Artificial Intelligence, once a buzzword in the digital world, has become a part of our everyday life. From Google Assistant, Siri, Alexa to Uber and Ola, several AI-enabled services are available today that make our lives easier. The ongoing pandemic has undoubtedly impacted business models but it didn't wane the impact AI has on our lives and businesses. On the contrary, it has become evident that Artificial Intelligence, with its self-teaching and learning algorithms, will play an essential role in transforming businesses in 2021.Companies have swiftly started leveraging the potential of AI. Companies like Amazon, Microsoft, and Google have grown immensely due to the incorporation of AI for forecasting, adapting to changing market conditions and generating profit.

Recommendation systems underpin the serving of nearly all online content in the modern age. From Youtube and Netflix recommendations, to Facebook feeds and Google searches, these systems are designed to filter content to the predicted preferences of users. Recently, these systems have faced growing criticism with respect to their impact on content diversity, social polarization, and the health of public discourse. In this work we simulate the recommendations given by collaborative filtering algorithms on users in the MovieLens data set. We find that prolonged exposure to system-generated recommendations substantially decreases content diversity, moving individual users into "echo-chambers" characterized by a narrow range of content. Furthermore, our work suggests that once these echo-chambers have been established, it is difficult for an individual user to break out by manipulating solely their own rating vector.

The feedback data of recommender systems are often subject to what was exposed to the users; however, most learning and evaluation methods do not account for the underlying exposure mechanism. We first show in theory that applying supervised learning to detect user preferences may end up with inconsistent results in the absence of exposure information. The counterfactual propensity-weighting approach from causal inference can account for the exposure mechanism; nevertheless, the partial-observation nature of the feedback data can cause identifiability issues. We propose a principled solution by introducing a minimax empirical risk formulation. We show that the relaxation of the dual problem can be converted to an adversarial game between two recommendation models, where the opponent of the candidate model characterizes the underlying exposure mechanism. We provide learning bounds and conduct extensive simulation studies to illustrate and justify the proposed approach over a broad range of recommendation settings, which shed insights on the various benefits of the proposed approach.

Unlike humans, conversational artificial intelligence (AI), most commonly deployed today via chatbots, are "up" 100% of the time. Beyond chatbots, automated voice response systems (as annoying as they may still be) and virtual voice assistants all utilize conversational AI to power human-to-machine dialog. Conversational AI is the technology that allows users to ask queries to a machine and get automated responses. The most notable of these machines are the virtual assistants such as Alexa, Siri, and Google Assistant. At the heart of Conversation AI, is the utilization of Natural Language Processing (NLP).

The holiday shopping season is in full swing -- and yes, we know it's only early November. Retailers like Amazon, Walmart and Best Buy have already kicked off the first rounds of their big sale events, and there will be more to come in the lead-up to Black Friday. This week, we saw AirPods drop to a new all-time low; a great price for Samsung's Galaxy Watch 3; and a bunch of worthwhile deals at Walmart for Instant Pots, robot vacuums and more. Here are the best deals from this week that you can still get today. Now's the time to grab Apple's classic AirPods for someone on your list, or for yourself.

A smart clock is a great addition to a bedroom. Not only does it tell the time and other pertinent information, but it's also smart enough to dim at night and brighten up in the morning. Plus, they usually don't have cameras, making them better than a typical smart display for intimate spaces like your bedside. Lenovo's Smart Clock series features Google Assistant integration, too, so you can easily control your connected devices or get weather and traffic reports by speaking to the device. When the company's basic Smart Clock Essential launched this year, it was pretty affordable at just $50.

Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation, e.g., multi-armed bandit approach, addresses this limitation by interactively exploring user preference online and pursuing the exploration-exploitation (EE) trade-off. However, existing bandit-based methods model recommendation actions homogeneously. Specifically, they only consider the items as the arms, being incapable of handling the item attributes, which naturally provide interpretable information of user's current demands and can effectively filter out undesired items. In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively. This important scenario was studied in a recent work. However, it employs a hand-crafted function to decide when to ask attributes or make recommendations. Such separate modeling of attributes and items makes the effectiveness of the system highly rely on the choice of the hand-crafted function, thus introducing fragility to the system. To address this limitation, we seamlessly unify attributes and items in the same arm space and achieve their EE trade-offs automatically using the framework of Thompson Sampling. Our Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play. Extensive experiments on three benchmark datasets show that ConTS outperforms the state-of-the-art methods Conversational UCB (ConUCB) and Estimation-Action-Reflection model in both metrics of success rate and average number of conversation turns.

The study of adverse childhood experiences and their consequences has emerged over the past 20 years. In this study, we aimed to leverage explainable artificial intelligence, and propose a proof-of-concept prototype for a knowledge-driven evidence-based recommendation system to improve surveillance of adverse childhood experiences. We used concepts from an ontology that we have developed to build and train a question-answering agent using the Google DialogFlow engine. In addition to the question-answering agent, the initial prototype includes knowledge graph generation and recommendation components that leverage third-party graph technology. To showcase the framework functionalities, we here present a prototype design and demonstrate the main features through four use case scenarios motivated by an initiative currently implemented at a children hospital in Memphis, Tennessee. Ongoing development of the prototype requires implementing an optimization algorithm of the recommendations, incorporating a privacy layer through a personal health library, and conducting a clinical trial to assess both usability and usefulness of the implementation. This semantic-driven explainable artificial intelligence prototype can enhance health care practitioners ability to provide explanations for the decisions they make.

Due to accessible big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many companies especially in the retail business because of its importance in decision making. Improvement of the forecasting accuracy, even by a small percentage, may have a substantial impact on companies' production and financial planning, marketing strategies, inventory controls, supply chain management, and eventually stock prices. Specifically, our research goal is to forecast the sales of each product in each store in the near future. Motivated by tensor factorization methodologies for personalized context-aware recommender systems, we propose a novel approach called the Advanced Temporal Latent-factor Approach to Sales forecasting (ATLAS), which achieves accurate and individualized prediction for sales by building a single tensor-factorization model across multiple stores and products. Our contribution is a combination of: tensor framework (to leverage information across stores and products), a new regularization function (to incorporate demand dynamics), and extrapolation of tensor into future time periods using state-of-the-art statistical (seasonal auto-regressive integrated moving-average models) and machine-learning (recurrent neural networks) models. The advantages of ATLAS are demonstrated on eight product category datasets collected by the Information Resource, Inc., where a total of 165 million weekly sales transactions from more than 1,500 grocery stores over 15,560 products are analyzed.

AI is not limited to any one industry; it is so diverse that it has an array of applications in various areas. Let's take another peek into Mark's life and discover AI's role in one of his ideal and amusing weekends. Mark is visiting his sister and her family in Montreal. He has decided to spend the whole weekend with them and his journey starts with an early morning flight. Once he is ready and having his breakfast, like a creature of habit he picks up his phone.