Rite Aid has been banned from using facial recognition technology for five years over allegations that its surveillance system was used incorrectly to identify potential shoplifters, especially Black, Latino, Asian or female shoppers. The settlement with the Federal Trade Commission addresses charges that the struggling drugstore chain didn't do enough to prevent harm to its customers and implement "reasonable procedures," the government agency said. Rite Aid said late Tuesday that it disagrees with the allegations, but that it's glad it reached an agreement to resolve the issue. The FTC said in a federal court complaint that technology used by Rite Aid for several years led to thousands of incorrect matches, including an incident where Rite Aid store employees stopped and searched an 11-year-old girl. Rite Aid used facial recognition technology in hundreds of stores from October 2012 to July 2020 to identify shoppers "it had previously deemed likely to engage in shoplifting or other criminal behavior," the FTC said. The complaint noted that many images it used for its database were low-quality, coming from security cameras, employee phone cameras and news stories in some cases.

Kurt "CyberGuy" Knutsson talks about the best transmitter to use for your TV for Bluetooth earbuds or headphones. Have you ever wished self-checkout was easier than the glitchy scanning of barcodes? A new checkout process using old technology is rolling out to happy shoppers. RFID stands for "radio frequency identification," a technology that uses radio waves to identify and track objects. RFID tags are small electronic devices that can be attached to products, and RFID readers are devices that can scan the tags and communicate with them.

But the chain's "reckless" failure to adopt safeguards, coupled with the technology's long history of inaccurate matches and racial biases, ultimately led store employees to falsely accuse shoppers of theft, leading to "embarrassment, harassment, and other harm" in front of their family members, co-workers and friends, the FTC said in a statement.

With advanced AI, while every industry is growing at rocket speed, the smart home industry has not reached the next generation. There is still a huge leap of innovation that needs to happen before we call a home a Smart home. A Smart home should predict residents' needs and fulfill them in a timely manner. One of the important tasks of maintaining a home is timely grocery tracking and supply maintenance. Grocery tracking models are very famous in the retail industry but they are nonexistent in the common household. Groceries detection in household refrigerators or storage closets is very complicated compared to retail shelving data. In this paper, home grocery tracking problem is resolved by combining retail shelving data and fruits dataset with real-time 360 view data points collected from home groceries storage. By integrating this vision-based object detection system along with supply chain and user food interest prediction systems, complete automation of groceries ordering can be achieved.

Urban environments, characterized by their complex, multi-layered networks encompassing physical, social, economic, and environmental dimensions, face significant challenges in the face of rapid urbanization. These challenges, ranging from traffic congestion and pollution to social inequality, call for advanced technological interventions. Recent developments in big data, artificial intelligence, urban computing, and digital twins have laid the groundwork for sophisticated city modeling and simulation. However, a gap persists between these technological capabilities and their practical implementation in addressing urban challenges in an systemic-intelligent way. This paper proposes Urban Generative Intelligence (UGI), a novel foundational platform integrating Large Language Models (LLMs) into urban systems to foster a new paradigm of urban intelligence. UGI leverages CityGPT, a foundation model trained on city-specific multi-source data, to create embodied agents for various urban tasks. These agents, operating within a textual urban environment emulated by city simulator and urban knowledge graph, interact through a natural language interface, offering an open platform for diverse intelligent and embodied agent development. This platform not only addresses specific urban issues but also simulates complex urban systems, providing a multidisciplinary approach to understand and manage urban complexity. This work signifies a transformative step in city science and urban intelligence, harnessing the power of LLMs to unravel and address the intricate dynamics of urban systems. The code repository with demonstrations will soon be released here https://github.com/tsinghua-fib-lab/UGI.

To help with my holiday shopping this year, I recently turned to a new personal assistant online. "I'm looking for a Christmas present for my mother, who spends long hours working," I typed. "Is there something she can use in her office every day?" "Of course!" came the instant reply. "Does your mother have any specific preferences or needs for her office? For example, does she need organization tools, desk accessories, or something to help her relax during breaks?"

