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.

It's no secret that artificial intelligence (AI)-enabled devices are listening in or observing what we're doing, collecting and digitising massive amounts of data. What underpins this entire enterprise is the work under the hood – maintaining data lakes and warehouses that store the data, performing data engineering tasks to establish and enhance the structure, using business intelligence and statistical analysis to make sense of it and, finally, training an AI program to make predictions that yield more "intelligent" decisions. For example, many of us are familiar with virtual assistant technologies that take in information, digitise the data, put it into data pipelines, and analyse it so the AI algorithms become better in near real-time, fine-tuning it specifically to one's world. What makes an AI algorithm potent is when it can start making connections with other data sets and data points. This results in the creation of a profile that encompasses your shopping behaviour, what you're doing at home, what music you like listening to, what you like eating, and even where you live – in suburbia or in the city.

The harmful impacts of algorithmic decision systems have recently come into focus, with many examples of systems such as machine learning (ML) models amplifying existing societal biases. Most metrics attempting to quantify disparities resulting from ML algorithms focus on differences between groups, dividing users based on demographic identities and comparing model performance or overall outcomes between these groups. However, in industry settings, such information is often not available, and inferring these characteristics carries its own risks and biases. Moreover, typical metrics that focus on a single classifier's output ignore the complex network of systems that produce outcomes in real-world settings. In this paper, we evaluate a set of metrics originating from economics, distributional inequality metrics, and their ability to measure disparities in content exposure in a production recommendation system, the Twitter algorithmic timeline. We define desirable criteria for metrics to be used in an operational setting, specifically by ML practitioners. We characterize different types of engagement with content on Twitter using these metrics, and use these results to evaluate the metrics with respect to the desired criteria. We show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users. Overall, we conclude that these metrics can be useful tools for understanding disparate outcomes in online social networks.

In real-world recommender systems and search engines, optimizing ranking decisions to present a ranked list of relevant items is critical. Off-policy evaluation (OPE) for ranking policies is thus gaining a growing interest because it enables performance estimation of new ranking policies using only logged data. Although OPE in contextual bandits has been studied extensively, its naive application to the ranking setting faces a critical variance issue due to the huge item space. To tackle this problem, previous studies introduce some assumptions on user behavior to make the combinatorial item space tractable. However, an unrealistic assumption may, in turn, cause serious bias. Therefore, appropriately controlling the bias-variance tradeoff by imposing a reasonable assumption is the key for success in OPE of ranking policies. To achieve a well-balanced bias-variance tradeoff, we propose the Cascade Doubly Robust estimator building on the cascade assumption, which assumes that a user interacts with items sequentially from the top position in a ranking. We show that the proposed estimator is unbiased in more cases compared to existing estimators that make stronger assumptions. Furthermore, compared to a previous estimator based on the same cascade assumption, the proposed estimator reduces the variance by leveraging a control variate. Comprehensive experiments on both synthetic and real-world data demonstrate that our estimator leads to more accurate OPE than existing estimators in a variety of settings.

Data science enables businesses to process huge amounts of structured and unstructured big data to detect patterns. This in turn allows companies to increase efficiencies, manage costs, identify new market opportunities, and boost their market advantage. Asking a personal assistant like Alexa or Siri for a recommendation demands data science. So does operating a self-driving car, using a search engine that provides useful results, or talking to a chatbot for customer service. These are all real-life applications for data science.

NEARLY 40 AND SINGLE, Tracy Morris had been online dating for a while, but she wasn't about to settle. Not for the man who turned out to have a whiny, mewling voice; its weak quality made her feel unsafe. Nor for the one who spoke in short, chopped, angry sentences. "Voice is a pheromone to me," said Ms. Morris, a McLean, Virginia-based interior designer. "I think it's one of the senses."