It seems Amazon may be putting profits before customers. The Wall Street Journal has reported that the retail giant tweaked its product-search algorithms in order to favor its own'private label' and higher profit margin products– instead of what is most relevant for consumers. Programmers involved with the search algorithm are said to have opposed the change, as Amazon's principles stress they'work to earn and keep customer trust'. The changes were cited by sources familiar with the situation, who claimed Amazon's product-search system was changed last year. The Wall Street Journal has reported that the retail giant tweaked its product-search algorithms in order to favor its own'private label' and higher profit margin products– instead of what is most relevant for consumers Prior to the switch, algorithms would first show products that were bestsellers or relevant to what customers were looking to purchase.

The future of retail continues looking grim, as more brick and mortar stores close their doors. US retailers have announced 8,558 store closures so far this year, with total US store closures predicted to hit 12,000 by the end of 2019, reported Coresight Research on Friday. While the internet and automation are typically to blame for these closures, the same technology could actually be the solution for physical store locations, said Paul Winsor, general manager of retail at DataRobot. "If retailers want to stay open in the existing stores that they are operating in, my recommendation to them is to ask: Are they understanding the changing habits of those customers, and how they're shopping with them, in those locations?" "To survive in the tough, tough retail market, you have to start to turn your business, and make predictions, based on learning from your historical data," he added.

When Richa Kar and Kapil Karekar started Zivame in 2011 as an online lingerie marketplace, the goal was simple – to give women a platform to uninhibitedly and effortlessly shop for intimate wear. Over the years, it has emerged as a women-centric brand adding other product verticals, including active... You must have an active subscription to view the content and the comments on this page. Please click the "Subscribe" button or the "Login" button if you already have an account. Over the past few months, a Walmart neighborhood market in New York's Levittown area has been quietly transforming.

Robots have transcended the realm of sci-fi fantasy and are now making revolutionary waves in several industries. Industries that deal with complex, life-threatening tasks have been enjoying tremendous benefits from robots. From being controlled by a human operator to now being fully autonomous, robotics has vastly grown. Equipped with cutting-edge technologies, some robots today are designed such that they closely emulate human intelligence, in one form or another. Due to this, we see the use cases of robots in areas that need human intelligence and decision-making capabilities.

Retail Industry is looking at Artificial intelligence (AI) and machine learning (ML) as a solution to take their organization to the next level of productivity and customer experience. To explain this better, let us look at it this way – Retail companies have access to a massive amount of data about their customers and their shopping preferences. It is difficult for the companies to drill down into these huge mines of useful data and analyze it properly and derive actionable insights in real time. Therefore, massive amounts of this useful information could go waste which would otherwise have helped in increasing sales conversion rates or enhancing the customer satisfaction. With the help of AI and ML, the huge amount of big data could be used in creating web shops that take customer information and turn it into targeted shopping experiences or online chatbots that can easily answer questions and assist customers, or in-store intelligence to make the customer experience even more interactive.

Reviewed by Douglas Farenick, University of Regina Undergraduate mathematics textbooks are not what they used to be, and Gilbert Strang's superb new edition of Introduction to Linear Algebra is an example of everything that a modern textbook could possibly be, and more. First, let us consider the book itself. As with his classic Linear Algebra and its Applications (Academic Press) from forty years ago, Strang's new edition of Introduction to Linear Algebra keeps one eye on the theory, the other on applications, and has thestated goal of "opening linear algebra to the world" (Preface, page x).Aimed at the serious undergraduate student - though not just thoseundergraduates who fill the lecture halls of MIT, Strang's homeinstitution - the writing is engaging and personal, and the presentation is exceptionally clear and informative (even seasoned instructors maybenefit from Strang's insights). The first six chapters offer atraditional first course that covers vector algebra and geometry,systems of linear equations, vector spaces and subspaces, orthogonality, determinants, and eigenvalues and eigenvectors. The next three chapters are devoted to the singular value decomposition, lineartransformations, and complex numbers and complex matrices, followed bychapters that address a wide range of contemporary applications andcomputational issues. The book concludes with a brief but cogenttreatment of linear statistical analysis. I would like to stress that there is arichness to the material that goes beyond most texts at this level.Included are guides to websites and to OpenCourseWare, which I shallcomment upon later in this review.

Linear algebra is an important subject not only to mathematicians but also to an ever widening range of disciplines.

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One of the hot trends in artificial intelligence (AI) revolves around the use of deep learning (DL) technologies for image and video classification. These AI-driven applications use computer vision to classify or categorize an image or video file on the basis on its visual content. So, what is deep learning? In a few words, DL is a subset of machine learning (ML), and one of the key building blocks for AI solutions. It uses artificial neural networks as the underlying architecture for training algorithms, or models.

Global Artificial Intelligence in Retail Market was valued US$993.6 Mn in 2017 and is expected to reach US$8314 Mn by 2026, at a CAGR of 30.41% …