As futuristic as the year 2025 sounds, it is now less than eight years away. Will time travel, flying cars and summer vacations to Mars be in the cards? Probably not, but drone delivery, chatbots and an AI-driven retail industry are near certainties. And the people driving these changes, the "digital native" generations, are quite open to disrupting, if not destroying, old retail practices. Sure, McKinsey predicts brick-and-mortar stores will still account for approximately 85% of U.S. retail sales in 2025, but will the shopper journey be the same as it is today?

The use of artificial intelligence in retail settings remains a vague and experimental concept, but early results from lingerie chain Cosabella suggests that even early uses of automation can drive online-to-offline sales. Emarsys artificial intelligence (AI) enabled marketing automation platform to enhance customer engagement and acquisition, and conversion growth. The Italian-based chain, which operates boutiques in New York and Florida in addition to selling its brand at Nordstrom department stores, began using an AI platform operated by mobile tech provider Emarsys in October 2016. The specific tools Cosabella has been using includeEmarsys's Automation, Predict Web Extend, Smart Insight and CRM Ads products. Since the integration of that platform, Cosabella says it has seen a doubled its email subscriber list.

Artificial intelligence is all around us, from searching on Google to what news you see on social media to using Siri. And with the momentum around AI growing every day, it's not surprising that some of the most innovative retail sites have recently been experimenting with the use of AI, as well. Businesses that ignore this growing trend will find themselves playing catch-up for years. The big question is how exactly is this new technology going to change retail. E-commerce is a space with a lot of potential, in part because it's such a data-rich industry, and, there's some momentum around AI gathering already. What's more, a lot of the AI techniques that are enjoying success in other applications are well-positioned to make serious impact on the space, streamlining retail processes and transforming the online experience into something more like talking to an experienced salesperson at a brick-and-mortar location.

There is growing concern about the impact of automation on employment - or in crude terms - the threat that robots will eat our jobs. But if you want to see how important robotics and artificial intelligence can be to a business Ocado is a good place to start. "Without it we simply couldn't do what we do at this scale," the online retailer's chief technology officer Paul Clarke told me. With margins in the supermarket business wafer thin, continually bearing down on costs and waste has been vital. At its Hatfield distribution centre I got a glimpse of how far the process of automating the sorting of thousands of grocery orders has come.

Online grocery retailer Ocado is increasingly turning to automation. In its distribution warehouse in Hampshire, swarms of robots collect groceries. The BBC had a look at some of the robotics the firm is working on.

A "mystery benefactor" bought the copies of George Orwell's dystopian satire at Booksmith and asked that they be given away free to interested customers, the San Francisco Chronicle reports. Booksmith is located in San Francisco's famously progressive Haight-Ashbury district. The Orwell books were claimed, but the giveaway idea caught on. Another patron purchased copies of Margaret Atwood's novel "The Handmaid's Tale" and Erik Larson's nonfiction book "In the Garden of Beasts: Love, Terror, and an American Family in Hitler's Berlin" to be given away to shoppers. Orwell's "1984," a dark vision about a Britain taken over by a totalitarian regime that uses "doublethink" and "Newspeak" to mislead and control its citizens, has seen a spike in sales since late January, after President Trump's advisor Kellyanne Conway appeared on "Meet the Press" and used the term "alternative facts" to defend the administration's provably false statements about the size of the crowds at the inuaguration.

Amazon has made plans for a two-storey, automated supermarket run by robots. A staff of robots on the top floor will automatically grab shopping from shelves and bring it down to customers below meaning that each store could be run by only three staff at a time. The ground-level of the prototype store is a supermarket-sized version of the company's recently announced'Amazon Go' convenience store. Amazon has made plans for a two-storey, automated supermarket run by robots. The rumoured robot-run supermarkets are not Amazon's first foray into the world of grocery shopping.

The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications.

This seems to be the very first book on this ML framework (H2O). And is is just great. The book is crystal clear and extremely comprehensive, very easy to read, with examples you can reproduce easily (datasets are on line in a public Git repo). It covers a very practical ground on the 4 main algorithms implemented in H2O cluster: RandomForest, GBM, GLM, and last but not least: deep learning... "Practical" means explanations are strongly grounded on a set of 4 datasets, the author plays with, explaining both their preparation, analysis with H2O (code is both in R and PYTHON), and a great deal of time is spent on very useful considerations on how to'tune' the various algorithms to obtain better models, comparing their effectiveness. A must have for everyone interested in implementing ML features concretely.

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