GPU manufacturer Nvidia is expanding its enterprise software offering with three new AI workflows for retailers it hopes will also drive sales of its hardware accelerators.\n\nThe workflows are built on Nvidia\u2019s existing AI technology platform. One tracks shoppers and objects across multiple camera views as a building block for cashierless store systems; one aims to prevent ticket-switching fraud at self-service checkouts; and one is for building analytics dashboards from surveillance camera video.\n\nNvidia isn\u2019t packaging these workflows as off-the-shelf applications, however. Instead, it will make them available for enterprises to integrate themselves, or to buy as part of larger systems developed by startups or third-party systems integrators.\n\n\u201cThere are several of them out there, globally, that have successfully developed these kinds of solutions, but we're making it easier for more software companies and also system integrators to build these kinds of solutions,\u201d said Azita Martin, Nvidia\u2019s VP of retail.\n\nShe expects that demand for the software will drive sales of edge computing products containing Nvidia\u2019s accelerator chips, as latency issues mean the algorithms for cashierless and self-checkout systems need to be running close to the checkout and not in some distant data center.\n\nIn addition to tracking who is carrying what items out of the store, the multiple camera system can also recognize when items have been put back on the wrong shelf, directing staff to reshelve them so that other customers can find them and stock outages are avoided, she said.\n\n\u201cWe\u2019re seeing huge adoption of frictionless shopping in Asia-Pacific and Europe, driven by shortage of labor,\u201d said Martin.\n\nNvidia will face competition from Amazon in the cashierless store market, though, since while Amazon initially developed its Just Walk Out technology for use in its own Amazon Go and Amazon Fresh stores, it\u2019s now offering it to third-party retailers, too. The first non-Amazon supermarket to use the company\u2019s technology opened in Kansas City in December.\n\nAssessing cost control\n\nThe tool to prevent ticket switching is intended to be integrated with camera-equipped self-service point-of-sale terminals, augmenting them with the ability to identify the product being scanned and verify it matches the barcode.\n\nThe cost of training the AI model to recognize these products went beyond the usual spending on computing capacity.\n\n\u201cWe bought tens of thousands of dollars of products like steak and Tide and beer and razors, which are the most common items stolen, and we trained these algorithms,\u201d said Martin.\n\nNvidia kept its grocery bill under control using its Omniverse simulation platform. \u201cWe didn't buy every size of Tide and every packaging of beer,\u201d she adds. \u201cWe took Omniverse and created synthetic data to train those algorithms even further for higher accuracy.\u201d\n\nBeer presents a particular challenge for the image recognition system, as it often sells in different-size multipacks or in special-edition packaging associated with events like the Super Bowl. However, the system continues to learn about new product formats and packaging from images captured at the checkout.\n\nWhile implementation will be left up to retailers and their systems integrators, Martin suggested the tool might be used to lock up a point-of-sale terminal when ticket switching is suspected, summoning a member of staff to reset it and help the customer rescan their items.\n\nNvidia is touting high accuracy for its algorithms, but it remains to be seen how this will work out in deployment.\n\n\u201cThese algorithms will deliver 98% accuracy in detecting theft and shutting down the point of sale and preventing it,\u201d she said.\n\nBut that still leaves a 2% false positive rate, so CIOs will want to carefully monitor the potential impact on profitability, customer satisfaction, and frequent resets to prevent ticket switching.\n\nA $100 billion problem\n\nA 2022 survey by the National Retail Federation found that inventory shrink amounted to 1.44% of revenue \u2014 a relatively stable figure over the last decade \u2014 and in 2021, losses due to shrink totaled almost $100 billion, the NRF estimated.\n\nOf that, survey respondents said 26% was due to process or control failures, 29% due to employee or internal theft, and 37% due to external theft.\n\nBut Nvidia suggests that its loss prevention technology could eliminate 30% of shrinkage. That, though, would mean it could prevent four-fifths of all external retail theft, even though in addition to ticket switching, that category also includes shoplifting and organized retail crime activities such as cargo theft, and the use of stolen or cloned credit cards to obtain merchandise.\n\nPlus, potential gains must be weighed against the cost of deploying the technology, which, Martin says, \u201cdepends on the size of the store, the number of cameras and how many stores you deploy it to.\u201d\n\nMore positively, Nvidia is also offering AI workflows that can process surveillance camera video feeds to generate a dashboard of retail analytics, including a heatmap of the most popular aisles and hour-by-hour trends in customer count and dwell time. \u201cAll of this is incredibly important in optimizing the merchandising, how the store is laid out, where the products go, and on what shelves to drive additional revenue,\u201d Martin said.