Messaging app Kik has launched an online store for chatbots, to allow brands to talk to users via bots. Chatbots - computer programs designed to simulate conversation with humans - are increasingly being seen as a key way to engage customers. Kik, which has 275 million users, has signed up 16 partners including make-up store Sephora and The Weather Channel. Microsoft opened a chatbot store last week and Facebook is expected to follow suit. In Kik's store, users will be able to download bots for entertainment, customer service and shopping.

Were you able to attend this year's South by Southwest (SXSW)? As always, the annual convergence of anything relevant and compelling has ignited some amazing conversation. THE FUTURE OF BIG DATA AND AI, by far one of our favorite panels, brought to light persuasive commentary on the applications of AI and how data has renewed these functions. Being the retail aficionados that we are, we couldn't help but tie those topics right back to retail and eCommerce. Amongst the four panelist, Dr. Doug Lenant, CEO of Cycorp, affirmed that deep learning technology has been around for about 30 years.

Amazon.com Inc. acquired artificial-intelligence startup Orbeus Inc., according to a person familiar with the matter, part of a broader push by the world's largest Internet retailer into smart software for its cloud-computing and connected-device businesses. The acquisition took place in the fall of 2015, said the person who asked not to be identified because Amazon hasn't announced the deal. An Amazon spokeswoman and representatives at Sunnyvale, California-based Orbeus, including Chief Executive Officer Yi Li, did not respond to requests for comment. An online search shows that the startup's domain name, Orbe.us, is owned by registrant Amazon Hostmaster, part of an Amazon subsidiary called Amazon Technologies Inc. Orbeus developed photo-recognition technology based on a powerful type of AI called neural networks and made this available as a consumer application, as well as a service for other companies and developers called ReKognition. It automatically categorized and identified the contents of photos. Orbeus's app, PhotoTime, came out before Google launched its successful AI-based Photos app.

Perhaps you heard recently about a new algorithm that can drive a car? Or scan a picture and find your face in a crowd? It seems as though every week companies are finding new uses for algorithms that adapt as they encounter new data. Last year Wired quoted an ex-Google employee as saying that "Everything in the company is really driven by machine learning." Machine learning has tremendous potential to transform companies, but in practice it's mostly far more mundane than robot drivers and chefs.

During the dot-com boom, online clothing sales were predicted to grow to 40% -50% of total sales. Although online sales of some other kinds of merchandise, such as books, have reached 50% of the market in the past 15 years, the percentage of online clothing sales hovers around 20%. The difficulty in finding the correct size and fit is one of the primary reasons that consumers are reluctant to buy clothes online. And their concern is not groundless; sizing varies among clothing manufacturers, and it is difficult to ascertain fit from online images. Consequently, 30%-40% of online clothing purchases are returned.

When Amazon executive Dave Limp first saw the pitch for the product that would become Echo in 2011, his main reaction was doubt. "This is going to be hard," Limp recalls thinking. But it would require a lot of inventions." The reaction was understandable given the lofty goals outlined in the Echo's original plan: It envisioned an intelligent, voice-controlled household appliance that could play music, read the news aloud and order groceries -- all by simply letting users talk to it from anywhere in the house. Since that time, the Echo has emerged as Amazon's sleeper hit, a hot-selling gadget that's being hailed as the standard-bearer for an entirely new computing paradigm in which Amazon suddenly has an edge on rivals such as Apple and Google. But the Echo's path into consumers' homes was hardly a sure thing. The gadget was stuck in Amazon's in-house labs for years, subject to the perfectionist demands of Amazon CEO Jeff Bezos and lengthy internal debates about its market appeal.

In the recent Walmart Kaggle competition I used a Random Forest classifier to solve a market basket problem. A market basket model is built on the idea there exists relationships between items purchased together. For example, a person purchasing a new toothbrush is more likely to also purchase toothpaste than motor oil in the same shopping. Retailers use these market basket relationships in the design of their stores for ease of use and also to increase sales. In this specific problem Walmart has broken up their shopping trips into 38 unique'TripType'.

Many retailers today employ inventory management systems based on Re-Order Point Policies, most of which rely on the assumption that all decreases in product inventory levels result from product sales. Unfortunately, it usually happens that small but random quantities of the product get lost, stolen or broken without record as time passes, e.g., as a consequence of shoplifting. This is usual for retailers handling large varieties of inexpensive products, e.g., grocery stores. In turn, over time these discrepancies lead to stock freezing problems (see Ref. [1]), i.e., situations where the system believes the stock is above the reorder point but the actual stock is at zero, and so no replenishments or sales occur. Motivated by these issues, we model the interaction between sales, losses, replenishments and inventory levels as a Dynamic Bayesian Network (DBN), where the inventory levels are unobserved (i.e., hidden) variables we wish to estimate. We present an Expectation-Maximization (EM) algorithm to estimate the parameters of the sale and loss distributions, which relies on solving a one-dimensional dynamic program for the E-step and on solving two separate one-dimensional nonlinear programs for the M-step.

The North Face wants its customers to get the perfect jacket for whatever they're doing - skiing in Vermont, ice skating in New York City or just trying to stay warm on the way home from work. So how do they give online customers that extra assistance and know-how? "The issue is that online shopping over the past two decades has been about a grid of products on a white background," said Cal Bouchard, senior director of e-commerce at The North Face. "That's how customers find their products. We've developed onsite search and navigation, but we still made the consumer do the work. We wanted to take the conversation you might have with an associate in a store and see if we could put that as a service online: 'Here's what I need. "We want to make it much more personal and much more intuitive," she added. "Consumers are going to get smarter and smarter and say, 'Really?

When Amazon executive Dave Limp first saw the pitch for the product that would become Echo in 2011, his main reaction was doubt. "This is going to be hard," Limp recalls thinking. But it would require a lot of inventions." The reaction was understandable given the lofty goals outlined in the Echo's original plan: it envisioned an intelligent, voice-controlled household appliance that could play music, read the news aloud and order groceries -- all by simply letting users talk to it from anywhere in the house. Since that time, the Echo has emerged as Amazon's sleeper hit, a hot-selling gadget that's being hailed as the standard-bearer for an entirely new computing paradigm in which Amazon suddenly has an edge on rivals such as Apple and Google. But the Echo's path into consumers' homes was hardly a sure thing. The gadget was stuck in Amazon's in-house labs for years, subject to the perfectionist demands of Amazon CEO Jeff Bezos and lengthy internal debates about its market appeal.