If you think that it's hard to remember where all your favorite products are in the local grocery store, well … it's not just you. In the case of large supermarkets carrying thousands of goods, even employees can have trouble remembering where everything is. That's why Toronto's 4D Retail Technology Corp. developed the stock-taking, store-mapping 4D Space Genius robot. In less than an hour, the self-guiding Segway-based Space Genius can reportedly move along every aisle of an average-sized (43,000 sq ft/3,995 sq m) supermarket or other large store, scanning all of the products and barcodes on display in HD and 3D as it does so. First and foremost, this allows it to create an interactive 3D map of the store, in which the location of every item is indicated.

Over the last several weeks, we've reached peak AI/Bot mania. Most of the conversation has centered around Chatbots and the potential emergence of a new platform/distribution layer. If you've been mostly ignoring the press, some good reads are: Tl;dr – the major takeaways are as follows: given that consumers don't really download apps anymore, brands & retailers have a new access point to end consumers, sitting on top of existing messaging platforms and leveraging chatbots to ensure mass scale. The truth is that the chatbot platform conversation is really just an extension of the one we had about a year ago during the emergence of Magic/Operator and SMS as the new platform, which we discussed in Are We Already Rebundling Mobile. An important extension given that such bots have been democratized and can now be spun up not just by tech companies, but by traditional retailers (on their own or within Messenger) or even by individuals such as you and me.

The use of robots on the retail sales floor is the same sort of double-edged sword as when used for the warehouse. Some see them as capable of handling arduous processes to allow employees to focus on customer service. Others see robots as pure replacements, threatening to cut out the need for real live human staff. The latest robot to make an appearance at the front of the store is, if it catches on, likely to generate the same sort of controversy. A company called 4D Retail Technology Corp. has created a robot capable of automating the inventory process by rolling through the aisles and imaging every product and every barcode in a store.

A data scientist is one of the most in-demand, high-profile careers in IT today, but Tom Walsh and Alex Krowitz have been working behind the scenes in the field for years. Walsh, a research engineer and Krowitz, a senior research engineer at cloud workforce management solutions company Kronos, sift through the influx of proprietary and customer data to identify patterns and gain insights based on that data. There are generally two kinds of projects we regularly handle; mining patterns within data to improve our own products is one and the other is taking on specific sets of customer data to gather and deliver insights from that," says Walsh. What companies are looking for is ultimately the capability to make predictions based on that data, says Krowitz. Companies use those predictions to help drive everything from marketing strategy to resource allocation, personnel levels and staffing, or to predict retail sales, he says. "We have products that use machine learning algorithms to help customers with these predictions.

We introduce sparse random projection, an important dimension-reduction tool from machine learning, for the estimation of discrete-choice models with high-dimensional choice sets. Initially, high-dimensional data are compressed into a lower-dimensional Euclidean space using random projections. Subsequently, estimation proceeds using cyclic monotonicity moment inequalities implied by the multinomial choice model; the estimation procedure is semi-parametric and does not require explicit distributional assumptions to be made regarding the random utility errors. The random projection procedure is justified via the Johnson-Lindenstrauss Lemma -- the pairwise distances between data points are preserved during data compression, which we exploit to show convergence of our estimator. The estimator works well in simulations and in an application to a supermarket scanner dataset.

Humans have always had a complicated relationship with new "technologies." From awe to fear, centuries ago, Plato even worried that writing would adversely affect people's memories. Modernity has had a particular curiosity regarding artificial intelligence (AI). From Terminator-style killer robots to emotive humanoids, the mention of AI brings to mind the many silver screen renderings of some future civilization. More likely than any of these, however, is the reality that AI will probably turn out to be another commonplace technology that, while novel at first, will end up integrated into our everyday lives.

ReplyYes has seen success with its automated messaging system. Chatbots already have a little bit of a bad name. Early reviews for the ones on Facebook Messenger have been rough due to apparent malfunctions, and Microsoft's Tay has been an utter disaster, at least on a couple of occasions. But a startup called ReplyYes, which offers a text-to-buy system for retailers, provides a glimpse into the potential of automated messaging. Interestingly, the company has a pair of e-commerce ventures: One sells vinyl records, the other graphic novels.

Telling Jeff Bezos he's wrong is always a frightening proposition. In the fall of 2014, though, a small group of the men and women building Amazon's new voice-controlled speaker felt they needed to confront the CEO. The release of the speaker was looming, and for the most part, things were falling into place. The device looked good, its voice recognition software was improving quickly, and even the boxes it would ship in had been designed and assembled. But there was a lingering issue with the name printed on those boxes: the Amazon Flash. Many people who worked at Lab126, Amazon's hardware division, hated the name, according to two former employees. Bezos, on the other hand, was strongly in favor.

Nowadays, brick-and-mortar stores are finding it extremely difficult to retain their customers due to the ever increasing competition from the online stores. One of the key reasons for this is the lack of personalized shopping experience offered by the brick-and-mortar stores. This work considers the problem of persona based shopping recommendation for such stores to maximize the value for money of the shoppers. For this problem, it proposes a non-polynomial time-complexity optimal dynamic program and a polynomial time-complexity non-optimal heuristic, for making top-k recommendations by taking into account shopper persona and her time and budget constraints. In our empirical evaluations with a mix of real-world data and simulated data, the performance of the heuristic in terms of the persona based recommendations (quantified by similarity scores and items recommended) closely matched (differed by only 8% each with) that of the dynamic program and at the same time heuristic ran at least twice faster compared to the dynamic program.

Learn how to apply test-driven development (TDD) to machine-learning algorithms--and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they can't account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you're familiar with Ruby 2.1, you're ready to start.