The'world's first' AI-powered hearing aid connects to your smartphone and can translate speech into 27 different languages, say its creators. Experts claim to have mastered near-real time translation of Arabic, Japanese and French, among others, by listening for the foreign language and relaying it to the phone. The device is akin to the fictitious alien Babel fish that performs instant translations in comedy science fiction series The Hitchhiker's Guide to the Galaxy. Creators Starkeys claim the technology reduces noisy environments by 50 per cent, while artificial intelligence optimises the hearing experience and its translation is compatible to Google Translate in terms of accuracy. The Livio AI, which is now on sale in the UK and costs £3,000 ($3,900), also has brain tracking technology and Alexa connectivity, and interfaces with the mobile app, Thrive Hearing.

If Apple's strategy this week is to build excitement for next week's "Show time" event, then it's doing a bang-up job. Following Monday's iPads and Tuesday's iMacs, Apple updated another popular product today with the launch of the second-generation AirPods that bring a new chip, new features, a new case, and more convenience. The new AirPods look exactly like the first-generation model, but include several key enhancements. The biggest is the addition of a new H1 chip, which Apple says was "developed specifically for headphones, delivers performance efficiencies, faster connect times, more talk time and the convenience of hands-free'Hey Siri.'" That means you no longer need to tap your ear to get Siri's attention.

Recall assistance methods are among the key aspects that improve the accuracy of online dietary assessment surveys. These methods still mainly rely on experience of trained interviewers with nutritional background, but data driven approaches could improve cost-efficiency and scalability of automated dietary assessment. We evaluated the effectiveness of a recommender algorithm developed for an online dietary assessment system called Intake24, that automates the multiple-pass 24-hour recall method. The recommender builds a model of eating behavior from recalls collected in past surveys. Based on foods they have already selected, the model is used to remind respondents of associated foods that they may have omitted to report. The performance of prompts generated by the model was compared to that of prompts hand-coded by nutritionists in two dietary studies. The results of our studies demonstrate that the recommender system is able to capture a higher number of foods omitted by respondents of online dietary surveys than prompts hand-coded by nutritionists. However, the considerably lower precision of generated prompts indicates an opportunity for further improvement of the system.

Tinder will no longer rely on a controversial method of sorting users based on an internal'desirability' score, according to a recent announcement from the company. Instead, the popular dating app's new algorithm will prioritize its patrons based on one overarching factor: volume of usage. The hidden ranking system that Tinder previously relied on assigned scores to users based on how many people have liked their profile. Tinder will no longer rely on a controversial method of sorting users based on an internal'desirability' score, according to a recent announcement from the company. According to the company, Tinder has matched 30 billion users around the world.

If you don't have hundreds of dollars to spend on new bulbs, hubs, and thermostats, you can still make your home a whole lot smarter. And today's a great time to start: B&H Photo is selling the TP-Link HS300 smart Wi-Fi power strip for $55Remove non-product link when applying the 31 percent coupon on the listing,, along with a three-pack of TP-Link's HS200 smart Wi-Fi switches for $57Remove non-product link, nearly 50 percent off its list price of $105. The smart Wi-Fi power strip allows you to plug in up to six devices at once, and control them individually with their individual switches or by using the connected app. There are also three handy USB ports for charging up extra devices. Using TP-Link's Kasa app, you can set schedules and turn devices on and off remotely, as well as monitor energy usage.

Your message has been sent. There was an error emailing this page. Welcome to the Google Home shadow market, a symbiotic ecosystem of third-party products that add a bit more versatility to the Google Home Mini and original Google Home smart speaker. Some grant true portability to Google's otherwise-tethered speakers. Others help you place the speakers in a more convenient position.

Interactive recommendation that models the explicit interactions between users and the recommender system has attracted a lot of research attentions in recent years. Most previous interactive recommendation systems only focus on optimizing recommendation accuracy while overlooking other important aspects of recommendation quality, such as the diversity of recommendation results. In this paper, we propose a novel recommendation model, named \underline{D}iversity-promoting \underline{D}eep \underline{R}einforcement \underline{L}earning (D$^2$RL), which encourages the diversity of recommendation results in interaction recommendations. More specifically, we adopt a Determinantal Point Process (DPP) model to generate diverse, while relevant item recommendations. A personalized DPP kernel matrix is maintained for each user, which is constructed from two parts: a fixed similarity matrix capturing item-item similarity, and the relevance of items dynamically learnt through an actor-critic reinforcement learning framework. We performed extensive offline experiments as well as simulated online experiments with real world datasets to demonstrate the effectiveness of the proposed model.

Collaborative filtering (CF) is a core technique for recommender systems. Traditional CF approaches exploit user-item relations (e.g., clicks, likes, and views) only and hence they suffer from the data sparsity issue. Items are usually associated with unstructured text such as article abstracts and product reviews. We develop a Personalized Neural Embedding (PNE) framework to exploit both interactions and words seamlessly. We learn such embeddings of users, items, and words jointly, and predict user preferences on items based on these learned representations. PNE estimates the probability that a user will like an item by two terms---behavior factors and semantic factors. On two real-world datasets, PNE shows better performance than four state-of-the-art baselines in terms of three metrics. We also show that PNE learns meaningful word embeddings by visualization.

To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information. In general, the attributes are not isolated but connected with each other, which forms a knowledge graph (KG). In this paper, we propose Knowledge Graph Convolutional Networks (KGCN), an end-to-end framework that captures inter-item relatedness effectively by mining their associated attributes on the KG. To automatically discover both high-order structure information and semantic information of the KG, we sample from the neighbors for each entity in the KG as their receptive field, then combine neighborhood information with bias when calculating the representation of a given entity. The receptive field can be extended to multiple hops away to model high-order proximity information and capture users' potential long-distance interests. Moreover, we implement the proposed KGCN in a minibatch fashion, which enables our model to operate on large datasets and KGs. We apply the proposed model to three datasets about movie, book, and music recommendation, and experiment results demonstrate that our approach outperforms strong recommender baselines.

Artificial Intelligence (AI) is currently the hottest buzzword in tech. And with good reason--the last few years have seen a number of techniques that have previously been in the realm of science fiction slowly transform into reality. Experts look at artificial intelligence as a factor of production which has the potential to introduce new sources of growth and change the way work is done across industries. According to the report How AI Boosts Industry Profits and Innovations, AI is predicted to increase economic growth by an average of 1.7 percent across 16 industries by 2035. The report goes on to say that, by 2035, AI technologies could increase labor productivity by 40 percent or more, there by doubling economic growth in 12 developed nations that continue to draw talented and experienced professionals to work in this domain.