Several new innovations that change the way retailers manage inventory and consumers purchase products were on display at the National Retail Federation's annual trade show, The New York Times reported. The convention floor included displays of alert systems programmed to identify heavy-spending customers, smart shelves that can track inventory in real time and robots for supply chain applications. During the three-day event, retail industry leaders discussed artificial intelligence, Big Data and automation. Drawing more than 600 exhibitors, the convention featured sessions with leaders from Walmart, Best Buy, Neiman Marcus and other big-name merchants. According to technology on display, certain consumers will soon be able to test-drive or purchase vehicles without any human contact, using their mobile phones at a garage that doubles as a vending machine.

Walking up and down aisles of a retail store, scanning row after row of products to help keep track of inventory is one of the most tedious and time-consuming tasks store employees endure, and most dread the assignment. This is especially true for a company like Wal-Mart Stores (NYSE:WMT), which can have as many as 200,000 products lining its shelves. The cost of not completing this monotonous chore can be high. Every out-of-stock item can result in a lost sale for the retailer. This is particularly critical as brick-and-mortar stores contend with the rise of online shopping from the likes of Amazon.com

Many retailers like Macy's, Tommy Hilfiger and Home Depot have evidently proven that Artificial Intelligence can be used for Visual recognition as it provides them with visual product searches.

In the 2006 film, The Devil Wears Prada, actor Meryl Streep who plays Miranda Priestly, a powerful fashion editor, gives her new assistant a dressing down for not understanding fashion. She tells her that fashion is whatever a select group of designers say it is. But what she fails to anticipate is how these czars of style will one day be challenged on their own turf by another set of fashionistas: machines. As artificial intelligence (AI) pervades almost every field today, lines of an algorithm are now sashaying down the catwalk. India's fashion and retail industry too have started to rely on the power of machines to come up with the latest styles.

Artificial intelligence isn't coming to terminate traditional retailers -- just like in "Terminator 2: Judgment Day," it's actually here to help. That was a key takeaway from a panel on robotics and AI held at NRF 2018: Retail's Big Show in New York City on Jan. 14. Instead of seeing AI as a threat that eliminates jobs through automation, retailers should embrace the technology as a tool that can solve their business problems, retail AI experts said. Michelle Bacharach, co-founder and CEO of FindMine, a company that uses supervised machine learning techniques to create complete outfits around each product an apparel retailer sells, said on the panel that AI should not be an end in itself. "If you start there, you've already failed," she advised retailers.

Despite the record amount of brick-and-mortar stores announcing closures in 2017, the National Retail Federation claims the industry isn't dead at all and in fact it's on the verge of a turnaround with more jobs and innovation expected by 2020. "I think there will be more jobs that will be opened than there are now," Cristina Cersoli, NRF senior vice president for retail strategy told FOX Business on the future of industry. "We're already seeing more jobs in advanced analytics and artificial intelligence." On Sunday, the National Retail Federation revealed its new Innovation Lab to retailers that showcased what shopping will look like in 2020. The new retail experience features new advancements in augmented reality, artificial intelligence, machine learning, facial recognition, big data and robotics.

Agent Smith from "The Matrix," Skynet from the "Terminator" franchise, Ash from "Alien," the ship from "WALL-E" -- when humanity envisions artificial intelligence (AI) going bad, we envision it going so apocalyptically bad that our tools rise up and enslave us. That tension can be felt in the world of robotics and AI development today. Elon Musk rather famously told Bloomberg's Ashlee Vance about his concerns that Alphabet could have perfectly good intentions but could still "produce something evil by accident."

New technology is ready to make the internet as seamless as possible and transform the way we work, live and play. The next phase of digital disruption is'invisible technology', making the way that consumers get information and buy products as integrated into our daily lives as possible. Voice assistants like Alexa, Siri and Cortana can help consumers function in hands-free and even eyes-free situations. A good example of this is in an application like cooking. As these devices become part of everyone's kitchen, they have the potential to be an'extra hand', telling consumers recipes, reminding consumers when to stir the dish and even suggesting a wine pairing for the meal.

For all 10 trends, watch the full video here. 'Technofoodology,' and artificial intelligence in particular, are some of the best things to ever happen to a grocery store. Our smartphones and our home-based assistants are ushering in a new way to buy our foods. Sure, we can replenish our foods by asking Alexa to order from Amazon, and just last week one of my favorite c-stores, Sheetz, announced that their made-to-order foods from all their 564 stores can be ordered on Alexa. By 2020, there will be 55 million smart devices in our homes--making that the biggest supermarket chain on the planet.

If you work in analytics or data science, like we do, you are familiar with the fact that data is being generated all the time at ever faster rates. Analysts are often trained to handle tabular or rectangular data that is mostly numeric, but much of the data proliferating today is unstructured and text-heavy. Many of us who work in analytical fields are not trained in even simple interpretation of natural language. We developed the tidytext (Silge and Robinson 2016) R package because we were familiar with many methods for data wrangling and visualization, but couldn't easily apply these same methods to text. We found that using tidy data principles can make many text mining tasks easier, more effective, and consistent with tools already in wide use.