Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage. Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Arcanum Digitheca, a free service provided by Arcanum, which enables you to search and explore Hungarian cultural heritage, including 600,000 faces over 500,000 images. For example, you can find historical works by author Mór Jókai or photos on topics like weddings. The Arcanum team chose Amazon Rekognition to free valuable staff from time and cost-intensive manual labeling, and improved label accuracy to make 200,000 previously unsearchable images (approximately 40% of image inventory), available to users.
Historically, humans have observed animal behaviors and applied them for different purposes. For example, behavioral observation is important in animal ecology, such as how often the behaviors are, when the behaviors occur, or whether there is individual difference or not. However, identifying and monitoring these behaviors and movements can be hard and can take a long time. To provide an automation for this workflow, a team from the agile members of pharmaceutical customer (Sumitomo Dainippon Pharma Co., Ltd.) and AWS Solutions Architects created a solution with Amazon Rekognition Custom Labels. Amazon Rekognition Custom Labels makes it easy to label specific movements in images, and train and build a model that detects these movements.
Today, Amazon Web Services (AWS) launched Amazon Rekognition Custom Labels, a new feature of Amazon Rekognition that enables customers to build their own machine learning (ML) based image analysis capabilities to detect unique objects and scenes, relevant to their business need. For example, customers using Amazon Rekognition to detect machine parts from images can now train a ML model with a small set of labeled images to detect "turbochargers" and "torque converters" without needing any ML expertise. Instead of having to train a model from scratch, which requires specialized machine learning expertise and millions of high-quality labeled images, customers can now use Amazon Rekognition Custom Labels to achieve state-of-the-art performance for their unique image analysis needs.
Amazon frequently receives credit for successfully employing machine learning to engage consumers and drive sales with its well-known recommendation engine, which generates 35% of the company's revenue, according to McKinsey . However, competitor Walmart has a surprising amount of machine learning activity going on behind the scenes. For instance, Walmart created a facial recognition system that allowed the company to pinpoint customers who were unhappy about waiting in line. The system alerted sales associates that new lanes needed to be opened, which increased customer satisfaction and helped the retailer to manage employee workflow more efficiently. While hotels are, in some ways, worlds away from retailers in terms of the scope of operations and product, the hospitality industry can learn from the experience of retailers when it comes to machine learning, positive customer service, and merchandising.
Shelf-mounted cameras paired with artificial intelligence facial recognition software that can identify a person's age, gender, and ethnicity were one of the emerging systems being pitched to retail companies during this year's National Retail Federation Big Show in New York in January. The idea was to give physical stores demographic information that could guide how they market to individual customers. It's something that could give them a competitive edge against online retailers such as Amazon, that have been leveraging customer data all along. But using cameras to capture photos of your customers in a way they may not even notice seems like it could be crossing that line between cool technology and creepy technology. Beyond that, there could be other problems, too.
If you think that freezer door just gave you a second look, you might be right. In their efforts to eliminate marketing misfires in the aisles, more retailers are investing in ways to physically connect with their customers within their stores. From cooler doors that recognize a face to dressing room mirrors that can dim the lights, retailers are investing in artificial intelligence (AI) for one key purpose: to accurately anticipate customer behavior at scale. This was a theme recently of the National Retail Federation's Big Show in New York. Specifically, retailers are using AI, facial recognition and other advanced technologies for their physical tracking capabilities, to make better sense of the factors that influence shopper purchase decisions in real time.
A research paper and associated article published yesterday made claims about the accuracy of Amazon Rekognition. We welcome feedback, and indeed get feedback from folks all the time, but this research paper and article are misleading and draw false conclusions. This blog post shares details which we hope will help clarify several misperceptions and inaccuracies. People often think of accuracy as an absolute measure, such as a percentage score on a math exam, where each answer is either right or wrong. To understand, interpret, and compare the accuracy of machine learning systems, it's important to understand what is being predicted, the confidence of the prediction, and how the prediction is to be used, which is impossible to glean from a single absolute number or score.
Malcolm Fisher of Domino's Inc. learns about the Zivelo self-order kiosk from Mike Moon at the NRF Big Show. The merging of digital and physical retail continues to advance at a rapid pace, giving new life to an industry that many believed was headed for oblivion. The race to introduce interactive technologies in stores has unleashed a historic demand for self-service kiosks that was in full view at the NRF Big Show at Javits Center in New York City this week. Self-serve kiosks were dominant on the trade show floor, offering a range of technologies such as artificial intelligence, robotics, virtual reality, augmented reality, facial recognition, voice recognition, machine learning, advanced analytics, digital currency acceptance and more. The cashierless store concept, spearheaded in the past year by Amazon Go, has spawned scores of competitors, several of which were on display at NRF.
Consumers want a seamless user experience from start to finish, complete with a user-friendly platform and outstanding customer service. To achieve this, companies are increasingly investing in AI software development to help them improve UX and gain brand loyalty. Let's look at the ways in which artificial intelligence can enhance user experience. The relatively new field, emotion AI, represents one of the most cutting-edge applications of AI. Emotion AI uses facial recognition software, data, and machine learning algorithms to sort and categorize emotional responses to a variety of stimuli like the colors on a website, product offerings, and advertising content, among others.
Have a nagging feeling that someone -- or something -- is watching you? Chances are it's a camera. Cities around the world employ computer vision-enabled CCTV to monitor vehicle and pedestrian traffic. And in the near future, brick-and-mortar stores might use them to nab shoplifters. Japanese telecom company NTT East teamed up with tech startup Earth Eyes to create AI Guardsman, a machine learning system that attempts to catch crooks in the act.