Google's AI assistant can now understand people even if they repeatedly switch between two languages during a conversation. This marks the first time an AI-powered voice assistant has been able to distinguish between two different languages during the same interaction. Google Assistant can now understand any pair of languages from English, German, French, Spanish, Italian, and Japanese. More languages are planned for a future update, Google says. Google hopes the upgrade will make using its Assistant easier in bilingual households, and says it could help people who are trying to learn a new language.

Artificially intelligent systems map our journeys, unlock our homes, feed us entertainment, and foretell the weather. But could our electronic assistants also start to learn our emotions and use that knowledge to serve us better? In other words, does Alexa know when you get mad? Close up of a boy's face aged 8 years wearing Clown make up face paint with rainbow markings on his arm. In fact, Amazon teams have been working on analyzing your emotions from your vocal intonations for over a year.

When Samsung launched the Galaxy Home speaker earlier this month, people were quick to point out how its name seemed ripped off from Google. Not to be outdone, Huawei is unveiling its own AI speaker here at IFA 2018, and it's clearly borrowed much more from the Google Home... just not the name. The AI Cube is a cylindrical speaker that looks like a stretched out version of Google's device, though it will offer Amazon's Alexa instead of Assistant. Like Samsung, Huawei is promising high-quality audio on its speaker. That's not all -- the AI Cube is also a 4G router.

Artificial intelligence is already everywhere. The technology is widely used in ways that are quite obvious, such as self-driving cars, and others that are inconspicuous. It keeps your mobile phone ticking over, translates for Alexa, helps doctors analyse medical images, controls robotics in factories, and so much more, quietly working behind the scenes to automate both simple and complicated tasks. Though these are small examples of its capabilities, AI is predicted to have a huge impact on our lives, with plenty predicting disruption to our jobs and work life and others seeing the benefits of churning through vast data sets. Keeping up with such changes requires understanding the various technologies behind AI, be it neural networks, deep learning and machine learning, and seeing how they're already being used.

Our unique multi-level hierarchy gives the James platform the ability to handle thousands of intents. Our ensemble of prediction models can interpret user intents with as little as 10 training messages - a feature unlikely to be found in other solutions.

Dating in 2018 can be a challenge. I'm sorry, let me rephrase: It suuuuuuuuccckkkkksssss. Apps like Tinder, Bumble, Hinge, Grindr, and others are the dater's tools of choice, and yet hating them is the one thing we can all agree on these days. They're often more hazard than help, and the forced psychoanalysis of every picture and witty answer can shake even the most durable of confidences loose. Why am I not getting more matches? But is it your fault, or the app's?

Item-item collaborative filtering (CF) models are a well known and studied family of recommender systems, however current literature does not provide any theoretical explanation of the conditions under which item-based recommendations will succeed or fail. We investigate the existence of an ideal item-based CF method able to make perfect recommendations. This CF model is formalized as an eigenvalue problem, where estimated ratings are equivalent to the true (unknown) ratings multiplied by a user-specific eigenvalue of the similarity matrix. Preliminary experiments show that the magnitude of the eigenvalue is proportional to the accuracy of recommendations for that user and therefore it can provide reliable measure of confidence.

Following recent successes in exploiting both latent factor and word embedding models in recommendation, we propose a novel Regularized Multi-Embedding (RME) based recommendation model that simultaneously encapsulates the following ideas via decomposition: (1) which items a user likes, (2) which two users co-like the same items, (3) which two items users often co-liked, and (4) which two items users often co-disliked. In experimental validation, the RME outperforms competing state-of-the-art models in both explicit and implicit feedback datasets, significantly improving Recall@5 by 5.9~7.0%, NDCG@20 by 4.3~5.6%, and MAP@10 by 7.9~8.9%. In addition, under the cold-start scenario for users with the lowest number of interactions, against the competing models, the RME outperforms NDCG@5 by 20.2% and 29.4% in MovieLens-10M and MovieLens-20M datasets, respectively. Our datasets and source code are available at: https://github.com/thanhdtran/RME.git.

Cold-start is a very common and still open problem in the Recommender Systems literature. Since cold start items do not have any interaction, collaborative algorithms are not applicable. One of the main strategies is to use pure or hybrid content-based approaches, which usually yield to lower recommendation quality than collaborative ones. Some techniques to optimize performance of this type of approaches have been studied in recent past. One of them is called feature weighting, which assigns to every feature a real value, called weight, that estimates its importance. Statistical techniques for feature weighting commonly used in Information Retrieval, like TF-IDF, have been adapted for Recommender Systems, but they often do not provide sufficient quality improvements. More recent approaches, FBSM and LFW, estimate weights by leveraging collaborative information via machine learning, in order to learn the importance of a feature based on other users opinions. This type of models have shown promising results compared to classic statistical analyzes cited previously. We propose a novel graph, feature-based machine learning model to face the cold-start item scenario, learning the relevance of features from probabilities of item-based collaborative filtering algorithms.

Today, Google rolled out a new ability for the Google Assistant. The helpful, disembodied entity that lives inside smartphones and Google Home devices can now interpret two languages at the same time, including French, German, Japanese, Spanish, Italian and English. But, how will users know about it? It's a question I recently encountered in my own personal experience. A few weeks ago, the familiar command that turns my Philips Hue lights on and off stopped working."OK, Google, turn off the light in the living room."