It's not every day that an 18-year-old college student catches the eye of the computing world, but when Ewin Tang took aim at recommendation algorithms similar to those commonly used by the likes of Amazon and Netflix, the University of Texas at Austin mathematics and computer science undergraduate blew up an established belief: that classical computers cannot perform these types of calculations at the speed of quantum computers. In a July 2018 paper, which Tang wrote for a senior honors thesis under the supervision of computer science professor Scott Aaronson, a leading researcher in quantum computing algorithms, she discovered an algorithm that showed classical computers can indeed tackle predictive recommendations at a speed previously thought possible only with quantum computers. "I actually set out to demonstrate that quantum machine learning algorithms are faster," she explains. "But, along the way, I realized this was not the case." Ewin Tang set out to show that quantum machine learning algorithms are faster than classical algorithms, "but ... I realized this was not the case."

Rusty Langford has been spending a lot of time lately helping companies introduce intelligent bots within their customer service centers. So he's learned a lot about when bots actually help customers and when they annoy them. Langford, who's spent more than 25 years in customer service and is the vice president of client services at Harte Hanks, says the first step to exploring bots is understanding what your customers typically need help with, and how much human interaction it takes to provide that support. Look for highly repetitive, easily defined problems, he advises--scenarios in which service center teams generally know what the conversation needs to look and feel like, and what the outcome is likely to be. "Those are safe ways for companies to put their toes in the water and begin to understand what bots can actually do," Langford says.

Personalization is a mission-critical feature of effective marketing, as studies show that a personalized journey leads to increased customer engagement and long-term loyalty. Netflix movie recommendations, Spotify music suggestions and special promotions on Amazon demonstrate that personalized content is not only becoming the norm but a consumer expectation. Businesses are accomplishing this task using machine learning, which is becoming the essential go-to tool in content personalization. Evergage, Monetate, Certona, Dynamic Yield and a number of other personalization engine vendors offer this functionality and are increasingly in demand. Gartner's "Magic Quadrant for Personalization Engines" 2019 report shows that personalization engine adoption is up 28% since 2016.

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.