Labor Day weekend deals extended into the rest of the week with a number of sales on electronics and home appliances. Amazon's $99 Instant Pot deal is still available while August's latest WiFi smart lock can still be had for $30 off. Some of the latest gadgets are on sale, too, including Apple's 2020 iPad Pros and Samsung's Galaxy Note 20 Ultra smartphone (which just came out). Here are the best deals from this week that you can still grab today. The Galaxy Note 20 Ultra has only been available for about a month and it's already on sale.

This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision. Also stochastic Hopfield network with hidden units. Also exhaustive search or generate and test. Also deep structured learning or hierarchical learning.

Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the "music mainstream" strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervised learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user's country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-a-vis state-of-the-art algorithms that do not exploit this type of context information.

With children talking to smart-speakers, smart-phones and even smart-microwaves daily, it is increasingly important to educate students on how these agents work-from underlying mechanisms to societal implications. Researchers are developing tools and curriculum to teach K-12 students broadly about artificial intelligence (AI); however, few studies have evaluated these tools with respect to AI-specific learning outcomes, and even fewer have addressed student learning about AI-based conversational agents. We evaluate our Conversational Agent Interface for MIT App Inventor and workshop curriculum with respect to eight AI competencies from the literature. Furthermore, we analyze teacher (n=9) and student (n=47) feedback from workshops with the interface and recommend that future work leverages design considerations from the literature to optimize engagement, collaborates with teachers, and addresses a range of student abilities through pacing and opportunities for extension. We found students struggled most with the concepts of AI ethics and learning, and recommend emphasizing these topics when teaching. The appendix, including a demo video, can be found here: https://gist.github.com/jessvb/1cd959e32415a6ad4389761c49b54bbf

Game recommendation is an important application of recommender systems. Recommendations are made possible by data sets of historical player and game interactions, and sometimes the data sets include features that describe games or players. Collaborative filtering has been found to be the most accurate predictor of past interactions. However, it can only be applied to predict new interactions for those games and players where a significant number of past interactions are present. In other words, predictions for completely new games and players is not possible. In this paper, we use a survey data set of game likes to present content based interaction models that generalize into new games, new players, and both new games and players simultaneously. We find that the models outperform collaborative filtering in these tasks, which makes them useful for real world game recommendation. The content models also provide interpretations of why certain games are liked by certain players for game analytics purposes.

Beyond asking for the latest temperature, calendar appointments and recipes, Amazon Echo and Google Nest Hub devices can be used for phone calls. Amazon announced on Wednesday a new alliance with wireless carrier AT&T to enable AT&T customers (on "eligible rate plans") to link their mobile numbers and turn their speaker into a two-way phone. This will enable them to make calls and answer their phone from contacts at home by saying "Alexa answer" without having to search for the phone, or answer on a dead battery. You can also have a choice of where you want to answer, via the phone, on your device, or Echo speaker. The alliance is exclusive with AT&T.

OLED TVs, as expensive as they can be, are a favorite among display aficionados. Both Sony and LG announced new OLED TVs earlier this year at CES and now there's actually a decent sale on one of them. The Sony 55-inch A8H 4K OLED TV is $400 off right now at a bunch of retailers, which brings its price down to $1,499. It goes without saying that OLED TVs are worth the money if you care about color quality and getting the blackest blacks possible. It's also an Android TV, so it has built-in Google Cast and support for voice assistants including the Google Assistant and Amazon's Alexa.

If you want to create a world-class recommendation system, follow this recipe from a global team of experts: Blend a big helping of GPU-accelerated AI with a dash of old-fashioned cleverness. The proof was in the pudding for a team from NVIDIA that won this year's ACM RecSys Challenge. The competition is a highlight of an annual gathering of more than 500 experts who present the latest research in recommendation systems, the engines that deliver personalized suggestions for everything from restaurants to real estate. At the Sept. 22-26 online event, the team will describe its dish, already available as open source code. They're also sharing lessons learned with colleagues who build NVIDIA products like RAPIDS and Merlin, so customers can enjoy the fruits of their labor.

Multi-objective gradient methods are becoming the standard for solving multi-objective problems. Among others, they show promising results in developing multi-objective recommender systems with both correlated and uncorrelated objectives. Classic multi-gradient descent usually relies on the combination of the gradients, not including the computation of first and second moments of the gradients. This leads to a brittle behavior and misses important areas in the solution space. In this work, we create a multi-objective Adamize method that leverage the benefits of the Adam optimizer in single-objective problems. This corrects and stabilizes the gradients of every objective before calculating a common gradient descent vector that optimizes all the objectives simultaneously. We evaluate the benefits of Multi-objective Adamize on two multi-objective recommender systems and for three different objective combinations, both correlated or uncorrelated. We report significant improvements, measured with three different Pareto front metrics: hypervolume, coverage, and spacing. Finally, we show that the Adamized Pareto front strictly dominates the previous one on multiple objective pairs.

In the WWW (World Wide Web), dynamic development and spread of data has resulted a tremendous amount of information available on the Internet, yet user is unable to find relevant information in a short span of time. Consequently, a system called recommendation system developed to help users find their infromation with ease through their browsing activities. In other words, recommender systems are tools for interacting with large amount of information that provide personalized view for prioritizing items likely to be of keen for users. They have developed over the years in artificial intelligence techniques that include machine learning and data mining amongst many to mention. Furthermore, the recommendation systems have personalized on an e-commerce, on-line applications such as Amazon.com, Netflix, and Booking.com. As a result, this has inspired many researchers to extend the reach of recommendation systems into new sets of challenges and problem areas that are yet to be truly solved, primarily a problem with the case of making a recommendation to a new user that is called cold-state (i.e. cold-start) user problem where the new user might likely not yield much of information searched. Therfore, the purpose of this paper is to tackle the said cold-start problem with a few effecient methods and challenges, as well as identify and overview the current state of recommendation system as a whole