Combining computer vision, IoT, and AI creates a system that delivers more value than those three technologies in isolation, as the retail industry is highlighting.
While enterprise organizations have traditionally tended to stay away from emerging tech, preferring to wait until the major bugs have been worked out before making the leap, the arrival of Agile and DevOps has changed the timelines. Enterprises are moving more quickly these days as digital natives threaten to disrupt markets and industries.
With that in mind, InformationWeek spotted some emerging technology applications at the National Retail Federation Big Show this week for you to watch. Sure, these were geared towards the retail industry vertical, but even if you aren't a retail company, these applications are built on the same technologies that are being customized for a range of industries. It's not a stretch to see how they could be applied to many other sectors beyond retail. Gartner analyst Janelle Hill recently recommended one of the best ways to accelerate your AI maturity level and get ahead of competitors is to find a use case in another industry and apply it to your own business. With that in mind, here's a quick look at some of the interesting technology spotted at NRF.
Mood Media and WestRock (partners in the Intel booth) and Smart Shelf (in the Innovation Lab exhibit) are offering systems that include shelf-mounted cameras, shelf-edge context-aware digital signage, and software that collects visual data about customers and analyzes it. For instance, the systems use machine learning to classify customers by age and gender so that the digital signage can then be customized to offer that customer the best offer for their particular demographic segment. The systems are considered a physical store equivalent of collecting such data in the same way that online retailers like Amazon have collected data and then analyzed it to provide customers with the next offer.
The software behind the Mood Media and WestRock system's gender recognition works on a scale, so, for instance, the software may determine that it is 52% confident you are a man. But retailers can set rules for the customized offers so that the segment-specific offer is only served if the software reaches 80% confidence, or some other custom level of confidence, about the customer's gender. Otherwise, the shelf signage will serve up a more generic offer.
Smart Shelf's booth representatives told me that their software-as-a-service also captures data about what the customer looks at on the shelf and what the customer reaches for. Smart Shelf retains ownership of this data but shares it only with the retail customer for whom it was collected.
Other industries may find uses in this kind of facial identification for employee access to facilities to broader security applications in physical bank locations and other public locations.
Perfitly (in the Innovation Lab exhibit area at NRF) creates virtual avatars of customers based on their measurements so that these customers can virtually try on different sizes of clothes from online retailers. Co-founder and CTO Kash Vyas told me that the company is also working to create an even simpler way for end-customers to create their own virtual avatars -- by taking two photos of themselves with their phones and uploading those to the system.
The system will use machine learning to determine the measurements and correct sizes and shapes of the avatars. Customers can then use their personal avatar to shop at online retailers who partner with Perfitly. Customers can view the fit of different sizes on the avatar in a 360 degree view. In a demo, Vyas showed how smaller sizes of a button-down shirt gaped between the buttons when worn by the avatar (which was based on founder Vyas' measurements). Larger sizes didn't have the same issues and Vyas rotated the virtual model on a tablet to show how the shirt fit in front, at the sides, and at the back of the garment as worn by the model.
AiFi created NanoStore: 24/7 AutoCheckout AI, an automated and unattended retail store concept just outside the exhibit hall. The store included technologies that tag shoppers via their mobile devices as they come through a secured entrance, monitor whether items have been removed from shelves, and add the items to the shoppers' virtual carts automatically. When the shopper leaves the store, the items are paid for via their mobile app. This is very much like the Amazon Go stores that the online retail giant has been developing and plans to expand. AiFi wasn’t the only company showing similar technology at NRF. Shanghai Yunna Smart Science and Technology Co. Ltd. also provided an in-booth demo of a similar store that combined computer vision, IoT technologies, and machine learning to operate an unstaffed store.
Other demonstrations featured robots and drones monitoring and replenishing store inventories, robots providing customer service, and a few companies providing facial recognition software.
Each of these demonstrations showed the power of combining several technologies to create applications of greater value for a particular industry, in this case, retail.
Jessica Davis has spent a career covering the intersection of business and technology at titles including IDG's Infoworld, Ziff Davis Enterprise's eWeek and Channel Insider, and Penton Technology's MSPmentor. She's passionate about the practical use of business intelligence, ... View Full Bio
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