Whether you're arriving home late at night or just need some extra assistance around the house, the addition of smart light bulbs to your living space can help you find your way around without fumbling in the dark for a traditional light switch. Smart light bulbs are just like regular light bulbs except that they can be controlled remotely by voice, Bluetooth, and mobile apps. If you're teetering on the edge of setting up your house with smart home products, light bulbs are a great place to start. However, before you outfit your house with smart light bulbs, there are some things you need to know before making your purchase. It's easy to install a smart light bulb. Just remove your current bulbs and screw in the new smart light bulbs.

Line of Duty fans have asked Amazon's Alexa voice assistant about the identity of the mysterious'H' so many times that the devices are now quipping back. Videos have emerged online of fans asking Alexa to unmask the corrupt cop atthe centre of the show's plot but the device refuses, saying she's'too scared' or'can't be bothered'. The identity of'H' has plagued viewers throughout the series but the big reveal will identify the corrupt police officer at the top of the organised crime chain who has been pulling the strings. Social media user, Daniel Smith, was among those who could not bear to wait. he filmed himself asking his Alexa: 'Who is H?' The exasperated device replied: 'Honestly, I can't be bothered to talk about this anymore. 'Too many people are asking me who H is.' Gareth Evans, from Aberdare, Helen England, from North Tipperary, Teresa Rodmell, from Milton Keynes and Ollie Charles, from London, all asked the same question but got a more sinister response.

An online tool allows users to see exactly what kind of detail Facebook, Google, and Instagram are keeping about the digital online activities of users. Popular dating site Tinder, for example, knows the time, date and number of exchanges you have online. The findings show that apps can even ignore'Do not track' requests from mobile devices, and these include Netflix and online dating apps Hinge and Happn. A new site allows users to see exactly what kind of detail social media sites like Facebook, Google, and Instagram keep about your online activity. Facebook can tell if its emails to user's email accounts have been opened, and a wealth of information about the status of the device and signal being used to access the app.

Data is the lifeblood of all business. Data-driven decisions increasingly make the difference between keeping up with the competition or falling further behind. Machine learning can be the key to unlocking the value of corporate and client data. And determining decisions that save a company ahead of the competition. We can see that Learn machine Learning is very important for life to a bright career in the tech world.

With Google's dominance in the online search engine market we entered the Age of Free. Indeed, services offered online are nowadays expected to be offered at no cost. Which, of course, does not mean that there is no cost to it, only that the consumer doesn't pay it. Early attempts financed the services with ads, but we soon saw a move toward making the consumer the product. Today, free and unfree services alike compete for "users" and then make money off the data they collect.

This is what many people fear when they think about artificial intelligence. But AI technology is often misunderstood, and the many benefits of machine intelligence are easy to overlook. The dystopian vision of AI as an omniscient superintelligence is nothing like the technology we see and use today. Contemporary AI is actually a cluster of related technologies--machine learning, supervised learning, and computer vision, for example--that allow companies to automate tasks at large scale. "There are a lot of real risks to AI," said CNET senior editor Stephen Shankland, like "job displacement, more advanced weapons, and new ways for humans to be bad to other humans."

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real-world scenarios. Distinct from the collaborative similarity that implies co-interact patterns from the user perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research. In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system. We find that both the relation type and the relation value are crucial in inferring user preference. To this end, we develop a two-level hierarchical attention mechanism to model user preference. The first-level attention discriminates which types of relations are more important, and the second-level attention considers the specific relation values to estimate the contribution of a historical item in recommending the target item. To make the item embeddings be reflective of the relational structure between items, we further formulate a task to preserve the item relations, and jointly train it with the recommendation task of preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF. Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by the modeling of multiple item relations.

Email has remained a principal form of communication among people, both in enterprise and social settings. With a deluge of emails crowding our mailboxes daily, there is a dire need of smart email systems that can recover important emails and make personalized recommendations. In this work, we study the problem of predicting user triage actions to incoming emails where we take the reply prediction as a working example. Different from existing methods, we formulate the triage action prediction as a recommendation problem and focus on the content-based approach, where the users are represented using the content of current and past emails. We also introduce additional similarity features to further explore the affinities between users and emails. Experiments on the publicly available Avocado email collection demonstrate the advantages of our proposed recommendation framework and our method is able to achieve better performance compared to the state-of-the-art deep recommendation methods. More importantly, we provide valuable insight into the effectiveness of different textual and user representations and show that traditional bag-of-words approaches, with the help from the similarity features, compete favorably with the more advanced neural embedding methods.

Mobile context determination is an important step for many context aware services such as location-based services, enterprise policy enforcement, building or room occupancy detection for power or HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the user is in "Location X") are defined based on mobile context, it is paramount to verify the accuracy of the mobile context. To this end, two stochastic models based on the theory of Hidden Markov Models (HMMs) to obtain mobile context are proposed-personalized model (HPContext) and collaborative filtering model (HCFContext). The former predicts the current context using sequential history of the user's past context observations, the latter enhances HPContext with collaborative filtering features, which enables it to predict the current context of the primary user based on the context observations of users related to the primary user, e.g., same team colleagues in company, gym friends, family members, etc. Each of the proposed models can also be used to enhance or complement the context obtained from sensors. Furthermore, since privacy is a concern in collaborative filtering, a privacy-preserving method is proposed to derive HCFContext model parameters based on the concepts of homomorphic encryption. Finally, these models are thoroughly validated on a real-life dataset.

From suggesting the meaning of life is'chocolate' to stating that having a boyfriend just ends in heartbreak and loneliness, these conversations show just how sassy Apple's Siri can be. A hilarious gallery, collated by Bored Panda, shows the amusing automated responses owners of Apple gadgets have received when trying to communicate with their virtual assistant. Many people from around the world have contributed conversations in which Siri struggled to understand what they were trying to say. One person was asked by their virtual assistant if they have'anything better to do' after they sent it a tongue-twister. Another was informed by Siri that a reminder was set to tell them they're dumb.