A Chinese database has exposed 42.5 million user records that were mined from a range of popular dating apps. The database was discovered by security researcher Jeremiah Fowler, who said it was not password protected and the majority of the records appeared to be from US users. Worryingly, the data left exposed included users' IP addresses, geolocation data, age and usernames. A Chinese database has exposed 42.5 million user records that were mined from a range of popular dating apps. The database included 42.5 million user records from an array of dating apps.

Amazon has been under fire from critics concerned about the potential loss of privacy when Alexa hears your every word. So on a day Amazon unveiled its latest smart speaker with a display – the $89.99 Echo Show 5 – the company announced privacy features that will apply to all its Alexa-infused devices: notably, the ability to ask Alexa to delete the recordings of your voice captured when you summon Alexa for a task or query. Starting today, you can utter the words, "Alexa, delete what I said today" and recordings from the given day will be erased. In the coming weeks in the U.S. (and later elsewhere), you will be able to say," Alexa, delete what I just said," to wipe out the last request you made. Amazon separately put the spotlight on a new Alexa Privacy Hub meant to provide transparency around how you can ensure privacy when using Alexa and Echo devices.

Amazon has rolled out a new security feature to give users greater control over their voice recordings. The internet giant will now let users ask Alexa-equipped devices to delete their voice recordings from that day. It comes as Amazon has faced growing privacy concerns tied to its Alexa digital assistant, including who is able to access users' voice recordings and how it stores them. Amazon has rolled out a new security feature to give users greater control over their voice recordings. 'Simply say, "Alexa, delete everything I said today" and the respective recordings will be deleted,' Amazon said.

After 60 years, Artificial intelligence (AI) has moved from academic research discipline to a technology that affects people's lives every day. We have digital assistants with which you can carry rudimentary conversations, systems that make medical diagnoses more accurately than humans, and cars that drive themselves in regular traffic, for instance. At the same time, despite decades of development, AI is still in its infancy when it comes to commercial applications. There are few standards, little cooperation across companies and countries, and business users and consumers still rely on a small group of experts to be able contribute to AI solutions. There are significant issues that also need to be solved to ensure that as AI adoption grows, it creates positive effects on businesses and society.

Social aspects of connectivity and information dispersion are often ignored while weighing the potential of Internet of Things (IoT). In the specialized domain of Internet of Vehicles (IoV), Social IoV (SIoV) is introduced realization its importance. Assuming a more commonly acceptable standardization of Big Data generated by IoV, the social dimensions enabling its fruitful usage remains a challenge. In this paper, an agent-based model of information sharing between vehicles for context-aware recommendations is presented. The model adheres to social dimensions as that of human society. Some important hypotheses are tested under reasonable connectivity and data constraints. The simulation results reveal that closure of social ties and its timing impacts dispersion of novel information (necessary for a recommender system) substantially. It was also observed that as the network evolves as a result of incremental interactions, recommendations guaranteeing a fair distribution of vehicles across equally good competitors is not possible.

Several complex tasks that arise in organizations can be simplified by mapping them into a matrix completion problem. In this paper, we address a key challenge faced by our company: predicting the efficiency of artists in rendering visual effects (VFX) in film shots. We tackle this challenge by using a two-fold approach: first, we transform this task into a constrained matrix completion problem with entries bounded in the unit interval [0, 1]; second, we propose two novel matrix factorization models that leverage our knowledge of the VFX environment. Our first approach, expertise matrix factorization (EMF), is an interpretable method that structures the latent factors as weighted user-item interplay. The second one, survival matrix factorization (SMF), is instead a probabilistic model for the underlying process defining employees' efficiencies. We show the effectiveness of our proposed models by extensive numerical tests on our VFX dataset and two additional datasets with values that are also bounded in the [0, 1] interval.

Amazon has a long and rich heritage of Machine learning and Deep Learning Solutions such as personalized shopping recommendations, automated fulfillment and inventory management, robotic drones for fast delivery, a checkout-free computer vision retail experience with Amazon Go, voice interactions with Alexa and a lot more. And now, AWS is the center of gravity for Artificial Intelligence/Machine Learning because of the massive volume of data stored and processed on our Big Data platform. By lowering the barriers for AI/ML workloads with an innovative suite of products and services, AWS enables you to easily build machine learning powered smart applications to meet complex business needs. We will kick-off this workshop with an overview of the AWS AI/ML platform, demonstrating the ease of use and the art of the possible for solving a real-world scenario, followed by an AI/ML case study. We will then have two tracks with multiple hands-on sessions for building your own smart applications using AWS AI driven services such as Amazon Lex, Amazon Polly, Amazon Rekognition, as well as Amazon SageMaker, an end to end ML platform that accelerates the process of building, training, and deploying machine learning models at any scale.

What if you could wake up every morning and open the blinds before you crawl out from underneath the covers? Motorized shades are nothing new to the world of home design. But the light-blocking treatments have undergone a convenient--and smart--update in recent years. Like regular window treatments, smart blinds offer privacy, allow you to control the amount of outdoor light coming into your home, and may provide some relief to your energy bill by blocking out heat from the sun. And, just like typical window coverings, smart blinds come in a variety of styles, fabrics, and designs.

"Do you believe in magic?" Google asked attendees of its annual developer conference this May, playing the seminal Lovin' Spoonful tune as an introduction. Throughout the three-day event, company executives repeatedly answered yes while touting new features of the Google Assistant, the company's version of Alexa or Siri, that can indeed feel magical. The tool can book you a rental car, tell you what the weather is like at your mother's house, and even interpret live conversations across 26 languages. But to some of the Google employees responsible for making the Assistant work, the tagline of the conference – "Keep making magic" – obscured a more mundane reality: the technical wizardry relies on massive data sets built by subcontracted human workers earning low wages. "It's smoke and mirrors if anything," said a current Google employee who, as with the others quoted in this story, spoke on condition of anonymity because they were not authorized to speak to the press.

I started using Codota recently, and have been highly impressed with what the tool can do. Simply put, the goal of Codota is to make development simpler, and most importantly – a lot faster. Working through an implementation with the tool helping in the background is just a lot less time intensive. The best I can describe it is – Codota is learning as I'm writing code, and helping me code better. It's using AI and machine learning under the hood, and it basically gives relevant suggestions, as I'm working.