The primary appeal of motorized top-down/bottom-up shades is their ability to open and close in two directions: They can open by dropping the top of the shade down from the window's head to the sill, and by lifting the bottom of the shade up from the sill to the head. But Hunter Douglas couldn't justify the lofty price tag of its Duette with PowerView Automation shades unless they were also the most luxurious and innovative shades we've reviewed to date. Top-down/bottom-up shades are a fantastic option because they enhance privacy without completely blocking light from entering the room. If your window faces a busy street, you can lower the shade down from the top to admit light without exposing your room to a view from the street. Or you can drop the top of the shade down in the early morning, so the room is bathed in morning sunlight without impeding your ability to move about the room freely--anyone looking toward your window will only be able to as much of you as you wish to expose. And since these are motorized smart shades, you can create automated schedules to reposition the shades as many times each day and night that you'd care to program, including at sunrise and sunset.

Planning to invest in a mobile app? Here are the top 15 AI/ML/VR/AR app development ideas that ensure your success in 2020–21! With the availability of around 5 million apps existing in the app stores, the trends of developing ordinary mobile apps are just fading away. The increasing usage of mobile applications with each passing year also pushes the demand for innovative technologies to meet future mobile app users' demands. And Artificial Intelligence and Machine Learning (AI & ML) have become the most influencing technologies in the field of mobile app development and creating a plethora of opportunities for startups in 2021.

The linear submodular bandit problem was proposed to simultaneously address diversified retrieval and online learning in a recommender system. If there is no uncertainty, this problem is equivalent to a submodular maximization problem under a cardinality constraint. However, in some situations, recommendation lists should satisfy additional constraints such as budget constraints, other than a cardinality constraint. Thus, motivated by diversified retrieval considering budget constraints, we introduce a submodular bandit problem under the intersection of $l$ knapsacks and a $k$-system constraint. Here $k$-system constraints form a very general class of constraints including cardinality constraints and the intersection of $k$ matroid constraints. To solve this problem, we propose a non-greedy algorithm that adaptively focuses on a standard or modified upper-confidence bound. We provide a high-probability upper bound of an approximation regret, where the approximation ratio matches that of a fast offline algorithm. Moreover, we perform experiments under various combinations of constraints using a synthetic and two real-world datasets and demonstrate that our proposed methods outperform the existing baselines.

The recent advances in artificial intelligence namely in machine learning and deep learning, have boosted the performance of intelligent systems in several ways. This gave rise to human expectations, but also created the need for a deeper understanding of how intelligent systems think and decide. The concept of explainability appeared, in the extent of explaining the internal system mechanics in human terms. Recommendation systems are intelligent systems that support human decision making, and as such, they have to be explainable in order to increase user trust and improve the acceptance of recommendations. In this work, we focus on a context-aware recommendation system for energy efficiency and develop a mechanism for explainable and persuasive recommendations, which are personalized to user preferences and habits. The persuasive facts either emphasize on the economical saving prospects (Econ) or on a positive ecological impact (Eco) and explanations provide the reason for recommending an energy saving action. Based on a study conducted using a Telegram bot, different scenarios have been validated with actual data and human feedback. Current results show a total increase of 19\% on the recommendation acceptance ratio when both economical and ecological persuasive facts are employed. This revolutionary approach on recommendation systems, demonstrates how intelligent recommendations can effectively encourage energy saving behavior.

Have you ever used Google Assistant, Apple's Siri, or Amazon Alexa to make decisions for you? Perhaps you asked it what new movies have good reviews, or to recommend a cool restaurant in your neighborhood. Artificial intelligence and virtual assistants are constantly being refined, and may soon be making appointments for you, offering medical advice, or trying to sell you a bottle of wine. Although AI technology has miles to go to develop social skills on par with ours, some AI has shown impressive language understanding and can complete relatively complex interactive tasks. In several 2018 demonstrations, Google's AI made haircut and restaurant reservations without receptionists realizing they were talking with a non-human.

Voice assistants are becoming an essential part of our daily lives. When Apple's Siri hit markets in 2011, it managed to gain an impressive attraction of tech enthusiasts, yet no one was certain about how this novelty shall bring a tech revolution. Today, we are regular users of Google Voice Assistant, Amazon Alexa, and many more. Things took a turn when Google Home, Amazon Echo, and Apple HomePod went mainstream in 2017. All these instances converge on how voice assistants are proving themselves as a tech enabler with impressive possibilities. Not only in households, but they are also slowly proving to be useful in the business quarters too.

Amazon's Echo line is the reigning champ of the smart speaker world. Compared to virtual assistants like Google and Siri, Alexa works with far more gadgets and responds to significantly more commands than the competition. Plus, Echo devices are pretty inexpensive, starting around $25 for the Flex and $30 for the Dot when you catch it on sale. I bought six of these Echo Shows for Christmas presents. They have a nice screen, so all the kids and grandkids can pop in and say hello to my Mom whenever they want.

In today's world, it is nearly impossible to avoid voice-controlled digital assistants. From the interactive intelligent agents used by corporations, government agencies, and even personal devices, automated speech recognition (ASR) systems, combined with machine learning (ML) technology, increasingly are being used as an input modality that allows humans to interact with machines, ostensibly via the most common and simplest way possible: by speaking in a natural, conversational voice. Yet as a study published in May 2020 by researchers from Stanford University indicated, the accuracy level of ASR systems from Google, Facebook, Microsoft, and others vary widely depending on the speaker's race. While this study only focused on the differing accuracy levels for a small sample of African American and white speakers, it points to a larger concern about ASR accuracy and phonological awareness, including the ability to discern and understand accents, tonalities, rhythmic variations, and speech patterns that may differ from the voices used to initially train voice-activated chatbots, virtual assistants, and other voice-enabled systems. The Stanford study, which was published in the journal Proceedings of the National Academy of Sciences, measured the error rates of ASR technology from Amazon, Apple, Google, IBM, and Microsoft, by comparing the system's performance in understanding identical phrases (taken from pre-recorded interviews across two datasets) spoken by 73 black and 42 white speakers, then comparing the average word error rate (WER) for black and white speakers.

Tinder, the most popular dating app in the world, has banned teens under the age of 18 but it's not stopping them from signing up. A Massachusetts man is accused of kidnapping and assaulting a woman he met on Tinder, threatening to kill her and her child if she went to the cops, authorities said. Peter Bozier, 28, was arrested Tuesday during a traffic stop in Sudbury after the victim told investigators she was severely beaten and strangled while being held against her will at Bozier's home, police said. The victim said the harrowing ordeal began a day earlier, police spokesman Lt. Robert Grady told the MetroWest Daily News. Grady said the woman managed to "release herself from the situation" and then went to a hospital in Burlington, where hospital staffers contacted police, the newspaper reported.

AI assistants have started carrying out tasks on a user's behalf by interacting directly with the web. However, training an interface that maps natural language (NL) commands to web actions is challenging for existing semantic parsing approaches due to the variable and unknown set of actions that characterize websites. We propose FLIN, a natural language interface for web navigation that maps NL commands to concept-level actions rather than low-level UI interactions, thus being able to flexibly adapt to different websites and handle their transient nature. We frame this as a ranking problem where, given a user command and a webpage, FLIN learns to score the most appropriate navigation instruction (involving action and parameter values). To train and evaluate FLIN, we collect a dataset using nine popular websites from three different domains. Quantitative results show that FLIN is capable of adapting to new websites in a given domain.