Over the next 12 months, Artificial Intelligence (AI) and Augmented Reality (AR) look set to make a huge explosion into the mainstream. We've already seen the launch of various smart virtual assistants, such as Alexa, Amazon's Echo and Google Home, with four million people already having adopted a device in the UK. And Apple is due to join the voice-activated assistant space with its HomePod device that will be released towards the end of this year. Recently, there has been a lot of conversation around how these developing technologies could change the way we shop. With the rise of mobile shopping, the way we make purchases has been quickly evolving.

The artificially intelligent voice assistant market is slowly getting crowded, and Amazon, one of the first movers in the segment, is striving to differentiate itself from the rest. The retail giant is bringing out special offers, especially for those who use its voice assistant Alexa. Amazon has offered a discount of $20 on the Amazon Prime membership if you sign up through Alexa. You can simply give Alexa the voice command saying, "Alexa, sign me up for Prime" and you will be able to sign up for the one-year membership for $79. The company also announced exclusive deals for Alexa users which will be available starting Wednesday until July 17.

We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accurate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers and mitigate exposure to losses. The system at ASOS provides daily estimates of the future value of every customer and is one of the cornerstones of the personalised shopping experience. The state of the art in this domain uses large numbers of handcrafted features and ensemble regressors to forecast value, predict churn and evaluate customer loyalty. Recently, domains including language, vision and speech have shown dramatic advances by replacing handcrafted features with features that are learned automatically from data. We detail the system deployed at ASOS and show that learning feature representations is a promising extension to the state of the art in CLTV modelling. We propose a novel way to generate embeddings of customers, which addresses the issue of the ever changing product catalogue and obtain a significant improvement over an exhaustive set of handcrafted features.

A prototype Moby Mart is being tested in Shanghai, China. Per Cromwell, the project's lead designer, says four to six additional mobile supermarkets are planned in the coming year. A prototype Moby Mart is being tested in Shanghai, China. Per Cromwell, the project's lead designer, says four to six additional mobile supermarkets are planned in the coming year. Browse the science fiction aisles and you can find all sorts of dystopian future visions -- environmental catastrophes, robot overlords, zombies swarms, triffids.

Over the course of the last few years, eCommerce has sprung up and put the brick-and-mortar side of the retail industry on notice. A growing number of eCommerce-focused merchants like Amazon have gradually increased their footprint in the physical store space. With the opening of the Amazon Go grocery store and bookstore in Seattle, Amazon is breathing innovation into the stodgy brick-and-mortar experience. It is also changing the retail game by eliminating the need for checkout stations through its artificial intelligence (AI) infused checkout system in its Amazon Go grocery store. In combination with its recent $13.7 billion acquisition of Whole Foods, it may be safe to assume that merchants in the retail space will follow Amazon's integration of more technology into the in-store experience.

Whether customers feel good or bad about their experiences is ultimately underpinned by their emotions. This doesn't matter what context they are in -- every interaction is emotional. As consumers, our shopping experiences mean quite a lot to us. If we stand in ridiculously big queues or receive terrible customer service, we are most likely to stay far away from or never return to that store. On the other hand, however, if we are delighted with the store's customer service, the product range or even the floor layout, we are most likely to continuously purchase and build a relationship with the brand. Thus it is how we feel about and interact with a brand that drives our consumer decisions.

Probabilistic forecasting, i.e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes. In retail businesses, for example, forecasting demand is crucial for having the right inventory available at the right time at the right place. In this paper we propose DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an auto-regressive recurrent network model on a large number of related time series. We demonstrate how by applying deep learning techniques to forecasting, one can overcome many of the challenges faced by widely-used classical approaches to the problem. We show through extensive empirical evaluation on several real-world forecasting data sets that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work.

The problem with regulating technology companies is that, faced with tough new rules, they can eventually innovate their way out, often by switching to newer, unregulated technologies. The risk of targeted regulation informed by little other than economic doctrines might even be fuelling a corporate quest for eternal disruption: instead of surrendering to the regulators, technology firms prefer to abandon their old business model. It's through this lens that we should interpret the likely fallout from the €2.4bn fine imposed on Alphabet, Google's parent company, by the European commission. It arrives after a lengthy, seven-year investigation into whether the company abused its dominance to promote its own online shopping service above search results. The commission's case seems sound; the sad fate of small online retailers, unable to compete with Alphabet over the past decade, suggests as much.

If you generate machine learning (ML) models, you know that the key challenge is exporting and importing them into other frameworks to separate model generation and prediction. Many applications use PMML (Predictive Model Markup Language) to move ML models from one framework to another. PMML is an XML representation of a data mining model. In this post, I show how to build a PMML application on AWS. First, you build a PMML model in Apache Spark using Amazon EMR.

Nothing is certain but death and taxes -- not even investing. There are no guarantees when you're putting your money in the stock market, and investors looking for a sure thing are bound to be disappointed. Yet there are a few things investors can look for to give them a better chance of finding a successful investment. Find a company that's tapping into an emerging trend and is the dominant player in that field. Seek out an innovator that isn't afraid to take calculated risks to leverage the future.