Artificial intelligence (AI) is having a major impact on the retail industry. AI technology is being used to improve many aspects of retail shopping, from product recommendation engines to personalized customer service. For example, many retailers are using AI-powered chatbots to provide instant customer service, and AI algorithms to recommend products to customers based on their previous purchases and browsing history. AI is also being used to improve supply chain management and warehouse operations, helping retailers to better manage inventory and reduce costs. Overall, the use of AI in retail is helping to make the shopping experience more efficient, personalized, and convenient for customers. The world of retail shopping is constantly changing and evolving, and one of the biggest trends in recent years has been the rise of hybrid online and offline shopping.

Artificial intelligence (AI) is rapidly transforming how businesses operate and interact with their customers. From automating routine tasks to transforming decision-making processes, AI has the potential to significantly improve efficiency and effectiveness in the business world. In this article, I will discuss how AI is shaping the future of business and provide examples of some companies that are already implementing these technologies. Automation is likely to be the only major way AI will shape the future of business. This has many implications for automating business activities and affects a wide range of industries.

Fashion-model pose is an important attribute in the fashion industry. Creative directors, modeling production houses, and top photographers always look for professional models able to pose. without the skill to correctly pose, their chances of landing professional modeling employment are regrettably quite little. There are occasions when models and photographers are unsure of the best pose to strike while taking photographs. This research concentrates on suggesting the fashion personnel a series of similar images based on the input image. The image is segmented into different parts and similar images are suggested for the user. This was achieved by calculating the color histogram of the input image and applying the same for all the images in the dataset and comparing the histograms. Synthetic images have become popular to avoid privacy concerns and to overcome the high cost of photoshoots. Hence, this paper also extends the work of generating synthetic images from the recommendation engine using styleGAN to an extent.

A significant share of political discourse occurs online on social media platforms. Policymakers and researchers try to understand the role of social media design in shaping the quality of political discourse around the globe. In the past decades, scholarship on political discourse theory has produced distinct characteristics of different types of prominent political rhetoric such as deliberative, civic, or demagogic discourse. This study investigates the relationship between social media reaction mechanisms (i.e., upvotes, downvotes) and political rhetoric in user discussions by engaging in an in-depth conceptual analysis of political discourse theory. First, we analyze 155 million user comments in 55 political subforums on Reddit between 2010 and 2018 to explore whether users' style of political discussion aligns with the essential components of deliberative, civic, and demagogic discourse. Second, we perform a quantitative study that combines confirmatory factor analysis with difference in differences models to explore whether different reaction mechanism schemes (e.g., upvotes only, upvotes and downvotes, no reaction mechanisms) correspond with political user discussion that is more or less characteristic of deliberative, civic, or demagogic discourse. We produce three main takeaways. First, despite being "ideal constructs of political rhetoric," we find that political discourse theories describe political discussions on Reddit to a large extent. Second, we find that discussions in subforums with only upvotes, or both up- and downvotes are associated with user discourse that is more deliberate and civic. Third, social media discussions are most demagogic in subreddits with no reaction mechanisms at all. These findings offer valuable contributions for ongoing policy discussions on the relationship between social media interface design and respectful political discussion among users.

While EON Reality's AI-powered XR solutions have long held the collective knowledge of millions, EON AI Assistant makes all of that information available and properly presented in the blink of an eye. Whether a part of the enterprise-focused modules such as EON Sales Assistant, EON AI Virtual Trainer and EON MRO Assist or any of the vast possibilities for academic-based experiences, libraries of information from the world's greatest minds are always mere seconds away from being an interactive and immersive XR experience.

