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.

Amazon's Echo Dot started out as a low-cost way to dip your toe in smart home waters, and it remains that, but with upgrades like multi-room audio, stereo pairing, and Amazon Sidewalk compatibility, even seasoned smart home fans have good reason to add a Dot or two to their smart setup. There are three versions of the Dot: the Dot, the Dot with Clock, and the Dot Kids Edition. While the majority of this review will focus on the regular Dot, all of the same features can be found on the Dot with Clock and Kids Edition. The Dot with Clock is exactly what it sounds like. It has a numerical display on the front that shows the time or status of timers you've set.

When I first watched Ironman, I was thrilled with its advancement in technologies. But what hooked me to the movie was J.A.R.V.I.S. (Just A Rather Very Intelligent System). Well, what is not to like about it. The AI system was so intelligent that it painted the ironman suit with a single command. The best thing is that now we have the best AI Android apps that are making this dream come true. AI apps are there to assist the users to achieve their daily targets and accomplish their goals. Well, some of them can have completely different functionalities that will be described further.

Consumers are ready for personalization in their banking routines. A 2019 Accenture study on consumer patterns in financial services outlines four bank consumer personas: the pioneer, the pragmatist, the skeptic and the traditionalist. Of the four groups, representing 47,000 banking and insurance customers globally, only the traditionalists -- which make up about a fifth of survey respondents -- showed any true resistance toward using personalized data to help improve the customer experience. Even then, 55 percent of traditionalists still said personalization is what they wanted. Among the largest persona group, the tech-averse skeptics, 80 percent of respondents said they would be willing to share their data in return for personalized services.

Artificial Intelligence (AI) has become a promising field in recent years. Despite the disruption 2020 has delivered, the opportunities around AI show no sign of losing their momentum. AI is no more science fiction and is already being adopted by public and private organizations globally. People are far away from the mythical thought that AI robots are coming for humans. The fact is, as a society, humankind is relying more on artificial intelligence with its growth.