In this paper, we introduce an approach for leveraging available data across multiple locales sharing the same language to 1) improve domain classification model accuracy in Spoken Language Understanding and user experience even if new locales do not have sufficient data and 2) reduce the cost of scaling the domain classifier to a large number of locales. We propose a locale-agnostic universal domain classification model based on selective multi-task learning that learns a joint representation of an utterance over locales with different sets of domains and allows locales to share knowledge selectively depending on the domains. The experimental results demonstrate the effectiveness of our approach on domain classification task in the scenario of multiple locales with imbalanced data and disparate domain sets. The proposed approach outperforms other baselines models especially when classifying locale-specific domains and also low-resourced domains.

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e.g., matrix factorization) assume a linear relationship between the user and item. This approach limits the capacity of recommender systems, since the interactions between users and items in real-world applications are much more complex than the linear relationship. To overcome this limitation, in this paper, we design and propose a deep learning framework called Signed Distance-based Deep Memory Recommender, which captures non-linear relationships between users and items explicitly and implicitly, and work well in both general recommendation task and shopping basket-based recommendation task. Through an extensive empirical study on six real-world datasets in the two recommendation tasks, our proposed approach achieved significant improvement over ten state-of-the-art recommendation models.

The past decade has seen artificial intelligence develop from a mere fantasy to a fully integrated part of a marketing strategy, for brands that look to differentiate and improve their customer experiences and online strategies. Take Farfetch for example, which utilized RFID-enabled clothing racks and digital mirrors to allow its customers the choice of size and colour before directly checking out online. This particular use of AI shows the seamless integration of online and offline experiences, and proves that this technology has no end to the benefits and creativity it can bring for a brands engagement efforts. Found at the core of AI technology is data and analytics, allowing brands to streamline digital ads and offer a personalized customer service. This can result in a significant lift to brands engagement efforts and empowers them to fully engage with customer at every stage of the purchase lifecycle.

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."