Google Play Music is currently the best streaming music service for people who have their own music collections. The service lets users upload 50,000 of their own music files, then access the audio on a wide range of streaming devices. It's a great way to access your own music files from anywhere, and it doesn't cost a dime. Unfortunately, the free ride is just about over. At the end of this year, Google will discontinue Google Play Music and push users over to YouTube Music as a replacement.

Artificial intelligence (AI) has reached new heights. The mix of the innovation, information, and talent that make intelligent frameworks possible has arrived at a critical stage, driving phenomenal development in AI investment. The time of AI has arrived. Established organizations are now moving past experimentation. Money is flowing into AI advances and applications at large organizations.

You must have heard about machine learning as it has become a buzzword. Machine learning is an innovative method of analyzing data that has the capability to automate analytical model building. It is a field of computer science and an important branch of artificial intelligence. Machine learning is based on the revolutionary idea that computer systems could learn from data, just like humans. As a result, they can identify patterns and make informed decisions without resorting to much human intervention. Machine learning is now a keyword in the world of technology.

Parents want the best for their children. While the goal is apparent, figuring out how to do it is challenging. Parents want their kids to be healthy, happy, secure, smart, sociable, smart, athletic, etc. That is a lot to do considering that they have to balance it out based on how quality time they can make for their child, how much resources they can put in to child development, meeting socio-economic needs, etc. With everyone turning to artificial intelligent assistants for help, could there be an AI digital assistant for parents doing one of the most human of things: raising their children?

Graph-based recommender systems (GRSs) analyze the structural information in the graphical representation of data to make better recommendations, especially when the direct user-item relation data is sparse. Ranking-oriented GRSs that form a major class of recommendation systems, mostly use the graphical representation of preference (or rank) data for measuring node similarities, from which they can infer a recommendation list using a neighborhood-based mechanism. In this paper, we propose PGRec, a novel graph-based ranking-oriented recommendation framework. PGRec models the preferences of the users over items, by a novel graph structure called PrefGraph. This graph is then exploited by an improved embedding approach, taking advantage of both factorization and deep learning methods, to extract vectors representing users, items, and preferences. The resulting embedding are then used for predicting users' unknown pairwise preferences from which the final recommendation lists are inferred. We have evaluated the performance of the proposed method against the state of the art model-based and neighborhood-based recommendation methods, and our experiments show that PGRec outperforms the baseline algorithms up to 3.2% in terms of NDCG@10 in different MovieLens datasets.

Google wants to make its smart displays more fun. Today, it's adding a handful of new games that you can play with Google Assistant on smart displays like the Nest Hub Max. If you're a trivia fan, you might choose to play Jeopardy! There are also wordplay games, a drawing game and Google's version of an escape-the-room challenge. Google Assistant was already capable of playing a few voice-based games.

During Alexa Live 2020, Amazon announced a new Alexa Skills Challenge with conversations. The deadline to submit is at 5pm EDT on September 14, 2020 (47 days away). You can submit and find the full details by visiting https://alexaconversations.devpost.com/. Here are some items to get started with. Good luck if you choose to participate.

Apple has updated its Store app for iOS and iPadOS with a new tab that shows all the devices linked to your Apple ID along with shopping suggestions based on that list. As 9to5Mac notes, tapping on the tab shows an overview of the iPhones, iPads and Macs you have under the "Your devices" list. The new section also shows accessories you can buy that are compatible with your devices. And if you have an iPhone, tapping on it shows its warranty information. In case it doesn't have one anymore, the app will display a trade-in value instead, as well as a quick link to start the Apple Trade In process.

The problem of MPI programs execution time prediction on a certain set of computer installations is considered. This problem emerges with orchestration and provisioning a virtual infrastructure in a cloud computing environment over a heterogeneous network of computer installations: supercomputers or clusters of servers (e.g. mini data centers). One of the key criteria for the effectiveness of the cloud computing environment is the time staying by the program inside the environment. This time consists of the waiting time in the queue and the execution time on the selected physical computer installation, to which the computational resource of the virtual infrastructure is dynamically mapped. One of the components of this problem is the estimation of the MPI programs execution time on a certain set of computer installations. This is necessary to determine a proper choice of order and place for program execution. The article proposes two new approaches to the program execution time prediction problem. The first one is based on computer installations grouping based on the Pearson correlation coefficient. The second one is based on vector representations of computer installations and MPI programs, so-called embeddings. The embedding technique is actively used in recommendation systems, such as for goods (Amazon), for articles (Arxiv.org), for videos (YouTube, Netflix). The article shows how the embeddings technique helps to predict the execution time of a MPI program on a certain set of computer installations.

The video game industry has adopted recommendation systems to boost users interest with a focus on game sales. Other exciting applications within video games are those that help the player make decisions that would maximize their playing experience, which is a desirable feature in real-time strategy video games such as Multiplayer Online Battle Arena (MOBA) like as DotA and LoL. Among these tasks, the recommendation of items is challenging, given both the contextual nature of the game and how it exposes the dependence on the formation of each team. Existing works on this topic do not take advantage of all the available contextual match data and dismiss potentially valuable information. To address this problem we develop TTIR, a contextual recommender model derived from the Transformer neural architecture that suggests a set of items to every team member, based on the contexts of teams and roles that describe the match. TTIR outperforms several approaches and provides interpretable recommendations through visualization of attention weights. Our evaluation indicates that both the Transformer architecture and the contextual information are essential to get the best results for this item recommendation task. Furthermore, a preliminary user survey indicates the usefulness of attention weights for explaining recommendations as well as ideas for future work. The code and dataset are available at: https://github.com/ojedaf/IC-TIR-Lol.