muselets.

Digital camera pipelines employ color constancy methods to estimate an unknown scene illuminant, enabling the generation of canonical images under an achromatic light source. By taking advantage of large amounts of labelled images, learning-based color constancy methods provide state-of-the-art estimation accuracy. However, for a new sensor, data collection is typically arduous, as it requires both imaging physical calibration objects across different settings (such as indoor and outdoor scenes), as well as manual image annotation to produce ground truth labels. In this work, we address sensor generalisation by framing color constancy as a meta-learning problem. Using an unsupervised strategy driven by color temperature grouping, we define many related, yet distinct, illuminant estimation tasks, aggregating data from four public datasets with different camera sensors and diverse scene content. Experimental results demonstrate it is possible to produce a few-shot color constancy method competitive with the fully-supervised, camera-specific state-of-the-art.