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.

While the earlier decade was all about data communication and internet proliferation, the economy of the next few years will thrive upon digitization of systems, where we will encounter implementation of smartness into everything i.e. smart home, smart city, smart appliances, smart retail, etc. The demand for smart home appliances is increasing due to numerous advancements and adoptions of digital technologies in everyday life. IoT solutions is one of the key focus areas of digital transformation projects in the consumer electronics and home appliances industry. With the addition of smart devices and appliances, and with capabilities in sensing, connectivity, and data transmission, people can interact, collect, and analyze highly valuable data to automate various operations at home, which were previously performed manually. Today, technology has evolved to such an extent that there's a possibility to design meaningful collaboration between humans and machines, primarily due to advancements in AI.

Google is everywhere at CES 2020. With the world's largest consumer electronics showcase under way here in Las Vegas, the search giant has dispatched an army of people clad in white uniforms to spread the gospel about the Google Assistant, the company's digital concierge software. The company built a massive fun house with slides and a ball pit. The words "Hey Google," the wake phrase for the software, are plastered all over buildings and the monorail in Las Vegas, the tech show's host city. It's a classic corporate marketing blitz, but it's also an apt metaphor for Google's grand ambition: to get its software all around you -- to fill up every inch of your life, from your commute to work to your Saturday morning vacuuming the house.

Kim Komando has all the answers about dating sites, political ads, Fire TV and more. Each week, I receive tons of questions from my listeners about tech concerns, new products and all things digital. Sometimes, choosing the most interesting questions to highlight is the best part of my job. This week, I received questions about whether any old tech is worth money to collectors, dating online, the Fire Stick, political ads and whether I shut down my app. Do you have a question you'd like to ask me?