Nowadays, people prefer using smart and interactive apps instead of basic ones. With continually evolving technologies, it is essential always to stay up-to-date. This means that the apps, games, and gadgets must change and become more dynamic. Are you wondering how all these interactive apps of the future are created? Would you like to know how come the Google Assistant understands what you're saying and can even help you with using your phone more proficiently?

BEGIN ARTICLE PREVIEW: Today we are taking a closer look at the Roborock S6 Pure robotic vacuum and mop, now available in black for $359.99 (Reg. $599) for 9to5Mac readers. Designed to offer users a completely customizable cleaning solution, the S6 Pure mops and sweeps on your command via the companion iOS app or just by yelling at Siri. Making use of a wide array of intelligent sensors and a LiDAR navigation system, the Roborock S6 Pure offers up a more intelligent cleaning system than your average robot vac on Amazon. Head below for more details on the customized cleaning system, expanded runtime, and today’s solid Black Friday price drop for 9to5readers.  Roborock S6 robotic vacuum and mop Roborock has employed advanced navigation and a series of smart sensors for an efficient and customizable cleaning experience. Making use of a LiDAR laser navigation system, the Roborock S6 Pure intelligently maps out each room in your home (even on multiple floors),

Do you know very much about Artificial Intelligence (AI) marketing? Well lucky for you, you have come to the right place. By the end of this article, you'll learn how AI works and how you can apply it to your marketing strategies to grow your home builder business. For starters, AI is everywhere and not just the villain in those thrilling sci-fi movies you watch with your family and friends. AI is your Netflix or Amazon suggestions.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A Florida woman was arrested for allegedly setting up a dating profile advertising "free meth tonight" that sent suitors to her romantic rival's home looking for sex, police said. Vanessa Marie Huckaba, 29, created the "Islandbabe1234" profile on the website Seeking Arrangement and included the name, photo, cellphone number and address of a woman who was dating her ex-boyfriend, according to an arrest report obtained by The Miami Herald. FLORIDA WOMAN CHARGED IN MACHETE ATTACK WANTED TO BE WITH VICTIM'S WIFE: DEPUTIES "Multiple strangers began arriving at the victim's residence thereafter," said Adam Linhardt, a spokesman for the Monroe County Sheriff's Office.

Machine Learning (ML) is a well-known innovation that nearly everyone knows about. A study uncovers that 77% of devices that we presently use are utilizing ML. From a social event of SMART devices over Netflix proposition through products like Amazon's Alexa, and Google Home, artificial intelligence services are proclaiming cutting-edge innovative solutions for organizations and regular day to day existences. The year 2021 is ready to observe some significant ML and AI trends that would maybe reshape our economic, social, and industrial workings. As of now, the AI-ML industry is developing at a quick rate and gives sufficient advancement scope to companies to bring the vital change. According to Gartner, around 37% of all companies reviewed are utilizing some type of ML in their business and it is anticipated that around 80% of modern advances will be founded on AI and ML by 2022.

Applications like recommendation engines only need to be accurate enough to be somewhat useful. After all, no one is going to lose their life if Netflix recommends the wrong movie or Siri misunderstands a word. It's a completely different matter for computer vision applications that are trained to assist in critical business functions and areas like healthcare or self-driving cars. For these applications, data quality and accuracy are critical.

Apple's new, cheaper HomePod is a tough smart speaker to nail down. On the one hand, the HomePod Mini boasts impressive audio quality for its size. The HomePod Mini also has a Thread radio that lets it act as a smart home hub, but for now, there are only a few Thread-enabled smart devices available to control. And while Apple's new Intercom feature makes for an easy way to broadcast messages to household members, it doesn't allow for two-way calling. Now, if you're a dedicated Apple user and you've been waiting for a more affordable Siri-powered smart speaker than the $300 HomePod, the $99 HomePod Mini is your best--and only--bet.

Data is the new game-changer, everywhere. According to reports, data-driven organizations are 19 times more likely to be profitable. Data and analytics are critical components of digital transformation. Considering the rate at which data is being generated, its analysis is becoming a hefty task. Organizing large volumes of real-time data from several sources is time-consuming and tedious. To reduce the human effort involved in this and decrease the required time, AI and ML are being employed.

Recently, the reciprocal recommendation, especially for online dating applications, has attracted more and more research attention. Different from conventional recommendation problems, the reciprocal recommendation aims to simultaneously best match users' mutual preferences. Intuitively, the mutual preferences might be affected by a few key attributes that users like or dislike. Meanwhile, the interactions between users' attributes and their key attributes are also important for key attributes selection. Motivated by these observations, in this paper we propose a novel reinforced random convolutional network (RRCN) approach for the reciprocal recommendation task. In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation. Moreover, we design a reinforcement learning based strategy to integrate with the random CNN component to select salient attributes to form the candidate set of key attributes. We evaluate the proposed RRCN against a number of both baselines and the state-of-the-art approaches on two real-world datasets, and the promising results have demonstrated the superiority of RRCN against the compared approaches in terms of a number of evaluation criteria.

Mitigating the new user cold-start problem has been critical in the recommendation system for online service providers to influence user experience in decision making which can ultimately affect the intention of users to use a particular service. Previous studies leveraged various side information from users and items; however, it may be impractical due to privacy concerns. In this paper, we present ColdGAN, an end-to-end GAN based model with no use of side information to resolve this problem. The main idea of the proposed model is to train a network that learns the rating distributions of experienced users given their cold-start distributions. We further design a time-based function to restore the preferences of users to cold-start states. With extensive experiments on two real-world datasets, the results show that our proposed method achieves significantly improved performance compared with the state-of-the-art recommenders.