The rise of cashless and cardless retail transactions suggests consumers are growing comfortable with paying for products from accounts linked to portable devices. How quickly these commerce modalities go mainstream is an open question, but Mastercard is leaving nothing to chance. The global payment network is not only preparing for digital commerce ruled by recommendation systems fueled by machine learning systems but also war-gaming for the arrival of 5G, as well as the proliferation of IoT and AI, says Jorn Lambert, Mastercard's executive vice president of digital solutions. Get the latest insights with our CIO Daily newsletter. This short- and long-term view sketches out a vision of payments revolutionized by machine-to-machine commerce and hyperpersonalized transactions, a reality Lambert expects will become mainstream in the next three to five years.

Despite applying multiple filters and typing in carefully crafted keywords, you still can't find what you're looking for online. Overwhelmed by choice, you scroll the page for the fourth time trying to choose one of the 15 shades of white paint. Do you want Paper White or Chalk White? Maybe you want beige instead. You start to wish you never undertook this do-it-yourself (DIY) project; surely, shopping should not be this hard. Ensuring a smooth and stress-free experience is critical in retaining and attracting new customers.

Here are five things in technology that happened this past week and how they affect your business. Picnic--a Seattle startup, --announced this past week that it has created an assembly platform that can make 300, 12-inch pizzas per hour (and 180 18-inch pizzas per hour), making this robot a first-of-its-kind. When a pizza is ordered, the order goes into a digital queue, prompting the robot to begin making the pie the moment that the dough is placed in the appropriate spot in the machine. Data is then sent back to Picnic through the internet in order for developers to help the robot to improve upon any errors made. The A.I. driven pizza platform is currently being used at 3 different establishments in Seattle.

Most CPG companies (CPGs) have struggled to find solid footing in a turbulent industry. Bain research has found that 34 of the world's top 50 consumer goods companies experienced a decline in revenues, profits or both in recent years,1 forcing CPG executives to find new ways to compete. Emerging technologies, including AI, have given a sharp advantage to firms in other sectors. Companies at the forefront of AI are household names, known for changing the playing field and reinventing their industries: Amazon, Facebook, Microsoft, Apple. To stay ahead, these leaders are investing heavily in technology. Amazon, for example, ranks No. 1 in R&D, spending more than 10% of its revenues on IT while retailers manage 1% to 2%.

Though most are loath to admit it, retailers already make extensive, behind-the-scenes use of biometrics to track employees, nab shoplifters and improve store security. An October 2018 New York Magazine article titled, "Smile! Peter Trepp, CEO of facial recognition software company FaceFirst, told McClatchy in May 2018 that his company can "match a face against the database of 25 million people in just under a second," and the firm's website counts hundreds of big-box stores, superstores, department stores, grocery stores, pharmacies and Fortune 500s among its clients. A similar article, published by BuzzFeed News in August 2018, titled, "Thousands of Stores Will Soon Use Facial Recognition, and They Won't Need Your Consent," similarly revealed the technology's pervasiveness and delved deeper into its privacy implications. In our latest report, "Biometric Marketing 2019," we found that despite these concerns, retailers are exploring biometric technology, including behavioral tracking and facial and voice recognition, for advertising and promotional targeting.

"This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue. A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time going through a formal degree program."--Deepak Agarwal, VP of Artificial Intelligence at LinkedIn "This book is a great introduction to machine learning from a world-class practitioner and LinkedIn superstar Andriy Burkov. He managed to find a good balance between the math of the algorithms, intuitive visualizations, and easy-to-read explanations. This book will benefit the newcomers to the field as a thorough introduction to the fundamentals of machine learning, while the experienced professionals will definitely enjoy the practical recommendations from Andriy's rich experience in the field."--Karolis

What the Amazon founder and CEO wants for his empire and himself, and what that means for the rest of us. Where in the pantheon of American commercial titans does Jeffrey Bezos belong? Andrew Carnegie's hearths forged the steel that became the skeleton of the railroad and the city. John D. Rockefeller refined 90 percent of American oil, which supplied the pre-electric nation with light. Bill Gates created a program that was considered a prerequisite for turning on a computer. At 55, Bezos has never dominated a major market as thoroughly as any of these forebears, and while he is presently the richest man on the planet, he has less wealth than Gates did at his zenith. Yet Rockefeller largely contented himself with oil wells, pump stations, and railcars; Gates's fortune depended on an operating system. The scope of the empire the founder and CEO of Amazon has built is wider. Indeed, it is without precedent in the long history of American capitalism. More product searches are conducted ...

Robotic Process Automation (RPA) has been around long enough now that industry-specific flavours have become mature, scalable models. Unlike in the earlier years of RPA, when implementations could fail because of poor or limited compatibility between different systems, RPA technology solutions these days are capable not only of working seamlessly with every system that's already in place but also of adapting to changes in environments caused by operational, legal, regulatory and other factors. In fact, it is expected that over 40% of the world's biggest corporations will implement RPA solutions by 2021. In complex industries that deal with as many processes as there are'moving parts', such as retail, banking, insurance, healthcare and manufacturing, RPA is proving to be a major game-changer with its impact on key parameters. Automation drives up efficiency and productivity; it also results in lower operational expenditure can that offset the higher initial costs within as low as 2 years.

What does the future hold for us? Or more precisely, how will the future unfold? One thing that we know unanimously is that the digital revolution is here. This revolution is cascading across every industry and organization, resulting in wide-scale enterprise disruption with redefined customer expectations. The ability to rapidly rotate to the new has been critical for companies striving to become digital leaders, as well as for the employees who are shifting beyond the digital culture shock.

A practitioner s tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field recommendations are provided for further study in order to build advanced skills in tackling important data problem domains.The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work.