Dating app Tinder will roll out new safety features that alerts LGBTQ users when they're using its service in a country with discriminatory laws. The new feature keys in on 70 different countries chosen with help from the the International Lesbian, Gay, Bisexual, Trans and Intersex Association and is effective today. Countries under the feature's umbrella include Iran, Saudi Arabia, Sudan, United Arab Emirates, and more. 'We're rolling out a Traveler Alert that will appear when Tinder is opened in one of these locations to ensure that our users are aware of the potential dangers the LGBTQ community faces so that they can take extra caution and do not unknowingly place themselves in danger for simply being themselves,' said the company in a statement. Tinder says the feature works by automatically hiding the user upon entering one of the countries and will appear in the form of an alert within the app.

This article was written by Kate Crawford & Vladan Joler. Below is an extract, featuring the first three sections of this long article (21 sections total.) Link to the full article is provided at the bottom. A cylinder sits in a room. It is impassive, smooth, simple and small.

Businesses can now use conversational AI to automate customer-facing touchpoints everywhere -- on social media platforms like Facebook and Twitter, on their website, their app or on voice assistant devices. Industry giants like Apple, Amazon, Baidu, Facebook, Google, IBM and Microsoft are investing large resources to drive AI progress. And though it's still relatively new among enterprises, by 2021 Gartner predicts 25% of enterprises across the globe will have a virtual assistant to handle support issues. If your organization is not yet familiar with conversational artificial intelligence, it is a set of technologies that enable computers to simulate real conversations. According to Georgia Partners, conversational AI refers to the use of messaging apps, speech-based assistants and chatbots to automate communication and create personalized customer experiences at scale.

Amazon's latest Echo Show 5 Alexa smart display is smaller, cheaper and has improved privacy, but is a £79.99 5.5in screen with a camera ready to replace your alarm clock in the bedroom? The Show 5 isn't the first Alexa smart display aimed at being your bedside clock. The Echo Spot, with its pleasingly round screen and ball-like shape, was released in 2018 and is still available for £120. While the Show 5 is not cute like the Spot, its rectangular shape makes it more practical for displaying content, even if it takes up a third more horizontal space on your bedside table. The new Alexa display looks like Amazon's 10.1in Echo Show hit with a shrink ray, replete with a fabric-covered back that is reminiscent of pre-flatscreen tube televisions.

If you ordered an Echo Show on Amazon Prime Day, you aren't alone. The smart device is one of the top-selling products from Amazon's biggest shopping event of the year. Check out the 15 deals everyone bought on Prime Day.) If you're the proud new owner of an Echo Show, you may be wondering how to set it up and exactly what it can do. Make sure to download the Alexa App on your smartphone or tablet so you're able to install skills and games on your Echo Show.

At a basic level, it's now much less clear as to what AI realistically can and cannot do, especially at the present moment. Yes, there's a lot of great speculation about what AI-driven technologies will eventually be able to do, but there are several things that we were led to believe they could do now, which turn out to be a lot less "magical" than they first appear. In the case of speech-based digital assistants, for example, there have been numerous stories written recently about how the perceived intelligence around personal assistants like Alexa and Google Assistant are really based more around things like prediction branches that have been human built after listening to thousands of hours of people's personal recordings. It's not hard to see that some of the original promise of AI isn't exactly living up to expectations. In other words, people analyzed typical conversations, based on those recordings, determined the likely steps in the dialog, and then built sophisticated logic branches based on that analysis.

Graph Neural Networks (GNNs) have been emerging as a promising method for relational representation including recommender systems. However, various challenging issues of social graphs hinder the practical usage of GNNs for social recommendation, such as their complex noisy connections and high heterogeneity. The oversmoothing of GNNs is an obstacle of GNN-based social recommendation as well. Here we propose a new graph embedding method Heterogeneous Graph Propagation (HGP) to tackle these issues. HGP uses a group-user-item tripartite graph as input to reduce the number of edges and the complexity of paths in a social graph. To solve the oversmoothing issue, HGP embeds nodes under a personalized PageRank based propagation scheme, separately for group-user graph and user-item graph. Node embeddings from each graph are integrated using an attention mechanism. We evaluate our HGP on a large-scale real-world dataset consisting of 1,645,279 nodes and 4,711,208 edges. The experimental results show that HGP outperforms several baselines in terms of AUC and F1-score metrics.

Matrix Factorization (MF) is a common method for generating recommendations, where the proximity of entities like users or items in the embedded space indicates their similarity to one another. Though almost all applications implicitly use a Euclidean embedding space to represent two entity types, recent work has suggested that a hyperbolic Poincar\'e ball may be more well suited to representing multiple entity types, and in particular, hierarchies. We describe a novel method to embed a hierarchy of related music entities in hyperbolic space. We also describe how a parametric empirical Bayes approach can be used to estimate link reliability between entities in the hierarchy. Applying these methods together to build personalized playlists for users in a digital music service yielded a large and statistically significant increase in performance during an A/B test, as compared to the Euclidean model.

Increased public interest in healthy lifestyles has motivated the study of algorithms that encourage people to follow a healthy diet. Applying collaborative filtering to build recommendation systems in domains where only implicit feedback is available is also a rapidly growing research area. In this report we combine these two trends by developing a recommendation system to suggest ingredients that can be added to a partial recipe. We implement the item-based collaborative filtering algorithm using a high-dimensional, sparse dataset of recipes, which inherently contains only implicit feedback. We explore the effect of different similarity measures and dimensionality reduction on the quality of the recommendations, and find that our best method achieves a recall@10 of circa 40%.

Machine learning techniques for Recommendation System (RS) and Classification has become a prime focus of research to tackle the problem of information overload. RS are software tools that aim at making informed decisions about the services that a user may like. On the other hand, classification technique deals with the categorization of a data object into one of the several predefined classes. In the multi-label classification problem, unlike the traditional multi-class classification setting, each instance can be simultaneously associated with a subset of labels. The focus of thesis is on the development of novel techniques for collaborative filtering and multi-label classification. We propose a novel method of constructing a hierarchical bi-level maximum margin matrix factorization to handle matrix completion of ordinal rating matrix. Taking the cue from the alternative formulation of support vector machines, a novel loss function is derived by considering proximity as an alternative criterion instead of margin maximization criterion for matrix factorization framework. We extended the concept of matrix factorization for yet another important problem of machine learning namely multi-label classification which deals with the classification of data with multiple labels. We propose a novel piecewise-linear embedding method with a low-rank constraint on parametrization to capture nonlinear intrinsic relationships that exist in the original feature and label space. We also study the embedding of labels together with the group information with an objective to build an efficient multi-label classifier. We assume the existence of a low-dimensional space onto which the feature vectors and label vectors can be embedded. We ensure that labels belonging to the same group share the same sparsity pattern in their low-rank representations.