After months of beta testing, Sonos is nearly ready to roll out Google Assistant to its Sonos One and Sonos Beam smart speakers. In today's quarterly earnings letter to shareholders, Sonos CEO Patrick Spence confirmed that the company is planning to launch Assistant next week. The functionality will launch in the US initially, "with more markets to come over the next few months," said Spence. "This feature will truly elevate the customer experience and marks the first time that consumers will be able to buy a single smart speaker and get to choose which voice assistant they want to use. We think giving consumers choice is always the right decision, and we anticipate this philosophy will be adopted in the industry over time," he said.

Amazon frequently receives credit for successfully employing machine learning to engage consumers and drive sales with its well-known recommendation engine, which generates 35% of the company's revenue, according to McKinsey . However, competitor Walmart has a surprising amount of machine learning activity going on behind the scenes. For instance, Walmart created a facial recognition system that allowed the company to pinpoint customers who were unhappy about waiting in line. The system alerted sales associates that new lanes needed to be opened, which increased customer satisfaction and helped the retailer to manage employee workflow more efficiently. While hotels are, in some ways, worlds away from retailers in terms of the scope of operations and product, the hospitality industry can learn from the experience of retailers when it comes to machine learning, positive customer service, and merchandising.

Last week it was reported by Bloomberg that our Alexa fears are true, that someone might be listening! Amazon came under scrutiny for having thousands of people around the world listening to Alexa commands to help improve the Alexa digital assistant powering its line of Echo speakers. According to Bloomberg the team listen to voice recordings captured in Echo owners' homes and offices. The recordings are transcribed, annotated and then fed back into the software to eliminate gaps in Alexa's understanding of human speech and help it better respond to commands. It is widely referenced that ComScore predicts that by 2020, voice will make up 50% of all searches, although criticisers of the trend cite the trust between the consumer and voice tech as a main barrier to growth.

As many as one billion people--15 per cent of the world's population--have some form of disability, with around three per cent suffering from severe disabilities, according to World Bank. For most of these people, accessing modern technology and all it has to offer presents a host of difficulties. Even something as simple as using a cell phone can be impossible. Global Accessibility Awareness Day (May 16) aims to combat that. Launched in 2015, the day is designed to get everyone thinking and talking about improving digital access and inclusion for people with disabilities.

Voice recordings captured by Amazon's Alexa can be deleted but the automatically produced transcriptions remain in the company's cloud, according to reports. After Alexa hears its'wake' word, the smart assistant starts listening and transcribing everything it hears. All the voice commands said to the virtual assistant can be deleted from the central system, but the company still has the the text logs, according to CNET. This data is kept on its cloud servers, with no option for users to delete it, but the company claims it is working on ways to make the data inaccessible. Amazon workers are listening to private and sometimes disturbing voice recordings to improve the voice-assistants understanding of human speech.

Machine learning models learn what we teach them to learn. Machine learning is at the heart of recommender systems. If a machine learning model is trained on biased data, the resulting recommender system may reflect the biases in its recommendations. Biases arise at different stages in a recommender system, from existing societal biases in the data such as the professional gender gap, to biases introduced by the data collection or modeling processes. These biases impact the performance of various components of recommender systems, from offline training, to evaluation and online serving of recommendations in production systems. Specific techniques can help reduce bias at each stage of a recommender system. Reducing bias in our recommender systems is crucial to successfully recommending dream jobs to hundreds of millions members worldwide, while being true to LinkedIn's vision: "To create economic opportunity for every member of the global workforce".

Thanks to the Big Data revolution and increasing computing capacities, Artificial Intelligence (AI) has made an impressive revival over the past few years and is now omnipresent in both research and industry. The creative sectors have always been early adopters of AI technologies and this continues to be the case. As a matter of fact, recent technological developments keep pushing the boundaries of intelligent systems in creative applications: the critically acclaimed movie "Sunspring", released in 2016, was entirely written by AI technology, and the first-ever Music Album, called "Hello World", produced using AI has been released this year. Simultaneously, the exploratory nature of the creative process is raising important technical challenges for AI such as the ability for AI-powered techniques to be accurate under limited data resources, as opposed to the conventional "Big Data" approach, or the ability to process, analyse and match data from multiple modalities (text, sound, images, etc.) at the same time. The purpose of this white paper is to understand future technological advances in AI and their growing impact on creative industries. This paper addresses the following questions: Where does AI operate in creative Industries? What is its operative role? How will AI transform creative industries in the next ten years? This white paper aims to provide a realistic perspective of the scope of AI actions in creative industries, proposes a vision of how this technology could contribute to research and development works in such context, and identifies research and development challenges.

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class.

This guide has been updated to include the new line of Echo devices, many new products with Alexa built in, and products that work with Alexa via Alexa Skills. Since Amazon unveiled the Echo smart speaker in 2014, Amazon has steadily become the hottest name in smart home products on the market. Suddenly, every tech company wants to integrate its products with Amazon's customizable virtual assistant, Alexa. The integrations keep coming, at such a pace that it can be hard to know exactly which products work with Alexa. Amazon's website tries to keep on top of things, but its interface can be tough to navigate.

Location data has been a sore subject for Google. In 2018, an Associated Press investigation found that Google continued storing phone location data even when users turned off a "location history" setting in Android. The company also revealed plans to overhaul Chrome to let users rein in so-called tracking cookies, which are bits of software that follow people around on the web. The move, which could have major repercussions for the digital advertising industry, would require companies to identify cookies used by third-party websites and advertisers to track users. "Unimpressive," declared Princeton computer scientist Jonathan Mayer, who said the scheme would be easy for advertisers to evade.