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.

Eric joined Talend as Chief Information Officer in 2018. Prior to Talend, he was the CIO of DocuSign, where he built the company's IT vision, infrastructure design, and led its global organization. Er… (show all) Eric joined Talend as Chief Information Officer in 2018. Prior to Talend, he was the CIO of DocuSign, where he built the company's IT vision, infrastructure design, and led its global organization. Eric spent more than a decade at Informatica in increasingly senior IT roles, most recently as Senior Vice President and CIO.

In the future, personalized AI assistants will use knowledge of our behaviors to inform us of goings-on and do chores, and as we grow older, they'll provide companionship and assistance when caregivers aren't around. These assistants will not only chart our calendars and supply answers to questions, but will help administer medical treatments, assist us in making difficult decisions, and connect us with the people we care deeply about. That's the vision the Computing Community Consortium (CCC), an organization representing over 220 North American academic departments, industrial research labs, and professional societies, articulated in a draft of its 20-year roadmap for AI research in the U.S., which was published this month. Its organizers -- who include University of Southern California director of knowledge technologies Yolanda Gil and Dr. Fei-Fei Li, a Stanford professor and formerly Google's chief AI scientist -- picture personal assistants that will fundamentally transform human lives around the world for the better. "[AI] will enable an elderly population to live longer independently, AI health coaches will provide advice for lifestyle choices, [and] customized AI tutors will broaden education opportunities," according to the report.

Digital marketing is experiencing a transformative shift to automated, personalized marketing and prescriptive, machine learning recommendation engines. Wire Stone is a leading provider of integrating marketing solutions and experiences, maintaining client relationships that span many years and, in some cases, decades. Wire Stone is uniquely qualified to assist marketing leaders and decision makers at every level of the various industries we work with: technology, telecommunications, automotive, aeronautics, sports retail, financial, and others. We're a unique mix of strategists, creatives, programmers, data analysts, engineers and architects--obsessed with the art and science of crafting integrated approaches and optimizing meaningful connections that few agencies can match. Wire Stone's Data Science team provides intelligence products and services to some of the world's largest and most innovative companies.

Voicesense makes an intriguing promise to its clients: give us someone's voice, and we'll tell you what they will do. The Israeli company uses real-time voice analysis during calls to evaluate whether someone is likely to default on a bank loan, buy a more expensive product, or be the best candidate for a job. It's one of a crop of companies looking for the personal insights contained in our speech. In recent years, researchers and startups have taken note of the rich trove of information that can be mined from voice, especially as the popularity of home assistants like Amazon's Alexa make consumers increasingly comfortable talking to their devices. The voice technology market is growing and is expected to reach $15.5 billion by 2029, according to a report by business analytics firm IdTechEx.