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.

The speed of technological change is exponential. What was yesterday's hot ticket quickly becomes tomorrow's old news. We are living in the midst of a huge surge of interest and research in Artificial Intelligence (AI). It seems like every week there is a new breakthrough in the field and a new record is set in some task previously done by humans. If you don't already know what IoT, AI, VR, AR, and bots mean, you better get up to speed immediately because these technologies are changing the way data is created, collected, interpreted, and communicated.

'Labor of Love,' hosted by'Sex and the City' star Kristin Davis, follows a woman as she searches for love and motherhood. Love is blind -- and bald too, thanks to an app catering to singles with hair loss. Bald Dating, a new matchmaking site that launched Monday, is aiming to make it easier for singles with hair loss to find "the one" without focusing on hair follicles. The website is being promoted using the tagline "heads & personalities shine." Now there's a niche dating app aiming to connect bald singles with partners who have an affinity for it.

Smart speakers have become so ubiquitous lately that you most likely have more than one set up at home. Whether you're using smart speakers from Google, Amazon, or Apple, you can send audio to several speakers at once, configure them as stereo pairs, or even get your music to follow you from room to room. Google not long ago rebranded its Home speakers as Nest speakers, so you might have one or more of each--but they'll still work together no matter what the label says. Speaker management is handled through the Google Home app for Android or iOS. Open up the app and you'll see all your Nest speakers listed, together with any other connected smart home devices like Chromecasts and Nest cameras.

You've thought long and hard about it, and you're finally ready to buy a smart speaker. They're all being heavily marketed for Black Friday sales, and you'd like to ask Siri, Alexa or the Google Assistant to play music, tell you the weather, turn off your TV or lights on command. But you're stuck: Which brand and model to buy? Amazon has five Echo speakers currently available, Google has six and Apple has two. Which one is right for you? And then there are the concerns about security and snooping.