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.

The field of eCommerce continues to be a hotbed for emerging technologies. On one level, online stores are competing with other online stores. But on another level, online stores have to compete with the conventional marketplace of physical stores. One of the key differentiators is artificial intelligence which is making significant inroads into addressing the inherent flaws of eCommerce. Earlier prototypes of artificial intelligence were known to be brittle and prone to wide margins of error.

Technology has transformed how customers and brands interact with each other. Shoppers once relied on face-to-face, in-store interactions to make purchases and receive support. Now, shoppers do their research before entering a store (81 percent of shoppers conduct online research before buying) and seldom rely on salespeople to help them make decisions. Retailers, for their part, have realized that by embracing technology, they can extend their storefronts to their customers' fingertips. The Internet, buy buttons, mobile payment apps such as Square and Venmo, and couponing and price-matching apps like SnipSnap have changed how we shop.

Reflektion*–which delivers innovative "individualized commerce" technology for retailers–recently announced it has raised 18 million in Series B financing led by Battery Ventures. Here, Powered by Battery chats with CEO Sean Moran about the how the company is aiming to transform digital retail, partly by leveraging new types of artificial-intelligence technologies. Powered by Battery: We hear a lot about "personalization" and predictive analytics when it comes to online retail. How does Reflektion and "individualized commerce" fit in? What exactly do you do?