Like so much in Britain, you can credit the weather for this. Ocado has rolled out AI using Google's open-source TensorFlow to improve service at its customer call centre. The online food retailer took six months to build and install a system based on machine learning., using Python, C, Kubernetes on Google Compute with TensorFlow. The cloud-based AI will do the heavy lifting for inbound customer emails, opening and scanning for key words and context, before prioritising and forwarding them. On an ordinary day the system handles 2000 messages and double that at busy times such as Christmas.

I picked up this book over the weekend after seeing an ad on reddit. Wasn't sure what to expect because I haven't read any Calum Chace books before. Until seeing the ad I hadn't even heard of him. Anyway, economic survival is something I've been thinking about, and despite trying to find predictions, forecasts and timelines I mostly found nothing. So I gave this book a chance and was pleasantly surprised by the amount of thought put into it.

Artificial intelligence (AI) is becoming more and more prevalent in almost every facet of people's day-to-day lives, from its ability to defeat world-class chess players to its implementation into self-driving cars. AI is hot, flashy, buzzworthy and is the future of many industries and applications, but many consumers don't realize the subtle way it is already influencing and shaping the world of retail. Thanks to an abundance of consumer data at their fingertips, retailers have slowly and subtly begun rolling out applications for many sectors of the retail industry, everything from using AI to offer better product recommendations, to chatbots that can carry on an (almost) lifelike conversation with consumers and help push them toward checkout, to being utilized in image recognition systems. "The pertinent question isn't necessarily when but where you'll see AI deployed. And the truth of the matter is that AI can benefit essentially every step and process of eCommerce, from site layout to personalization to -- and this part is extremely important -- customer happiness," Andy Narayanan, vice president of visual intelligence at Sentient, an artificial intelligence software provider, wrote for Total Retail in a piece back in July.

DESCRIPTION Online fashion shopping is a multi-billion industry which is growing exponentially year over year. Does the problem space of providing an awesome online experience for customer without the typical touch and feel retail experience thrill you? Are you excited by the challenge of building large scale systems that will be used by millions of customers, day-in-day-out? If the answer is yes, we would like you to take a look at the Amazon Fashion Technology team, which is currently solving this problem. Experiences like which-size-fits-me, what-goes-well-with-this are huge challenges to solve in an online space.

Artificial intelligence, intelligence as exhibited by machines, is not something that is new to this world. Nearly twenty years ago IBM's supercomputer Deep Blue beat world chess champion Gary Kasparov. The win was symbolically significant and a sign that artificial intelligence was catching up with human intelligence. Fast forward 20 years and the application of AI technologies is something we encounter on a regular basis. For example, manufacturing and the use of robots in assembly and packaging has revolutionised how our favourite products are made.

Amazon uses a mix of people and robots in its warehouses. The online retail giant announced Thursday that it plans to add 120,000 seasonal workers this holiday season. The jobs will be all across the U.S, with the company looking to bolster its fulfillment centers, sortation centers and customer service sites in advance of the yearly shopping madness. Last year the company added 100,000 holiday workers, of which over 14,000 became full-time employees, a number the company expects to grow this year. In a note sent out in September, Piper Jaffray analyst Gene Munster estimated that 44% of the U.S. population now lives near an Amazon warehouse or delivery station.

New research has shown that grocery retailers are struggling to optimise stock replenishment processes, with almost half saying that their decisions are still based on'gut feeling'. Retail applications provider Blue Yonder surveyed 750 grocery managers and directors in the US, UK, Germany and France. It found that, in spite of a rise in accurate algorithms for automated replenishment and demand planning, 46% of surveyed directors in the UK say that replenishment is still an entirely manual process and the same amount saying that it was fully automated. A further 30% believed that instinct-based decision making was slowing them down. Of the four countries involved in Blue Yonder's survey, Germany had the highest proportion of respondents using manual or partially automated systems, with just one-third of managers who had fully automated their stock replenishment processes.

Recently Tom (@neuralmarket) and I had the chance to work together with Amanda Shiga (@AmandaShiga) from Nonlinear Digital to build web analytics process using RapidMiner. Amanda has an on-going pilot project to apply data mining techniques to clickstream and user behavior data collected from her client's website. The website has a number of value-weighted micro-conversions, such as newsletter signup, or downloading a whitepaper, or event registration. For online retailers, seeing the visitors convert to paying customers is the ultimate goal. The focus of web analytics nowadays has shifted from getting visitors to a website to turning the web visitors into high value customers.

It is a great overview of a plethora of topics around doing scalable data analytics and data science. It is extremely up-to date, going through techniques that have existed for many years now like MapReduce, but also newer systems like Spark, all in the context of the Hadoop eco-system. They go into machine learning techniques, data management, and overall paint a nice picture around what data science is, and why data products are important, while teaching you how to make them! Every single concept is explained in a clear and concise manner, and wherever details are omitted there is always a citation to a source where the reader can continue reading more about it, which I think is great. Although I wouldn't classify myself as a beginner, I believe it is friendly to both professionals and beginners, as it is centered around python which makes most examples (that are conveniently uploaded in a nice github repository) really easy to simply run and play around with.

Billions of connected devices, zetabytes of data, power and brand loyalty now in the hands of the consumer, businesses having to market and sell to each and every one of us. How can any business make sense of it all? How can they learn and avoid making the same mistakes – and become smarter. Oh – and did I mention much of this needs to happen in real time? That's where Machine Leaning as part of a cognitive strategy comes in to its own.