The emergence of artificial intelligence has paved a new era in mobile application development. For quite some time, mobile app developers have made an extensive amount of progress in their innovation through AI. Take for the example of Apple's SIRI. It has been used for quite a long period of time, and still, it has the huge potential of transforming the future technological revolution. Even machine learning is developing at a faster rate and users need a flexible algorithm to enhance the experience. Now the advancement and availability of AI and machine learning are building a huge advancement, especially in the way businesses, users and developers appreciate the interactions with mobile apps.

Dating apps, including Tinder, give sensitive information about users to marketing companies, according to a Norwegian study released Tuesday. Dating apps, including Tinder, give sensitive information about users to marketing companies, according to a Norwegian study released Tuesday. A group of civil rights and consumer groups is urging federal and state regulators to examine a number of mobile apps, including popular dating apps Grindr, Tinder and OKCupid for allegedly sharing personal information with advertising companies. The push by the privacy rights coalition follows a report published on Tuesday by the Norwegian Consumer Council found that 10 apps collect sensitive information including a user's exact location, sexual orientation, religious and political beliefs, drug use and other information and then transmits the personal data to at least 135 different third-party companies. The data harvesting, according to the Norwegian government agency, appears to violate the European Union's rules intended to protect people's online data, known as the General Data Protection Regulation.

We are looking for a smart and creative Machine Learning Engineer. With your expertise, you will bring new ideas to the team and improve our music recommendation services. Your role will be to to solve real world problems by applying machine learning technics. From the simplest heuristic rule to the most advanced state of the art model, you have only one goal: provide the best listening experience to our users.

LG is throwing its resources behind developing a new breed of AI assistants that can be used to control aspects of cars. The Korean tech company said it has partnered with AI company Cerence to make an AI voice-assistant that is capable of being used to control various aspects of car's entertainment system, navigation, calling and more. That AI assistant, once completed, will eventually be integrated into the company's webOS software that, similarly to Apple CarPlay, powers computers inside vehicles. LG is planning on leasing its AI assistant out to auto manufacturers in search of an added dose of technology in their vehicles. The company's decision to enter the ring on developing an in-car voice assistant comes at a time when other major auto-manufacturers have also announced their intention to create similar products.

Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences, which could alleviate the data sparsity issue in collaborative filtering based recommendation. Early approaches relied on utilizing each user's first-order social neighbors' interests for better user modeling, and failed to model the social influence diffusion process from the global social network structure. Recently, we propose a preliminary work of a neural influence diffusion network~(i.e., DiffNet) for social recommendation~(Diffnet), which models the recursive social diffusion process to capture the higher-order relationships for each user. However, we argue that, as users play a central role in both user-user social network and user-item interest network, only modeling the influence diffusion process in the social network would neglect the users' latent collaborative interests in the user-item interest network. In this paper, we propose DiffNet++, an improved algorithm of DiffNet that models the neural influence diffusion and interest diffusion in a unified framework. By reformulating the social recommendation as a heterogeneous graph with social network and interest network as input, DiffNet++ advances DiffNet by injecting these two network information for user embedding learning at the same time. This is achieved by iteratively aggregating each user's embedding from three aspects: the user's previous embedding, the influence aggregation of social neighbors from the social network, and the interest aggregation of item neighbors from the user-item interest network. Furthermore, we design a multi-level attention network that learns how to attentively aggregate user embeddings from these three aspects. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model.

LONDON – Dating apps including Grindr, OkCupid and Tinder leak personal information to advertising tech companies in possible violation of European data privacy laws, a Norwegian consumer group said in a report Tuesday. The Norwegian Consumer Council said it found "serious privacy infringements" in its analysis of how shadowy online ad companies track and profile smartphone users. The council, a government-funded nonprofit group, commissioned cybersecurity company Mnemonic to study 10 Android mobile apps. It found that the apps sent user data to at least 135 different third party services involved in advertising or behavioral profiling. "The situation is completely out of control," the council said, urging European regulators to enforce the continent's strict General Data Privacy Regulation, or GDPR.

Amazon Echo can do seven things that Google Home can't. Amazon and Google have long competed for the No. 1 smart speaker spot. And while Google Home has features that Echo doesn't, like listening to multiple commands at a time, Amazon still has a set of unique skills that Google can't yet do. Only Amazon Echo ($100 at Amazon) can guard your house and alert you if it hears something suspicious while you're gone. It also lets you know when your packages are out for delivery.

Sonos has finally made a portable wifi and Bluetooth speaker that sounds great – but it's not quite what most will have imagined. For years Sonos has made some of the very best wifi speakers, recently adding optional voice assistants from Google and Amazon. But they have never been truly wireless, needing to be plugged in and on your home wifi network. The Move changes that, essentially taking the excellent Sonos One and adding a battery to the bottom. But it comes at a hefty £399 price tag, although some retailers already have it at £329. The Move is slightly larger than the One.

Research at the Dell EMC and Intel HPC and AI Innovation Lab is demonstrating how organizations can build better, faster neural networks to drive recommendation engines. If you go to Netflix to look for the next movie you want to watch, a recommendation engine will give you suggestions tailored to your interests and past viewing experiences. When you visit a website, you're likely to see ads based on your browsing history and past purchases. If you shop on Amazon, you will get all kinds of recommendations based on your purchasing history and the purchasing history of other customers with similar interests. As Amazon explains, "We examine the items you've purchased, items you've told us you own, items you've rated, and items you've told us you like. Based on those interests, we make recommendations."

Algorithmic fairness in decision-making has been studied extensively in static settings where one-shot decisions are made on tasks such as classification. However, in practice most decision-making processes are of a sequential nature, where decisions made in the past may have an impact on future data. This is particularly the case when decisions affect the individuals or users generating the data used for future decisions. In this survey, we review existing literature on the fairness of data-driven sequential decision-making. We will focus on two types of sequential decisions: (1) past decisions have no impact on the underlying user population and thus no impact on future data; (2) past decisions have an impact on the underlying user population and therefore the future data, which can then impact future decisions. In each case the impact of various fairness interventions on the underlying population is examined.