Tomorrow's retail stores want to take a page from their online rivals by embracing advanced technology -- everything from helpful robots to interactive mirrors to shelves embedded with sensors. The goal: Use these real-world store features to lure shoppers back from the internet, and maybe even nudge them to spend more in the process. Amazon's new experimental grocery store in Seattle, opening in early 2017, will let shoppers buy goods without needing to stop at a checkout line. This photo provided by SoftBank Robotics America demonstrates a shopping experience with SoftBank Robotics' humanoid robot called Pepper, waving at right. The robot can greet shoppers and has the potential to send messages geared to people¿s age and gender through facial recognition.

With machine-learning technology, retailers can address the common--and costly--problem of having too much or too little fresh food in stock. Fresh food, already a fiercely competitive arena in grocery retail, is becoming an even more crowded battleground. Discounters, convenience-store chains, and online players are recognizing the power of fresh-food categories to drive store visits, basket size, and customer loyalty. With fresh products accounting for up to 40 percent of grocers' revenue and one-third of cost of goods sold, getting fresh-food retailing right is more important than ever.1 1.Raphael Buck and Arnaud Minvielle, "A fresh take on food retailing," Perspectives on retail and consumer goods, Winter 2013/14. Fresh food is perishable, demand is highly variable, and lead times are often uncertain.

The online retail experience is the first area--but hardly the last--to be addressed by the Sentient Aware family of artificial intelligence products. But if you're an online retailer, you'll be glad we started here. Because with Sentient Aware for e-Commerce, you can now add an AI sales associate to transform your e-commerce website, helping your customers find exactly what they're looking for in real-time. Why start with the online retail experience? Because at Sentient, we appreciate how much time and effort you pour into optimizing your online store and curating your product assortment.

The "willing suspension of disbelief" is the idea that we (the audience, readers, viewers, content consumers) are willing to suspend judgment about the implausibility of the narrative for the quality of our own enjoyment. We do it all the time. Two-dimensional video on our screens is smaller than life and flat and not in real time, but we ignore those facts and immerse ourselves in the stories as if they were real. We have also learned the "conventions" of each medium. While we watch a movie or a video, we don't yell to the characters on the screen "Duck!" or "Look out!" when something is about to happen to them.

Thanks to the wonders of AI, it's possible to ask a robot to help you figure out the best way to deal with a difficult situation. One of the greatest hits as far as Christmas gift-giving was a gizmo called an Echo Dot I gave my nephew, Nathan. It was a hit despite the obligatory annoying learning curve that comes when any Southerner tries to talk to an artificial intelligence device ("Alexer, G-darnit, stop giving me the temperature in Celsius, I want it in American!") I had arrived in Chapel Hill weary from a verbal battle with Siri who, based on the convoluted traffic pattern she recommended, is a huge Duke fan. Artificial intelligence is a big buzzword for 2017.

Next time you stop for gas at a self-serve pump, say hello to the robot in front of you. Its life story can tell you a lot about the robot economy roaring toward us like an EF5 tornado on the prairie. Yeah, your automated gas pump killed a lot of jobs over the years, but its biography might give you hope that the coming wave of automation driven by artificial intelligence (AI) will turn out better for almost all of us than a lot of people seem to think. The first crude version of an automated gas-delivering robot appeared in 1964 at a station in Westminster, Colorado. Short Stop convenience store owner John Roscoe bought an electric box that let a clerk inside activate any of the pumps outside. Self-serve pumps didn't catch on until the 1970s, when pump-makers added automation that let customers pay at the pump, and over the next 30 years, stations across the nation installed these task-specific robots and fired attendants. By the 2000s, the gas attendant job had all but disappeared.

Amazon's 2016 has been record breaking on many fronts. The company recorded its sixth consecutive quarterly profit (previously, it mostly hemorrhaged cash). Meanwhile, this year marked Amazon's growing strength in hardware with its hit Echo home automation hub Amazon Echo, and its companion voice assistant Alexa. The company has also become force in entertainment, debuting a line of hit original shows through its Amazon Video Prime service. It's hard to imagine how Amazon could top 2016, but here are some likely moves by the Seattle-based Goliath in 2017: To save money over the past year, Amazon has been seeking to take over more shipping duties from the likes of UPS and FedEx by leasing trucks, planes, and ships.

This paper deals with price optimization, which is to find the best pricing strategy that maximizes revenue or profit, on the basis of demand forecasting models. Though recent advances in regression technologies have made it possible to reveal price-demand relationship of a number of multiple products, most existing price optimization methods, such as mixed integer programming formulation, cannot handle tens or hundreds of products because of their high computational costs. To cope with this problem, this paper proposes a novel approach based on network flow algorithms. We reveal a connection between supermodularity of the revenue and cross elasticity of demand. On the basis of this connection, we propose an efficient algorithm that employs network flow algorithms. The proposed algorithm can handle hundreds or thousands of products, and returns an exact optimal solution under an assumption regarding cross elasticity of demand. Even in case in which the assumption does not hold, the proposed algorithm can efficiently find approximate solutions as good as can other state-of-the-art methods, as empirical results show.

We consider the assortment optimization problem when customer preferences follow a mixture of Mallows distributions. The assortment optimization problem focuses on determining the revenue/profit maximizing subset of products from a large universe of products; it is an important decision that is commonly faced by retailers in determining what to offer their customers. There are two key challenges: (a) the Mallows distribution lacks a closed-form expression (and requires summing an exponential number of terms) to compute the choice probability and, hence, the expected revenue/profit per customer; and (b) finding the best subset may require an exhaustive search. Our key contributions are an efficiently computable closed-form expression for the choice probability under the Mallows model and a compact mixed integer linear program (MIP) formulation for the assortment problem.

We propose Sketched Online Newton (SON), an online second order learning algorithm that enjoys substantially improved regret guarantees for ill-conditioned data. SON is an enhanced version of the Online Newton Step, which, via sketching techniques enjoys a running time linear in the dimension and sketch size. We further develop sparse forms of the sketching methods (such as Oja's rule), making the computation linear in the sparsity of features. Together, the algorithm eliminates all computational obstacles in previous second order online learning approaches.