Today, Sennheiser introduced the Momentum True Wireless earbuds, which provide stereo sound thanks to 7mm dynamic drivers. These earbuds have a four-hour battery life; the charging case holds an additional eight hours of capacity. They will be available starting in mid-November for $299.95. The Momentum True Wireless earbuds are compatible with smart assistants like Siri and Google Assistant. Two-mic beamforming helps make picking up your voice in a noisy room clearer -- this translates to better phone calls and interaction with voice assistants.

Fans of Sony's high-end audio products will soon be able to control what they listen to – using the sound of their voices. The Japanese firm has announced that users will be able to use Amazon's Alexa smart assistant to control its soundbar and wireless headphones. This will give them access to tens of thousands of skills built into the AI software, including asking to play music, hear the news, and control smart home devices. Owners of compatible hardware will need to install the latest firmware updates to enable the voice assistant. Sony has yet to confirm the exact headphone and soundbars models that will get the firmware update.

Most people on Earth can speak two or more languages, but voice-operated virtual assistants have always forced them to pick and use just one--at least until today. Google Assistant is now the first multilingual virtual assistant. Users can specify that they want listening done in two languages in the app's settings on their phone or Google Home smart speaker. Then, a person can call out requests or commands in either language. Yell "Hey Google, turn off the hallway light!" as you walk out the house, and darkness will fall.

Today, Google announced that its smart assistant is now bilingual. While Google Assistant could already understand multiple languages, now you can speak two languages interchangeably and Assistant will be able to follow what you're saying. Supported languages include any pairing of English, German, French, Spanish, Italian and Japanese. More languages will be added in the next few months. Google is also expanding the availability of its smart speaker Google Home Max.

Gartner has said that chatbots and virtual assistants are between two and five years away from mainstream adoption, while speech recognition will reach that point in the next two years. The latest Hype Cycle - Gartner's visual tool for showing how close developing technologies are to mainstream adoption - puts chatbots at the early innovation trigger stage, while virtual personal assistants have passed the peak of inflated expectations and are on their way down to the trough of disillusionment. Despite the gap between them on the Hype Cycle, Gartner forecasts that both chatbots and virtual assistants will reach the mainstream at a roughly similar time, shortly after speech recognition - which they add value to. "The effects of speech recognition can be seen on a daily basis," said analyst Matthew Cain. Speech-to-text applications have proliferated due to the adoption of chatbots and virtual personal assistants (VPAs) by businesses, and consumer adoption of devices with speech interactions including smartphones, gaming consoles and specifically, VPA speakers." Van Baker, research VP at Gartner, said, "Increasingly, behaviour and event triggers will enhance virtual assistants.

Google's big, premium Apple HomePod rival the Home Max is finally being released in the UK today, bringing Google Assistant to the high-end smart speaker market. Announced in October 2017 and on sale in the US since November, the Home Max joins Google's smaller Home and smallest Home Mini smart speakers as the big one. Google Assistant sorts voice commands, controls and questions exactly the same as Google's smaller smart speaker offerings, but the way it sounds couldn't be more different. The Home Max is relatively large box speaker, by smart speaker standards, with a white or black smooth plastic body and grey or charcoal coloured fabric front. In white and grey it's simple fabric front and rounded corners make the Home Max look a bit bland up next to a Sonos Play:5 or an Apple HomePod, but it might be easier to blend in with soft home furnishings.

Google's most expensive speaker option is finally available in the US. It's certainly taking the tech giant some time to release Home Max in markets outside North America, but it's at least making its way to more places around the globe -- it also arrived in Australia a few weeks ago. Mountain View created Home Max to be 20 times more powerful than the ordinary Home speaker, so it can fill even large rooms with high-fidelity sound and a deep, balanced bass thanks to its 4.5 -inch high- excursion woofers. It also features Google's new AI-powered technology called "Smart Sound," which allows sounds to adapt to the speaker's environment. Just place the device wherever you want, and it'll tune itself to deliver the best sound possible.

Nowadays we hear very often the words "Recommender systems" and mainly it's because they are quite often used by companies for different purposes, such as to increase sales (items' suggestion while purchasing Amazon: user that have bought this as also bought this) or in suggestions to customers to give them a better customer experience (film suggestion Netflix) or also in advertising to target the right people based on preferences similarities. The recommender systems are basically systems that can recommend things to people based on what everybody else did. Here there is an example of film suggestion taken from an online course. I want to thank Frank Kane for this very useful course on Data Science and Machine Learning with Python. Here there is the course's link in case you would like to go deeper with Data Science.

Best-in-class companies understand they are in the customer experience (CX) business, and McKinsey reports that successful ones are seeing revenue gains of 5% to 10% and cost reductions of 15% to 25% within 2 to 3 years. The evolution of CX is being driven by several key factors, including the digitization of society and data, customer demand for more control and better outcomes in their commercial relationships, and the correlation between emotional connection and customer satisfaction. Tractica has identified six use cases where artificial intelligence (AI)-driven solutions will propel CX: customer service virtual digital assistants (VDAs); live agent assist; e-commerce and sales VDAs, sentiment/emotion analysis for product/market research; sentiment/emotion analysis in retail; and sentiment/emotion analysis in healthcare. Collectively, spending on AI-driven software for these use cases will reach more than $3.2 billion in 2021 and grow to more than $9.3 billion in 2025. This Tractica white paper explores the market issues surrounding these six use cases.

Convolutional Neural Networks (CNNs) models have been recently introduced in the domain of top-$N$ session-based recommendations. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into a 2-dimensional latent matrix, and treated as an image. The convolution and pooling operations are then applied to the mapped item embeddings. In this paper, we first examine the typical session-based CNN recommender and show that both the generative model and network architecture are suboptimal when modeling long-range dependencies in the item sequence. To address the issues, we propose a simple, but very effective generative model that is capable of learning high-level representation from both short- and long-range dependencies. The network architecture of the proposed model is formed of a stack of holed convolutional layers, which can efficiently increase the receptive fields without relying on the pooling operation. Another contribution is the effective use of residual block structure in recommender systems, which can ease the optimization for much deeper networks. The proposed generative model attains state-of-the-art accuracy with less training time in the session-based recommendation task. It accordingly can be used as a powerful session-based recommendation baseline to beat in future, especially when there are long sequences of user feedback.