This article intends to systematically identify and comparatively analyze state-of-the-art supply chain (SC) forecasting strategies and technologies. A novel framework has been proposed incorporating Big Data Analytics in SC Management (problem identification, data sources, exploratory data analysis, machine-learning model training, hyperparameter tuning, performance evaluation, and optimization), forecasting effects on human-workforce, inventory, and overall SC. Initially, the need to collect data according to SC strategy and how to collect them has been discussed. The article discusses the need for different types of forecasting according to the period or SC objective. The SC KPIs and the error-measurement systems have been recommended to optimize the top-performing model. The adverse effects of phantom inventory on forecasting and the dependence of managerial decisions on the SC KPIs for determining model performance parameters and improving operations management, transparency, and planning efficiency have been illustrated. The cyclic connection within the framework introduces preprocessing optimization based on the post-process KPIs, optimizing the overall control process (inventory management, workforce determination, cost, production and capacity planning). The contribution of this research lies in the standard SC process framework proposal, recommended forecasting data analysis, forecasting effects on SC performance, machine learning algorithms optimization followed, and in shedding light on future research.

Shoppers look at clothes while others walk around Twelve Oaks Mall on Nov. 24, 2023, in Novi, Michigan. BUY SMARTER: Major retailers use AI to slash number of clothing returns when shopping online. UNPLUGGED FROM SOCIETY: Experts warn new tech could cause people to withdraw socially. TACTICAL TECH: Cheap drones can take out expensive military systems, warns former Air Force pilot pushing AI-enabled force. The military metaverse enables pilots to have more frequent training against relevant targets, Robinson said.

With the exponential increase in information, it has become imperative to design mechanisms that allow users to access what matters to them as quickly as possible. The recommendation system ($RS$) with information technology development is the solution, it is an intelligent system. Various types of data can be collected on items of interest to users and presented as recommendations. $RS$ also play a very important role in e-commerce. The purpose of recommending a product is to designate the most appropriate designation for a specific product. The major challenges when recommending products are insufficient information about the products and the categories to which they belong. In this paper, we transform the product data using two methods of document representation: bag-of-words (BOW) and the neural network-based document combination known as vector-based (Doc2Vec). We propose three-criteria recommendation systems (product, package, and health) for each document representation method to foster online grocery, which depends on product characteristics such as (composition, packaging, nutrition table, allergen, etc.). For our evaluation, we conducted a user and expert survey. Finally, we have compared the performance of these three criteria for each document representation method, discovering that the neural network-based (Doc2Vec) performs better and completely alters the results.

ood retailing is now on an accelerated path to a success penetration into the digital market by new ways of value creation at all stages of the consumer decision process. One of the most important imperatives in this path is the availability of quality data to feed all the process in digital transformation. But the quality of data is not so obvious if we consider the variety of products and suppliers in the grocery market. Within this context of digital transformation of grocery industry, Midiadia is Spanish data provider company that works on converting data from the retailers' products into knowledge with attributes and insights from the product labels, that is, maintaining quality data in a dynamic market with a high dispersion of products. Currently, they manually categorize products (groceries) according to the information extracted directly (text processing) from the product labelling and packaging. This paper introduces a solution to automatically categorize the constantly changing product catalogue into a 3-level food taxonomy. Thus, we provide four different classifiers that support a more efficient and less errorprone maintenance of groceries catalogues, the main asset of the company. Finally, we have compared the performance of these three alternatives, concluding that traditional machine learning algorithms perform better, but closely followed by the score-based approach.ood One of the most important imperatives in this path is the availability of quality data to feed all the process in digital transformation. But the quality of data is not so obvious if we consider the variety of products and suppliers in the grocery market. Within this context of digital transformation of grocery industry, Midiadia is Spanish data provider company that works on converting data from the retailers' products into knowledge with attributes and insights from the product labels, that is, maintaining quality data in a dynamic market with a high dispersion of products. Currently, they manually categorize products (groceries) according to the information extracted directly (text processing) from the product labelling and packaging. This paper introduces a solution to automatically categorize the constantly changing product catalogue into a 3-level food taxonomy.