When my 87-year-old mother, Patricia Collinson, was given an Alexa speaker by my sister, she was delighted to find she could ask it to say the Hail Mary. Every morning for a week the devout Catholic asked Alexa to recite the prayer. What she was less delighted to learn was that she had unwittingly ordered a premium subscription payable through Amazon to a private company called Catholic Prayers. Patricia, a retired district nurse in Hastings, does not own a computer, and does not know how to use one. She had signed up by voice command, without being presented with the kind of outline or terms and conditions that now comes as standard when you pay for things online.

If you've been waiting to upgrade your home with the latest gear, this weekend might be the time to do so. From robot vacuums to Instant Pots, there are a number of great sales for connected appliances and kitchen gadgets for Memorial Day this year. As you can imagine, there are quite a lot of them, so we've collected some of the best ones below. Anker's Eufy RoboVac 11S is one of our favorite budget robot vacuums thanks to its slim profile, smart features and affordable price. It doesn't have WiFi, but it does have a remote control.

Contentgine, the world leader in content-based marketing, today released its latest "Top 5" research ranking the most popular artificial intelligence (AI) content consumed by B2B decision makers and analyzed by its Content Indication Platform (CIP). To determine the category leaders, Contentgine's CIP employed machine learning and AI to examine content consumption across more than 3000 AI case studies, research papers, and eBooks syndicated from the world's largest B2B library. "AI software is not only a category in and of itself, but it is also a core component of other categories," said "Top 5 in 15" Series Host Robert Rose, best-selling author and chief strategy advisor for the Content Marketing Institute. "We're talking about the core component of AI software that may or may not be embedded into other solutions to achieve advanced automation, decision insights, predictive measurement, targeting, personalization, content management, and conversational interfaces. Given the vast interest in this topic today, it's wonderful to see so many well performing assets available to decision makers."

We begin at this time's offers choice with a number of choices for these on the lookout for a brand new laptop computer. First up, we've got the Razer Ebook 13 Laptop computer that's presently receiving a really compelling $310 low cost that interprets to 17 % financial savings. In different phrases, you will get your fingers on a brand new Razer Ebook 13 Laptop computer for simply $1,490. The Razer Ebook 13 Laptop computer comes filled with a really potent Intel Core i7 processor, Intel Iris Xe graphics, a 13.4-inch UHD show able to delivering 60Hz refresh charges, 16GB RAM, and 1TB space for storing. It is available in a fantastic Mercury White presentation, which a white RGB backlit keyboard and assist for Thunderbolt 4 ports.

One of the best places to start when building a smart home ecosystem is smart lights. Not only are they relatively affordable compared to other IoT gadgets, often costing between $10 and $50 a bulb, but they can also completely change the feel of your home. You can go from boring and analogue to colorful and automated within minutes, and there are endless possibilities when it comes to creating funky-colored light scenes, setting schedules and more. But like the rest of the smart home space over the last few years, there are now more players in smart lighting than ever before. We tested out some of the most popular smart lights on the market and found that most of them are quite good, but there are differences in compatibility, color quality and mobile app usability that are worth considering before deciding which system will be right for your home.

Regularization is a popular technique to solve the overfitting problem of machine learning algorithms. Most regularization technique relies on parameter selection of the regularization coefficient. Plug-in method and cross-validation approach are two most common parameter selection approaches for regression methods such as Ridge Regression, Lasso Regression and Kernel Regression. Matrix factorization based recommendation system also has heavy reliance on the regularization technique. In this paper, we prove that such approach of selecting regularization coefficient is invalid, and we provide a theoretically accurate method that outperforms the most widely used approach in both accuracy and fairness metrics.

Whether in the form of Robotic Process Automation, chatbots, or some other type of digital assistants, the presence of intelligent bots is substantially increasing across the data ecosystem … in more ways than one. The diversification of the number of tasks these bots can perform is multiplying, as is the intrinsic complexity of those jobs, which unambiguously benefits knowledge workers worldwide. Whether dynamically engaging in natural language interactions with contact center agents, for example, or issuing and answering queries from a certified knowledge base, intelligent bots are integral for not only automating these data exchanges, but also implementing the ensuing action required to complete workflows. "Over the next one to two years we'll see tens of thousands more knowledge workers deploy digital assistants to reduce complexity, achieve error-free work, help their customers by drastically reducing their'on-hold' times and, most importantly, eliminate the frustration that arises from performing repetitive, manual tasks," presaged Automation Anywhere CTO Prince Kohli. These capabilities, of course, are naturally augmented by coupling intelligent bots with the sundry of Artificial Intelligence manifestations that are more pervasive today than they ever were before.

One of the most common challenges in an e-commerce business to build a well-performing product recommender and categorisation model. A product recommender is used to recommend similar products to users so that total time and money spent on platform per user will be increased. There is also a need to have a model to categorise products correctly since there might be some wrongly categorised products in those platforms especially where most of content is generated by users as in case of classified websites. A product categorisation model is used to catch those products and place them back into their right categories to improve overall user experience on the platform. This article has 2 main parts.

Many projects (both practical and academic) have designed algorithms to match users to content they will enjoy under the assumption that user's preferences and opinions do not change with the content they see. Evidence suggests that individuals' preferences are directly shaped by what content they see -- radicalization, rabbit holes, polarization, and boredom are all example phenomena of preferences affected by content. Polarization in particular can occur even in ecosystems with "mass media," where no personalization takes place, as recently explored in a natural model of preference dynamics by~\citet{hkazla2019geometric} and~\citet{gaitonde2021polarization}. If all users' preferences are drawn towards content they already like, or are repelled from content they already dislike, uniform consumption of media leads to a population of heterogeneous preferences converging towards only two poles. In this work, we explore whether some phenomenon akin to polarization occurs when users receive \emph{personalized} content recommendations. We use a similar model of preference dynamics, where an individual's preferences move towards content the consume and enjoy, and away from content they consume and dislike. We show that standard user reward maximization is an almost trivial goal in such an environment (a large class of simple algorithms will achieve only constant regret). A more interesting objective, then, is to understand under what conditions a recommendation algorithm can ensure stationarity of user's preferences. We show how to design a content recommendations which can achieve approximate stationarity, under mild conditions on the set of available content, when a user's preferences are known, and how one can learn enough about a user's preferences to implement such a strategy even when user preferences are initially unknown.

Robots are not limited to industrial works anymore! Thanks to the integration of artificial intelligence and voice recognition, robots are slowly invading our smart homes embedded with devices like wireless security cameras, Smart TVs, Amazon's Alexa, Amazon Echo, Google Assistant, Philips Hue lightbulbs, Ecobee4, etc. And it is not a secret that machine learning software development is on rise now. A lot of clients are coming to develop personalized ML solutions for their businesses. ABI Research predicts that this integration will grow, and by 2024 that over 79 million homes in the world will have a robot in the house.