Amazon's Alexa app is finally getting a much-needed revamp. Starting today, the Alexa app will have a whole new home screen with updated navigation that highlights Alexa-centric skills and a more personalized experience. Third-party skills have also been moved off the main screen in favor of more first-party features. The main screen will now have personalized suggestions based on your Alexa usage. So if you use Alexa to play music a lot or control your smart home, you'll see those commands prominently featured on the screen.

With the continuing shift to digital, especially in the retail industry, ensuring a highly personalized shopping experience for online customers is crucial for establishing customer loyalty. In particular, product recommendations are an effective way to personalize the customer experience as they help customers discover products that match their tastes and preferences. Google has spent years delivering high-quality recommendations across our flagship products like YouTube and Google Search. Recommendations AI draws on that rich experience to give organizations a way to deliver highly personalized product recommendations to their customers at scale. Today, we are pleased to announce that Recommendations AI is now publicly available to all customers in beta.

A British company has developed an artificial voice that can speak with'deep human emotion' -- and even cry -- with complete realism. The digital helpers that we are used to -- like Alexa and Google Assistant -- tend to speak in close-to monotones, without real inflection to convey emotion. While this may suffice for voice assistants, such flat computer-generated voices are unsuitable for applications like producing dialogue for video games or film. However, technology developed by the ten-person team at the London-based firm Sonantic allows the creation of authentic-sounding lines of speech in minutes. A British company has developed an artificial voice that can speak with'deep human emotion' -- and even cry -- with complete realism (stock image) 'We create hyper-realistic artificial voices.

The smart home revolution is truly underway. With reality taking a little time to catch up to the hyperbole (femtocell-powered smart fridges, anyone?), it has now become the norm. In fact, according to a 2019 survey by Smart Home Week, 57 percent of UK homes are now equipped with some sort of smart device, and 45 percent of households are planning to make their homes even smarter in the future. The demand for smart home technology is driving diversification in device categories – virtually any appliance you can think of comes in a connected variant today – and also opening up new ways in which advanced technologies like AI and 5G can be used to drive new consumer experiences. There a number of technologies driving innovation today but at the head of that queue is arguably 5G.

Graph neural networks emerge as a promising modeling method for applications dealing with datasets that are best represented in the graph domain. In specific, developing recommendation systems often require addressing sparse structured data which often lacks the feature richness in either the user and/or item side and requires processing within the correct context for optimal performance. These datasets intuitively can be mapped to and represented as networks or graphs. In this paper, we propose the Hierarchical BiGraph Neural Network (HBGNN), a hierarchical approach of using GNNs as recommendation systems and structuring the user-item features using a bigraph framework. Our experimental results show competitive performance with current recommendation system methods and transferability.

You searched for a product on internet but didn't buy it then you open your social media sites and you see ads of those products only, again and again. You must have noticed that, right? Well this is buying pattern prediction which is possible with the help of artificial intelligence. inspit of its extensive usage in our daily lives but still not all might be aware of artificial intelligence. This is now you know and today we have come to tell you what is artificial intelligence? With extensive research and development in technology, we all are hoping that all humans will be replaced by machines. One of the challenges that scientists faced was that machines could not think like a human. They cannot behave or think like a human does and are not as much intelligent as a human. Then happened the development in science and came artificial intelligence. With this technology, machines are designed so that they can perform all humanly functions. Artificial intelligence or (AI) is a branch of computer science which range widely and works on building smart machines which are capable of performing tasks that require human intelligence. AI is not just for high level tasks but is the Order of Daily Life as it is used extensively in our daily life. The virtual personal assistants in your phone like Cortana also uses AI to accumulate information and use that data to better understand your speech, interests, likes and dislikes and provide with suitable results. Video games, smart cars, plagiarism checkers and what not, there are endless applications of artificial intelligence and learning to build artificially intelligent systems are highly valued by companies that create advanced technology and professionals who acquire these skills have their careers skyrocket in some years. If you looking to start a career in a progressive field, then AI is your answer. For more videos, keep watching now you know.

Combating fake news and misinformation propagation is a challenging task in the post-truth era. News feed and search algorithms could potentially lead to unintentional large-scale propagation of false and fabricated information with users being exposed to algorithmically selected false content. Our research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms to study the effects of algorithmic transparency on end-users. We present evaluation results and analysis from multiple controlled crowdsourced studies. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining. The study results indicate that explanations helped participants to build appropriate mental models of the intelligent assistants in different conditions and adjust their trust accordingly for model limitations.

Robotic process automation (RPA) has emerged as the leading approach to automate tasks in business processes. Moving away from back-end automation, RPA automated the mouse-click on user interfaces; this outside-in approach reduced the overhead of updating legacy software. However, its many shortcomings, namely its lack of accessibility to business users, have prevented its widespread adoption in highly regulated industries. In this work, we explore interactive automation in the form of a conversational digital assistant. It allows business users to interact with and customize their automation solutions through natural language. The framework, which creates such assistants, relies on a multi-agent orchestration model and conversational wrappers for autonomous agents including RPAs. We demonstrate the effectiveness of our proposed approach on a loan approval business process and a travel preapproval business process.

\textbf{G}raph \textbf{C}onvolutional \textbf{N}etwork (\textbf{GCN}) is widely used in graph data learning tasks such as recommendation. However, when facing a large graph, the graph convolution is very computationally expensive thus is simplified in all existing GCNs, yet is seriously impaired due to the oversimplification. To address this gap, we leverage the \textit{original graph convolution} in GCN and propose a \textbf{L}ow-pass \textbf{C}ollaborative \textbf{F}ilter (\textbf{LCF}) to make it applicable to the large graph. LCF is designed to remove the noise caused by exposure and quantization in the observed data, and it also reduces the complexity of graph convolution in an unscathed way. Experiments show that LCF improves the effectiveness and efficiency of graph convolution and our GCN outperforms existing GCNs significantly. Codes are available on \url{https://github.com/Wenhui-Yu/LCFN}.

Machine learning applications can be found in virtually every aspect of our day-to-day lives. Our product recommendations, social media feeds, email spam filters, traffic predictions, virtual personal assistants, and more, are all driven by machine learning. Companies are increasingly on the hunt for talented machine learning practitioners, so there’s no time like the present to gain those highly sought-after skills!