Artificial Intelligence has been showing its presence in almost every field from sports to weather. But something that has long been coming and expected is the implementation of AI in news. News media has always been a target for Artificial Intelligence. And especially that is something that is needed more than ever before. Today, news organisations, let alone the regular person determine which news is trustworthy and which is not.

You've just noticed a great price on an Amazon Echo Dot or Google Home Mini smart speaker. The prices are intended to make them impulse purchases, because both Amazon and Google are desperate to get their foot in the door of your home. Voice assistants and smart home automation are expected to be the next big thing in tech, and both companies know that once you experience the convenience of a smart speaker in one room, you're likely to want it in others. Before making a purchase, you really should do your homework. It all comes down to the hardware, the capabilities of their digital assistants, and the way they can do things such as play music and control other smart devices in your home.

What do machine learning practitioners actually do? Written: 12 Jul 2018 by Rachel Thomas This post is part 1 of a series. Part 2 will explain AutoML and neural architecture search, and Part 3 will look at Google's AutoML in particular. There are frequent media headlines about both the scarcity of machine learning talent (see here, here, and here) and about the promises of companies claiming their products automate machine learning and eliminate the need for ML expertise altogether (see here, here, and here). In his keynote at the TensorFlow DevSummit, Google's head of AI Jeff Dean estimated that there are tens of millions of organizations that have electronic data that could be used for machine learning but lack the necessary expertise and skills.

If the internet was the major change for humanity following the agricultural and industrial revolutions, the application of artificial intelligence is perhaps the next transformation. Last week's headline, "Robots could take a million local jobs by 2038", may sound incredible or even be seen as scaremongering. But human resources being replaced by artificial intelligence will be an inevitable trend we have to reckon with. It is perhaps time we put more thought into the implications and the corresponding actions needed to help cope with the challenge. If the study by the One Country, Two Systems Research Institute is any reference, more than a quarter of Hong Kong's workforce, including secretaries and accountants, is vulnerable to automation. Jobs that are considered safer include doctors, nurses, teachers, architects and journalists.

If the internet was the major change for humanity following the agricultural and industrial revolutions, the application of artificial intelligence is perhaps the next transformation. Last week's headline, "Robots could take a million local jobs by 2038", may sound incredible or even be seen as scaremongering. But human resources being replaced by artificial intelligence will be an inevitable trend we have to reckon with. It is perhaps time we put more thought into the implications and the corresponding actions needed to help cope with the challenge. If the study by the One Country, Two Systems Research Institute is any reference, more than a quarter of Hong Kong's workforce, including secretaries and accountants, is vulnerable to automation. Jobs that are considered safer include doctors, nurses, teachers, architects and journalists.

Recommendation systems are an integral part of Artificial Intelligence (AI) and have become increasingly important in the growing age of commercialization in AI. Deep learning (DL) techniques for recommendation systems (RS) provide powerful latent-feature models for effective recommendation but suffer from the major drawback of being non-interpretable. In this paper we describe a framework for explainable temporal recommendations in a DL model. We consider an LSTM based Recurrent Neural Network (RNN) architecture for recommendation and a neighbourhood-based scheme for generating explanations in the model. We demonstrate the effectiveness of our approach through experiments on the Netflix dataset by jointly optimizing for both prediction accuracy and explainability.

Alexa has taken the world by storm as arguably the most comprehensive and full-featured digital assistant out there. Not only is it a great way to interact with your Amazon devices, but you can also find Alexa on a range of third-party devices – including some Sonos speakers, which many consider to be the best multi-room speakers on the market. If you're looking to set up Alexa to use on your Sonos speaker, there are a few things you should know. Like, for example, the fact that Alexa currently only works with the Sonos One and Sonos Beam speakers, though more are sure to come, and you can still use Alexa with other Sonos speakers. In any case, here's a step-by-step guide to set up Alexa for your Sonos One or Sonos Beam speaker.