Amazon.com Inc. said it is halting law-enforcement use of its facial-recognition software, adding its voice to a growing chorus of companies, lawmakers and civil rights advocates calling for greater regulation of the surveillance technology amid widespread concern about its potential for racial bias. Facial-recognition technology has long been criticized for perceived bias, with studies showing most algorithms are more prone to misidentifying African-Americans' and other minorities' faces than Caucasians'.

Machine learning has moved from the stuff of science fiction to a staple of modern business, as organizations across nearly every industry vertical implement ML technologies. Doctors are using machine learning to more accurately diagnosis and treat their patients, retailers are using ML to get the right merchandise to the right stores at the right time, and researchers are utilizing the technology to develop effective new medicines. That is just a sliver of the use cases emerging, as all sectors -- from energy and utilities, to travel and hospitality, to manufacturing to logistics -- and the various functions within any given organization increasingly put machine learning to work. Machine learning is a subset of artificial intelligence, where computers use algorithms to learn from data, allowing the machines to identify patterns -- a capability that organizations can put to use in multiple ways. Experts said machine learning enables organizations to perform tasks on a scale and scope previously impossible to achieve.

A group of anonymous Walmart workers have raised concerns about the anti-shoplifting technology used to monitor the company's self-checkout kiosks. A group that calls themselves'Concerned Home Office Associates' has circulated a video documenting the system's flaws, including frequent failures to identify unscanned items, and incorrectly identifying personal items potentially shoplifted. In an email sent to company management at Walmart's headquarters in Bentonville, Arkansas, the group claims to be'past their breaking point,' saying the system's frequent false positives are irritating customers and putting workers at greater risk of COVID-19 exposure by unnecessarily having to verify customer's purchases at unsafe distances. An anonymous group of Walmart employees have raised concerns about anti-theft technology used at self-checkout kiosks, saying it's'a fake AI that just pretends to safeguard' 'It's like a noisy tech, a fake AI that just pretends to safeguard,' one of the Walmart employees, who asked to remain anonymous, told Wired. The system was originally designed by Everseen--an artificial intelligence and technology firm based in Cork, Ireland--and relies on overhead cameras, or'digital eyes,' that film customers as they scan objects into the register.

The landscape of traditional retail is experiencing a seismic shift. A rapidly evolving competitive environment, a global move towards digital shopping, and the ever-changing sentiments of highly informed buyers are forcing a new perspective in the industry. This competitive situation is forcing traditional retailers to innovate and adopt accelerated analytics, robotics, and deep learning. At Drishtic we are focused on improving the intelligence at each retail store by leveraging the data from Video cameras already deployed in the stores.

In a series of papers scheduled to be presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Amazon researchers propose complementary AI algorithms that could form the foundation of an assistant that helps customers shop for clothes. One lets people fine-tune search queries by describing variations on a product image, while another suggests products that go with items a customer has already selected. Meanwhile, a third synthesizes an image of a model wearing clothes from different product pages to demonstrate how items work together as an outfit. Amazon already leverages AI to power Style by Alexa, a feature of the Amazon Shopping app that suggests, compares, and rates apparel using algorithms and human curation. With style recommendations and programs like Prime Wardrobe, which allows users to try on clothes and return what they don't want to buy, the retailer is vying for a larger slice of sales in a declining apparel market while surfacing products that customers might not normally choose.

This paper describes the application of reinforcement learning (RL) to multi-product inventory management in supply chains. The problem description and solution are both adapted from a real-world business solution. The novelty of this problem with respect to supply chain literature is (i) we consider concurrent inventory management of a large number (50 to 1000) of products with shared capacity, (ii) we consider a multi-node supply chain consisting of a warehouse which supplies three stores, (iii) the warehouse, stores, and transportation from warehouse to stores have finite capacities, (iv) warehouse and store replenishment happen at different time scales and with realistic time lags, and (v) demand for products at the stores is stochastic. We describe a novel formulation in a multi-agent (hierarchical) reinforcement learning framework that can be used for parallelised decision-making, and use the advantage actor critic (A2C) algorithm with quantised action spaces to solve the problem. Experiments show that the proposed approach is able to handle a multi-objective reward comprised of maximising product sales and minimising wastage of perishable products.

We revisit the interest of classical statistical techniques for sales forecasting like exponential smoothing and extensions thereof (as Holt's linear trend method). We do so by considering ensemble forecasts, given by several instances of these classical techniques tuned with different (sets of) parameters, and by forming convex combinations of the elements of ensemble forecasts over time, in a robust and sequential manner. The machine-learning theory behind this is called "robust online aggregation", or "prediction with expert advice", or "prediction of individual sequences" (see Cesa-Bianchi and Lugosi, 2006). We apply this methodology to a hierarchical data set of sales provided by the e-commerce company Cdiscount and output forecasts at the levels of subsubfamilies, subfamilies and families of items sold, for various forecasting horizons (up to 6-week-ahead). The performance achieved is better than what would be obtained by optimally tuning the classical techniques on a train set and using their forecasts on the test set. The performance is also good from an intrinsic point of view (in terms of mean absolute percentage of error). While getting these better forecasts of sales at the levels of subsubfamilies, subfamilies and families is interesting per se, we also suggest to use them as additional features when forecasting demand at the item level.

Hierarchical forecasting methods have been widely used to support aligned decision-making by providing coherent forecasts at different aggregation levels. Traditional hierarchical forecasting approaches, such as the bottom-up and top-down methods, focus on a particular aggregation level to anchor the forecasts. During the past decades, these have been replaced by a variety of linear combination approaches that exploit information from the complete hierarchy to produce more accurate forecasts. However, the performance of these combination methods depends on the particularities of the examined series and their relationships. This paper proposes a novel hierarchical forecasting approach based on machine learning that deals with these limitations in three important ways. First, the proposed method allows for a non-linear combination of the base forecasts, thus being more general than the linear approaches. Second, it structurally combines the objectives of improved post-sample empirical forecasting accuracy and coherence. Finally, due to its non-linear nature, our approach selectively combines the base forecasts in a direct and automated way without requiring that the complete information must be used for producing reconciled forecasts for each series and level. The proposed method is evaluated both in terms of accuracy and bias using two different data sets coming from the tourism and retail industries. Our results suggest that the proposed method gives superior point forecasts than existing approaches, especially when the series comprising the hierarchy are not characterized by the same patterns.

AWS DeepComposer is an educational AWS service that teaches generative AI and uses Generative Adversarial Networks (GANs) to transform a melody that you provide into a completely original song. With AWS DeepComposer, you can use one of the pre-trained music genre models (such as Jazz, Rock, Pop, Symphony, or Jonathan-Coulton) or train your own. As a part of training your custom music genre model, you store your music data files in NumPy objects. This post accompanies the training steps in Lab 2 – Train a custom GAN model on GitHub and demonstrates how to convert your MIDI files to the proper training format for AWS DeepComposer. For this use case, you use your own MIDI files to train a Reggae music genre model.

Through the centuries, human civilizations have undergone periodic leaps in technology. From steam to electricity to computing, societies have made radical shifts as new ways of doing things have transformed how we work. We are now in what's been deemed the Fourth Industrial Revolution, an era of digital transformation that will forever change the way we work, and the way organizations operate. In today's world, technological advances such as automation, robotics and artificial intelligence – allow businesses to continue to grow and remain competitive. It has become clear that the organization of the future will be driven by the opportunities these technologies create.