If you're looking to pick up a TV ahead of the Super Bowl, Samsung is currently discounting many of its QLED TVs, matching their lowest prices ever. Normally, the 75-inch model of the Samsung QN85A QLED TV costs $2,999.99 but is currently on sale at Samsung and Best Buy for $1,999.99. This massive, slim-bezel display features amazing visual fidelity and also includes a variety of other handy features. The Tizen OS grants access to most major streaming services and a number of helpful apps, and the TV features built-in support for Google Assistant and Amazon Alexa, allowing you to control playback or find your favorite show with ease. The already excellent picture quality is enhanced even further thanks to HDR10 support, a 120Hz refresh rate, and FreeSync compatibility for superior gaming performance.

Interactive recommender systems (RSs) allow users to express intent, preferences and contexts in a rich fashion, often using natural language. One challenge in using such feedback is inferring a user's semantic intent from the open-ended terms used to describe an item, and using it to refine recommendation results. Leveraging concept activation vectors (CAVs) [21], we develop a framework to learn a representation that captures the semantics of such attributes and connects them to user preferences and behaviors in RSs. A novel feature of our approach is its ability to distinguish objective and subjective attributes and associate different senses with different users. Using synthetic and real-world datasets, we show that our CAV representation accurately interprets users' subjective semantics, and can improve recommendations via interactive critiquing

Artificial intelligence has come a long way from being just a component of science function stories to reality. Currently, we have a host of intelligent machines like self-driving cars, smart virtual assistants, chatbots, and surgical robots, to name a few. Artificial intelligence has now become more mainstream with the ongoing industrial revolution. As our society is growing more dependent on technology, top technologies like AI and machine learning have been augmenting human capabilities and disrupting decades of old and traditional lifestyles. With the emergence of algorithm-driven artificial intelligence, the usefulness of AI continues to grow.

Under a new contract awarded by the Defense Advanced Research Projects Agency, Northrop Grumman will develop a prototype artificial intelligence assistant. The contract is part of DARPA's Perceptually-enabled Task Guidance program. The prototype will be embedded in an augmented reality (AR) headset to help rotary pilots perform expected and unexpected tasks. Northrop Grumman, in partnership with the University of Central Florida, will develop an Operator and Context Adaptive Reasoning Intuitive Assistant that will support UH-60 Blackhawk pilots, who fly with both visual and instrumented flight, which varies with weather, time of day, and other environmental factors. "The goal of this prototype is to broaden a pilot's skillset," said Erin Cherry, senior autonomy program manager, Northrop Grumman.

Click-through rate prediction is a critical task in online advertising. Currently, many existing methods attempt to extract user potential interests from historical click behavior sequences. However, it is difficult to handle sparse user behaviors or broaden interest exploration. Recently, some researchers incorporate the item-item co-occurrence graph as an auxiliary. Due to the elusiveness of user interests, those works still fail to determine the real motivation of user click behaviors. Besides, those works are more biased towards popular or similar commodities. They lack an effective mechanism to break the diversity restrictions. In this paper, we point out two special properties of triangles in the item-item graphs for recommendation systems: Intra-triangle homophily and Inter-triangle heterophiy. Based on this, we propose a novel and effective framework named Triangle Graph Interest Network (TGIN). For each clicked item in user behavior sequences, we introduce the triangles in its neighborhood of the item-item graphs as a supplement. TGIN regards these triangles as the basic units of user interests, which provide the clues to capture the real motivation for a user clicking an item. We characterize every click behavior by aggregating the information of several interest units to alleviate the elusive motivation problem. The attention mechanism determines users' preference for different interest units. By selecting diverse and relative triangles, TGIN brings in novel and serendipitous items to expand exploration opportunities of user interests. Then, we aggregate the multi-level interests of historical behavior sequences to improve CTR prediction. Extensive experiments on both public and industrial datasets clearly verify the effectiveness of our framework.

Purchases you make through our links may earn us a commission. Amazon's Echo smart speakers and displays can listen for more than just your wake word--devices like the Echo Dot and Echo Show 5 can also keep an ear out for everyday household noises like barking dogs, appliance beeps, and even your snoring spouse (to name a few). The idea is to help turn your home's everyday sounds into triggers for your smart devices to make your life a little easier. Get deals and shopping advice delivered straight to your phone. Sign up for text message alerts from the experts at Reviewed.

Smart lights bring real convenience, enabling you to turn them off and on from your phone or with your voice. You can transform the atmosphere from bright to cosy in seconds, or turn on the house lighting from the other side of the world. There are smart light bulbs, which are the easiest to install as they simply replace the existing bulbs. There are also full-on smart lights which can replace table and floor lamps, for instance. If you have other smart gadgets then they can work together, so that on a voice command like "film night" you can dim lights, close the blinds and turn on the TV.

If you have ever bought a product online and marveled at the inanity and non-applicability of the'related items' that haunt the buying and after-sales process, you already understand that popular and mainstream recommender systems tend to fall short in terms of understanding the relationships between prospective purchases. If you buy a unlikely and infrequent item, such as an oven, recommendations for other ovens are likely to be superfluous, though the worst recommender systems fail to acknowledge this. In the 2000s, for example, TiVO's recommender system created an early controversy in this sector by reassigning the perceived sexuality of a user, who subsequently sought to're-masculinize' his user profile by selecting war movies – a crude approach to algorithm revision. Worse yet, you don't need to actually buy anything at (for instance) Amazon, or actually begin watching a movie whose description you're browsing at any major streaming platform, in order for information-starved recommender algorithms to start merrily down the wrong path; searches, dwells and clicks into the'details' pages are enough, and this scant (and probably incorrect) information is likely to be perpetuated across future browsing sessions at the platform. Sometimes it's possible to intervene: Netflix provides a'thumbs up/down' system which should in theory help its machine learning algorithms remove certain embedded concepts and words from your recommendations profile (though its efficacy has been questioned, and it remains much easier to evolve a personalized recommender algorithm from scratch than it is to remove undesired ontologies), while Amazon lets you remove titles from your customer history, which should downgrade any unwelcome domains that infiltrated your recommendations.

Since customer-centric digital strategies are now the norm for successful brands, the current focus should be on ways to use new tools and tech to differentiate your brand experience from the competition. This is not so different from how brick-and-mortar shops operate: Customers walk in and are immersed in specific branding techniques, marketing strategies, and options for connection and personalized interaction. This is a tried-and-true formula for in-person shopping, so why shouldn't it be translated to digital storefronts as well? Let's look at some of the ways that brands can leverage emerging tech to create a powerful, profitable personalization experience. There are endless statistics about how important it is for consumers to feel like they are getting an individualized experience.

Artificial intelligence stocks are rarer than you might think. Many companies tout AI technology initiatives and machine learning. But there really are few -- if any -- public, pure-play AI stocks. In general, look for companies using AI technology to improve products or gain a strategic edge. Venture capitalist Marc Andreessen once observed how "software is eating the world" by remaking industries through automation.