Grace Peters recently wrote in HPCwire, "Thanks to machine learning, the page you see when you log-on to Amazon.com is likely very different from the one I see. Advertising, product recommendations, and special deals are all tailored to our unique customer profiles based on historical browsing trends and buying behavior. Online retailers like Amazon were among the first users of customer data collection and analysis for improving services and personalizing the shopping experience, and they've become so skilled some sites might even be able to predict what we will purchase before we even know what we're looking for." Peters goes on, "Advancements in digital technologies have driven a paradigm shift in the way businesses interact with their customers, with touchpoints increasingly moving to digital mediums. Because of the limited opportunities to satisfy customers on a person-to-person level, machine learning is now in widespread use by a variety of modern enterprises as a way to enrich customer experiences, create more personalized and customer-centric interactions, and offer seamless omnichannel communications."

Let's check in with who's on the AI (artificial intelligence) train. Apple kicked things off this year, announcing back in January that it had purchased Emotient, an AI tech startup in the facial recognition business. Did Apple say why it bought that company? Of course, it did not; mind your own business. Jump ahead to spring, and the next big company in the retail space (specifically, the biggest company in eCommerce) to make an AI-related move this year was Amazon.

A total of 2,552 players on over 2,000 teams participated in the Home Depot Product Search Relevance competition which ran on Kaggle from January to April 2016. Kagglers were challenged to predict the relevance between pairs of real customer queries and products. In this interview, the first place team describes their winning approach and how computing query centroids helped their solution overcome misspelled and ambiguous search terms. Andreas: I have a PhD in Wireless Network Optimization using statistical and machine learning techniques. I worked for 3.5 years as Senior Data Scientist at AGT International applying machine learning in different types of problems (remote sensing, data fusion, anomaly detection) and I hold an IEEE Certificate of Appreciation for winning first place in a prestigious IEEE contest.

Major corporations use this kind of artificial intelligence to help with the complexities of serving a massive, often international audience -- and now Amazon is making its machine learning software open source. The company's Deep Scalable Sparse Tensor Network Engine -- otherwise known as DSSTNE and pronounced "destiny" -- is now available to anyone who's interested in tinkering with it. Amazon hopes that outside influences will help make the platform even more powerful than it already is, according to a report from Engadget. "DSSTNE is built for production deployment of real-world deep learning applications, emphasizing speed and scale over experimental flexibility," reads documentation that accompanies the files released by Amazon. Internally, DSSTNE is used to deliver purchase recommendations to consumers based on their order histories. Product recommendations are big business for Amazon, as having such a daunting catalog of merchandise is really rather worthless unless customers are able to discover items that are relevant to their interests.

Qubit is a start-up which helps online retailers collect, analyse and act on data about the way their customers behave. We're looking for a Data Scientist to join our Research team, to help us develop intelligent products around this data, and conduct cutting-edge research into consumer behaviour on the web. This is a great opportunity to conduct real R&D around human behaviour. Our data collection tools store more than 1 billion data points every day. Overall, Qubit technology tracks consumer journeys leading to billions of pounds of online spending worldwide every year, for some of the largest names in online retail.

Amazon's virtual assistant was already hard at work helping with tasks via its Echo speakers and Fire TV, but now Alexa is getting more control of your television. The online retailer announced today that Alexa can handle more requests on its streaming gadgets, including launching apps, playing selections from Amazon video and add-on subscriptions (HBO Go, Starz, Showtime, SeeSo) and browsing local movie times. Fire TV already offered voice search and Alexa has been available on those devices as well, but this update expands the virtual assistant's workload.

Machine-learning is all the rage in fraud detection, with industry analysts, academics, businesses and technology media examining the advantages of algorithms and big data in the fight against e-commerce fraud. Especially for fraud analysts working in companies with small budgets, machine-learning tools are seen as a cost-effective way to tighten fraud controls while maintaining fast decision times, as Forrester noted in its 2015 cross-channel fraud report. There's no question that machine-learning tools can be an effective component of fraud reduction program, but relying on them to save staffing costs may not be cost-effective in the long run. That's because while machine learning is an invaluable tool in the fight against fraud, it relies on human input and insight to create a comprehensive solution that yields the best results. Algorithms are useful for identifying potential fraud quickly, but due to variability in consumer behavior – such as making online purchases while traveling abroad -- some transactions will be falsely flagged for decline.

Chinese online retailer JD.com has teamed up with robot maker Siasun to automate its logistic network, the company said on Friday. JD said it will work with Siasun to develop logistics robots to increase automation from order to delivery. JD has already invested heavily in making its warehouses and deliveries more efficient. "Logistics in the future will go beyond basic infrastructure," said JD's technology chief Zhang Chen, "Technology ranging from the Internet of Things, cloud computing, big data to artificial intelligence are making our service smarter." JD said it will work with other companies on automation similar to that at Amazon's distribution centers.

Thanks to machine learning, the page you see when you log-on to Amazon.com is likely very different from the one I see. Advertising, product recommendations, and special deals are all tailored to our unique customer profiles based on historical browsing trends and buying behavior. Online retailers like Amazon were among the first users of customer data collection and analysis for improving services and personalizing the shopping experience, and they've become so skilled some sites might even be able to predict what we will purchase before we even know what we're looking for. Advancements in digital technologies have driven a paradigm shift in the way businesses interact with their customers, with touchpoints increasingly moving to digital mediums. Because of the limited opportunities to satisfy customers on a person-to-person level, machine learning is now in widespread use by a variety of modern enterprises as a way to enrich customer experiences, create more personalized and customer-centric interactions, and offer seamless omnichannel communications. Machine learning goes a step beyond Big Data analytics, where machines employ advanced algorithms to autonomously adapt and learn from previous experiences, and therefore emulate the thought process behind human decision-making.

JD.com founder and CEO Richard Liu attends a launching ceremony of the "U.S. Mall" of JD.com in New York, the United States, July 20, 2015. BEIJING, May 15 (Xinhua) -- Chinese online retailer JD.com has teamed up with robot maker Siasun to automate its logistic network, the company said on Friday. JD said it will work with Siasun to develop logistics robots to increase automation from order to delivery. JD has already invested heavily in making its warehouses and deliveries more efficient. "Logistics in the future will go beyond basic infrastructure," said JD's technology chief Zhang Chen, "Technology ranging from the Internet of Things, cloud computing, big data to artificial intelligence are making our service smarter."