Smart displays are a unique type of product. The company is announcing today a new set of features for its smart displays, including an “improved visual experience,” a dark theme, relaxing sounds, gentle alarm and Meet improvements. Instead of sticking to a one-page layout with all your notifications and cards lined up horizontally, Google’s smart displays will now have five tabs at the top the following sections: Your Day, Home Control, Media, Communicate and Discover.

Artificial intelligence (AI) is creeping into our everyday lives, often without us realizing it. Today, AI can be found in the digital assistants we use such as Apple's (NASDAQ:AAPL) Siri and Amazon's (NASDAQ:AMZN) Alexa to check our schedules and search for things on the internet; in the cars we own that now park themselves as they are able to recognize space around the vehicle; and in the small robots we use to clean our houses, such as the Roomba vacuum. Artificial intelligence is becoming more a part of our lives all the time, and will only grow in importance in coming years. In the not too distant future, AI will influence everything from how we shop for groceries to how diseases are diagnosed and treated by doctors. It all adds up to a fast growing market.

This paper presents and characterizes an Open Application Repository for Federated Learning (OARF), a benchmark suite for federated machine learning systems. Previously available benchmarks for federated learning have focused mainly on synthetic datasets and use a very limited number of applications. OARF includes different data partitioning methods (horizontal, vertical and hybrid) as well as emerging applications in image, text and structured data, which represent different scenarios in federated learning. Our characterization shows that the benchmark suite is diverse in data size, distribution, feature distribution and learning task complexity. We have developed reference implementations, and evaluated the important aspects of federated learning, including model accuracy, communication cost, differential privacy, secure multiparty computation and vertical federated learning.

Neighborhood-based recommenders are a major class of Collaborative Filtering (CF) models. The intuition is to exploit neighbors with similar preferences for bridging unseen user-item pairs and alleviating data sparseness. Many existing works propose neural attention networks to aggregate neighbors and place higher weights on specific subsets of users for recommendation. However, the neighborhood information is not necessarily always informative, and the noises in the neighborhood can negatively affect the model performance. To address this issue, we propose a novel neighborhood-based recommender, where a hybrid gated network is designed to automatically separate similar neighbors from dissimilar (noisy) ones, and aggregate those similar neighbors to comprise neighborhood representations. The confidence in the neighborhood is also addressed by putting higher weights on the neighborhood representations if we are confident with the neighborhood information, and vice versa. In addition, a user-neighbor component is proposed to explicitly regularize user-neighbor proximity in the latent space. These two components are combined into a unified model to complement each other for the recommendation task. Extensive experiments on three publicly available datasets show that the proposed model consistently outperforms state-of-the-art neighborhood-based recommenders. We also study different variants of the proposed model to justify the underlying intuition of the proposed hybrid gated network and user-neighbor modeling components.

You might not have to depend on Google Podcasts if you're asking Assistant to play your favorite serialized audio show. Android Police and its readers have discovered that Google is adding support for third-party podcast services, starting with Spotify. You just have to visit podcast settings in Assistant to choose your provider. We've asked Google if it can comment on the feature's rollout. AP's writer had trouble getting it to work, though it may be due to regional issues.

Voice assistants such as Alexa and Siri will become common in children's bedrooms, according to a new report from Internet Matters, the online safety body, which says it is critical for parents to spend more time understanding new technology. The pandemic has accelerated the adoption of new technology at home by "three or four years", the researchers said, and families in the UK will become much more reliant on voice-enabled devices over the next five years. The report's author, Lynne Hall, professor of computer science at the University of Sunderland, said we would even see the emergence of a range of celebrity voice assistants. "You'd have Elsa from Frozen," Hall said. "You can imagine that with every Disney film that came out there would be a new voice skin."

Google promised an Assistant driving mode for phones would arrive in mid-2019, but that clearly didn't happen -- over a year passed without any sign of it. It appears to be ready, though. XDA-Developers has discovered (via Android Police) that Google Assistant's driving mode is at least partially enabled for Android users. The interface has changed considerably from the I/O 2019 demo you see above, but the concept remains the same with large buttons and text that let you chat, message and play music while keeping your driving distractions to a minimum. The rollout appears to be server-side, and might be part of a test.

Have you ever watched a video or movie because YouTube or Netflix recommended it to you? Or added a friend on Facebook from the list of "people you may know"? And how does Twitter decide which tweets to show you at the top of your feed? These platforms are driven by algorithms, which rank and recommend content for us based on our data. If you want to know when social media companies are trying to manipulate you into disclosing information or engaging more, the answer is always.

Amazon Echo can make your life easier. Amazon's Echo line is the reigning champ of the smart speaker world. Compared to virtual assistants like Google and Siri, Alexa works with far more gadgets and responds to significantly more commands than the competition. Plus, Echo devices are pretty inexpensive, starting around $25 for the Flex and $30 for the Dot when you catch it on sale. I bought six of these Echo Shows for Christmas presents.

Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommender systems. Missing-at-random (MAR) data, namely randomized controlled trials (RCTs), are usually required by most previous counterfactual learning methods for debiasing learning. However, the execution of RCTs is extraordinarily expensive in practice. To circumvent the use of RCTs, we build an information-theoretic counterfactual variational information bottleneck (CVIB), as an alternative for debiasing learning without RCTs. By separating the task-aware mutual information term in the original information bottleneck Lagrangian into factual and counterfactual parts, we derive a contrastive information loss and an additional output confidence penalty, which facilitates balanced learning between the factual and counterfactual domains. Empirical evaluation on real-world datasets shows that our CVIB significantly enhances both shallow and deep models, which sheds light on counterfactual learning in recommendation that goes beyond RCTs.