The Assistant feed has been available since March and continues to add new capabilities. This true Assistant successor to the original Google Now is now offering more Snapshot audio suggestions, including YouTube Music, and sports results. Back in June, Assistant Snapshot picked up a "Start listening for a fresh morning" card. This was solely aimed at offering "Podcasts for you." That card is now called "Perk up with fresh audio picks" to offer "News, podcasts, and music."

If you aren't set to go back to school (either physically or remotely), a number of this week's sales can help. Amazon discounted a bunch of its Echo and Fire TV devices and you can get Apple's latest iPad mini for $50 off. A few TCL 8-series Roku TVs are half off, too, and you can stock up on some digital Nintendo Switch games in the company's latest eShop sale. These are the best deals from this week that you can still buy today. It's a good time to grab an Echo or Fire TV device now that Amazon has discounted most of them in its latest back-to-school sale.

The algorithms build on FAIR's work in January of this year, when an agent was trained in Habitat to navigate unfamiliar environments without a map. Using just a depth-sensing camera, GPS, and compass data, it learned to enter a space much as a human would, and find the shortest possible path to its destination without wrong turns, backtracking, or exploration. The first of these new algorithms can now build a map of the space at the same time, allowing it to remember the environment and navigate through it faster if it returns. The second improves the agent's ability to map the space without needing to visit every part of it. Having been trained on enough virtual environments, it is able to anticipate certain features in a new one; it can know, for example, that there is likely to be empty floor space behind a kitchen island without navigating to the other side to look.

That as a mind-set gets people narrowed. Netflix's core competency in data science enables the personalization of the streaming experience based on user behavior. Netflix classifies and tags content to get a nuanced view of consumer preferences. Netflix has developed over 1,000 tag types that classify content by genre, time period, plot conclusiveness, mood, etc. These tags help to define micro-genres, which, by 2014, had already reached 76,897.

Most smart-home owners dim lamps by screwing in smart bulbs, but plugging your lamps into a smart plug that supports dimming is an even easier solution. Plug-in smart dimmers aren't as ubiquitous as simple on/off smart plugs, but every major electrical manufacturer has one. This Enbrighten model from Jasco is based on Zigbee technology and so depends on a smart home hub that supports the same. That can be a Samsung SmartThings, a Hubitat Elevation, an Amazon Echo Plus, or a second-generation Echo Show, among others. As with its in-wall dimmer, however, the Enbrighten plug-in dimmer is not formally certified to work with SmartThings.

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We know that the range of AI-loaded smart products is constantly expanding. But what's less obvious is how AI is also transforming the world of services – enabling service-based businesses to improve their offering, and even develop entirely new services and revenue streams that are underpinned by AI. Just as in product-based businesses, AI has become a driving factor for success in the service sector. Here are three ways businesses are delivering a better service through AI. AI provides incredible opportunities to get to know your customers – what they like and don't like, what they actually do (as opposed to what they say they do), how they engage with your service, what factors would encourage them to engage more deeply, and do on.

The closed feedback loop in recommender systems is a common setting that can lead to different types of biases. Several studies have dealt with these biases by designing methods to mitigate their effect on the recommendations. However, most existing studies do not consider the iterative behavior of the system where the closed feedback loop plays a crucial role in incorporating different biases into several parts of the recommendation steps. We present a theoretical framework to model the asymptotic evolution of the different components of a recommender system operating within a feedback loop setting, and derive theoretical bounds and convergence properties on quantifiable measures of the user discovery and blind spots. We also validate our theoretical findings empirically using a real-life dataset and empirically test the efficiency of a basic exploration strategy within our theoretical framework. Our findings lay the theoretical basis for quantifying the effect of feedback loops and for designing Artificial Intelligence and machine learning algorithms that explicitly incorporate the iterative nature of feedback loops in the machine learning and recommendation process.

As recommender systems send a massive amount of content to keep users engaged, users may experience fatigue which is contributed by 1) an overexposure to irrelevant content, 2) boredom from seeing too many similar recommendations. To address this problem, we consider an online learning setting where a platform learns a policy to recommend content that takes user fatigue into account. We propose an extension of the Dependent Click Model (DCM) to describe users' behavior. We stipulate that for each piece of content, its attractiveness to a user depends on its intrinsic relevance and a discount factor which measures how many similar contents have been shown. Users view the recommended content sequentially and click on the ones that they find attractive. Users may leave the platform at any time, and the probability of exiting is higher when they do not like the content. Based on user's feedback, the platform learns the relevance of the underlying content as well as the discounting effect due to content fatigue. We refer to this learning task as "fatigue-aware DCM Bandit" problem. We consider two learning scenarios depending on whether the discounting effect is known. For each scenario, we propose a learning algorithm which simultaneously explores and exploits, and characterize its regret bound.

Recommender systems play a fundamental role in web applications in filtering massive information and matching user interests. While many efforts have been devoted to developing more effective models in various scenarios, the exploration on the explainability of recommender systems is running behind. Explanations could help improve user experience and discover system defects. In this paper, after formally introducing the elements that are related to model explainability, we propose a novel explainable recommendation model through improving the transparency of the representation learning process. Specifically, to overcome the representation entangling problem in traditional models, we revise traditional graph convolution to discriminate information from different layers. Also, each representation vector is factorized into several segments, where each segment relates to one semantic aspect in data. Different from previous work, in our model, factor discovery and representation learning are simultaneously conducted, and we are able to handle extra attribute information and knowledge. In this way, the proposed model can learn interpretable and meaningful representations for users and items. Unlike traditional methods that need to make a trade-off between explainability and effectiveness, the performance of our proposed explainable model is not negatively affected after considering explainability. Finally, comprehensive experiments are conducted to validate the performance of our model as well as explanation faithfulness.