When a company doesn't pay a supplier when it's supposed to, it can be next to impossible to collect that money. Accounts receivable processes were designed to keep the bill collecting organized, but today, with B2B payment terms expanding longer and longer, AR has risen, out of necessity, into a more strategic part of the enterprise. For companies large and small today, the first step in strategizing the AR department was to digitize and automate: offer electronic invoices, support electronic payments and enable automated process management. But Anthony Venus and Eugene Vyborov, CEO and CTO, respectively, of AR solutions provider YayPay, say that accounts receivable is ready to take the next step in technological advancement. "Tons of companies are heading over 10 days sales outstanding, which is a huge number, to be honest," Vyborov said.

Amazon.com is still figuring out how to use robots to fill store shelves, but it's about done with clerks. Next year, the company will open a convenience store in Seattle where shoppers can walk in, take what they want -- and leave. The Amazon Go store is on the corner of 7th Avenue and Blanchard Street in Seattle, in the heart of Amazon's new campus development and a few blocks from the company's headquarters. Amazon wants people to walk in to the store and then just walk out with what they want. To figure out who to charge, and how much, Amazon will identify shoppers by scanning QR codes on their phones as they walk in, and use sensors and computer vision technology to determine which items they take.

Consumers'Couch Shopped' On Black Friday: Here Are The Likely Retail Winners And Losers Amazon CEO Jeff Bezos walks onstage for the launch of the new Amazon Fire Phone, in Seattle. Amazon.com has been tiptoeing into the world of brick-and-mortar retailing for some time now. There have been small outposts at colleges and a few beautifully designed bookstores have been popping up in cities with rumors for several hundred more. As I have said before regarding these efforts, they appear to be sleight of hand experiments designed to distract the retail world from the game changing concepts that are to come. Amazon Go is one such idea, one that could drastically change not just food retailing, but every segment of retail. The premise is simple and the video introduction that Amazon released Monday will surely be among the most watched futuristic retail videos around.

Amazon's YouTube video introducing its Amazon Go convenience store shows that the company has broken conventional supermarket wisdom and that its foray into food is one traditional retailers should fear -- for three reasons. The first reason is that Amazon knows how to communicate. "Just walk out" says it all! It's simple, straightforward and easy to understand for shoppers, and clearly underscores the benefits: no lines; no crabby cashiers; no swiping your credit card; no bagging. The grocery industry has a tendency to name and describe complex technologies in a way that confuses – Amazon's language breaks through all that.

Amazon has unveiled a convince store in downtown Seattle that replaces cashiers and lines with technology found in self-driving cars. The app uses a range of sensors that detect what shoppers take off shelves and bills it to their Amazon account if they don't put it back Malaria caused'widespread death' in ancient Rome: DNA... Area 51 revealed: Aerial timelapse of America's mysterious... Watch out, Uber! BMW seeks to be'coolest' ride-hailing firm... Malaria caused'widespread death' in ancient Rome: DNA... Area 51 revealed: Aerial timelapse of America's mysterious... Watch out, Uber! BMW seeks to be'coolest' ride-hailing firm... The store, called Amazon Go', lets customers enter using an accompanied app, grab the items they need and are able to walk out without stopping at a register To enter the store, customers simply open the Amazon Go app and place it to a sensor located on what appears to be a turnstyle in the entrance way. This futuristic store uses the same types of technologies found in self-driving cars – computer vision, sensor fusion and deep learning. When you decide not to purchase and item you've grabbed, just put it back and the technology will automatically remove it from your virtual cart The 1,800-square-foot Amazon Go store is located at 2131 7th Ave, Seattle, WA, on the corner of 7th Avenue and Blanchard Street.

How does Amazon recommend products you might be interested in purchasing? OR How does Netflix decide which movies or TV shows you might want to watch? OR How does Facebook or LinkedIn decide who might you want to form a link with? OR How does Udemy decide what courses to market to you? OR How does New York Times decide which news you might be interested in reading? How does Amazon recommend products you might be interested in purchasing?

The acquisition will bring Zementis' predictive analytics to Software AG's real-time streaming analytics platform. Software AG has acquired California-based Zementis for an undisclosed sum in a move designed to bolster its internet of things capability. Zementis offers software for'deep learning' which plays a crucial role in the development of machine learning, data science and fundamental technology that drives artificial intelligence (AI) development. According to Software AG, the advances in machine learning and AI are being applied in the next generation Internet of Things (IoT) such as self-driving cars, personal digital assistants, medical diagnosis, predictive maintenance and robotics. Software AG has already employed Adaptive Decision and Predictive Analytics (ADAPA) from Zementis into its Digital Business Platform to offer its clients with comprehensive insights for real time business analytics.

Predictive analytics and machine learning working separately or together can be just what a company needs to succeed. But understanding how they work is key to figuring out how they can help businesses thrive. So, what is predictive analytics? Datafloq's Mark van Rijmenam uses the car metaphor, according to which traditional, descriptive analytics is like looking at the rear-view mirror to see what has happened, while predictive analytics is using a navigation system to tell you what will happen, and prescriptive analytics is a self-driving car that knows how to take you to your destination. This metaphor, while easy to comprehend, may also be deceptively simple. It certainly is open to interpretation, so it's a good starting point for discussion.

This project involved implementing machine learning methodologies to identify similarities in job skills contained in resumes. An organization presented the project to the New York City Data Science Academy to explore whether Academy students might be interested in working on it. The three authors of this post, all students at the Academy at the time, agreed to take the project on. In formulating the analysis described in this post, the authors collaborated with several representatives of the organization. While the organization has asked us to refrain from disclosing its name at this time, the authors wish to convey their gratitude to the organization for the opportunity to work on the project as part of our studies at the Academy. The general idea underlying this project was to uncover semantic similarity and relations behind skills that appear on resumes. A semantic-based approach to evaluating job skill similarity has many potential applications that flow from an understanding of the relationships between skills found in resumes. While there are certainly other approaches to identifying semantic connections between job skills, machine learning techniques create interesting and powerful possibilities.

In today's world of big data and the internet of things, it is common for a business to find itself sitting atop a mountain of data. Possessing it is one thing, but leveraging it for data driven decision making is a much different ball game. Gut-feelings and institutionalized heuristics have traditionally been used to guide development of protocol and decision making, but the world of artificial intelligence and big disparate data is changing that. Everyone is trying to make sense of, and extract value from, their data. Those that are not will be left behind.