The sole aim of this book is to give a self-contained introduction to concepts and mathematical tools in Bayesian matrix decomposition in order to seamlessly introduce matrix decomposition techniques and their applications in subsequent sections. However, we clearly realize our inability to cover all the useful and interesting results concerning Bayesian matrix decomposition and given the paucity of scope to present this discussion, e.g., the separated analysis of variational inference for conducting the optimization. We refer the reader to literature in the field of Bayesian analysis for a more detailed introduction to the related fields. This book is primarily a summary of purpose, significance of important Bayesian matrix decomposition methods, e.g., real-valued decomposition, nonnegative matrix factorization, Bayesian interpolative decomposition, and the origin and complexity of the methods which shed light on their applications. The mathematical prerequisite is a first course in statistics and linear algebra. Other than this modest background, the development is self-contained, with rigorous proof provided throughout.

The use of AI/ML capabilities for improving HCI/UX work and delivering better UX in solutions is becoming a trend (Abbas et al., 2022; Wu et al., 2019; Nikiforova et al., 2021) and creates many new opportunities for HCI/UX professionals (Holmquist, 2017; Yang et al., 2020). Some even speculate "AI/ML is the new UX" (Yang et al., 2018). Researchers proposed that AI can perform as an assistant, collaborator, researcher, or facilitator (Bertão & Joo, 2021; Main & Grierson, 2020). AI technology will change the role of designers in the design process and generate an opportunity for creative collaboration between AI and designers (McCormack et al., 2020). Also, companies are moving fast to adopt AI for improving customer experience (CX).

If you're dating and haven't found any success on the endless dating apps in the App Store, you may want to consider joining the group of singles using Instagram as a replacement. DMing (direct messaging) on the social platform--with over 1 billion monthly users--isn't new, but young people are now using it, and Instagram's newer app features, to find better dating success than from traditional dating apps. These Instagram users are not alone– successful celebrity couples like Joe Jonas and Sophie Turner, and Simone Biles and Jonathan Owens, haven't shied away from the fact they've connected via the platform's DMs. Even Meghan Markle wouldn't have become royalty had her friend not posted an Instagram photo of Markle that caught the attention of her now-husband, Prince Harry. Shooting your shot in the DMs can seem daunting, so here are some tips for flirting and dating via Instagram, according to the Gen Z users finding success with it.

By providing explanations for users and system designers to facilitate better understanding and decision making, explainable recommendation has been an important research problem. In this paper, we propose Counterfactual Explainable Recommendation (CountER), which takes the insights of counterfactual reasoning from causal inference for explainable recommendation. CountER is able to formulate the complexity and the strength of explanations, and it adopts a counterfactual learning framework to seek simple (low complexity) and effective (high strength) explanations for the model decision. Technically, for each item recommended to each user, CountER formulates a joint optimization problem to generate minimal changes on the item aspects so as to create a counterfactual item, such that the recommendation decision on the counterfactual item is reversed. These altered aspects constitute the explanation of why the original item is recommended. The counterfactual explanation helps both the users for better understanding and the system designers for better model debugging. Another contribution of the work is the evaluation of explainable recommendation, which has been a challenging task. Fortunately, counterfactual explanations are very suitable for standard quantitative evaluation. To measure the explanation quality, we design two types of evaluation metrics, one from user's perspective (i.e. why the user likes the item), and the other from model's perspective (i.e. why the item is recommended by the model). We apply our counterfactual learning algorithm on a black-box recommender system and evaluate the generated explanations on five real-world datasets. Results show that our model generates more accurate and effective explanations than state-of-the-art explainable recommendation models.

Healthcare: AI-powered tools and systems can help improve the accuracy and efficiency of medical diagnoses and treatment plans, which can improve patient outcomes and increase overall well-being. Personalization: AI-powered platforms can provide personalized recommendations for products, media, and services, which can help individuals discover new things that align with their interests and preferences, thus increasing their happiness. Social Connection: AI-powered chatbots and virtual assistants can facilitate communication and connection with others, especially for people who have difficulty interacting with others in person, such as elderly or disabled individuals. Productivity: AI-powered tools can help individuals and businesses increase efficiency and automate repetitive tasks, which can free up time and energy for more enjoyable and fulfilling activities. Entertainment: AI-powered personal assistants like Amazon Echo and Google Home can provide entertaining and informative content, play music, answer questions and control other smart home devices, giving users more control of their environment, and making their experience more pleasant.