Nanoleaf has unveiled the latest light panels in its Shapes series, this time in the form of a triangle. Like the previous hexagon models, you can put them together in different patterns and then use them to mirror your TV, sync to music or control them with Google Assistant, Amazon Alexa and Apple HomeKit. However, the Triangles now come in large and small sizes and use a rigid connector that snaps into place. That allows you mount the panels as needed without having to attach each one to the wall, as before. Each panel lights up with 16 million different color combination and offers touch capabilities, customization and syncing functions.

Udemy Free Discount - Artificial Intelligence Exposed: Future 1.0 Extreme Edition, Learn and explore 100's of secretive tools, technologies and websites covering Artificial Intelligence and beyond NEW, 3.9 (8 ratings), Created by Srinidhi Ranganathan, Saranya Srinidhi, English [Auto-generated] Artificial Intelligence seems to be a unique technology of making a machine, a robot fully autonomous. AI is an analysis of how the machine is thinking, studying, determining and functioning when it is trying to solve problems. These kind of problems are present in all fields, the most emerging ones in 2020 and even beyond. The aim of Artificial Intelligence is to enhance machine functions relating to human knowledge, such as reasoning, learning and problems along with the ability to manipulate things. For example, virtual assistants or chatbots offer expert advice.

Remember Facebook's automated personal assistant, M, that was released in a bid to compete with Alexa and Siri? After a series of embarrassing mishaps due to poorly trained algorithms, Facebook abruptly pulled the plug. They weren't alone; chatbots are infamous for putting their metaphorical feet in their mouths. While these debacles are tough to watch, the underlying problem is not artificial intelligence (AI) itself. AI succeeds when underpinned with sound strategy and well-trained models.

Transfer learning is an effective technique to improve a target recommender system with the knowledge from a source domain. Existing research focuses on the recommendation performance of the target domain while ignores the privacy leakage of the source domain. The transferred knowledge, however, may unintendedly leak private information of the source domain. For example, an attacker can accurately infer user demographics from their historical purchase provided by a source domain data owner. This paper addresses the above privacy-preserving issue by learning a privacy-aware neural representation by improving target performance while protecting source privacy. The key idea is to simulate the attacks during the training for protecting unseen users' privacy in the future, modeled by an adversarial game, so that the transfer learning model becomes robust to attacks. Experiments show that the proposed PrivNet model can successfully disentangle the knowledge benefitting the transfer from leaking the privacy.

The world is increasingly awash in connected, "smart" devices. The phenomenon -- often termed the "Internet of Things" or IoT -- began with simple tools like Chromecast or personal digital assistants like Amazon's Alexa. Now it has grown to the point that nearly every device we use in our daily lives is, or is on the verge of being, electronically connected to all the others in a vast web of convenience. Innovations that seemed wildly futuristic just a few years ago are now part of our homes, our cities, and our supply chains. What people often fail to consider are the risks that come with all this interconnectedness.

Natural Language Processing makes the semantics of human language comprehendible to the systems, devices, and machines. Communication is the key to progress! This phrase has been regaled innumerable times, to establish a successful business. Be it an employee-employer relationship or owner-client/customer relationship, communication is the driving force behind successful decision making. Over the years, communication has been enthralled with innovations in technology.

Calldesk, the European pioneer in AI-powered voice agents, and Talkdesk, the cloud contact center for innovative enterprises, announced a partnership to accelerate the deployment of conversational AI in contact centers. Via the partnership with Calldesk, Talkdesk customers will now be able to leverage the advantages of conversational AI, by reducing the cost of processing simple requests and providing a better experience to their callers: a personalized response, 24/7, with no hold time, even during call peaks. The Covid-19 crisis has accelerated the transition to cloud contact centers, due to an increase in customer queries as people began working from home. Calldesk's AI-powered voice agents provide an innovative solution to automate repetitive calls in contact centers, in order to manage call spikes, avoid long queue times, and continue to deliver the best customer experience. "We are excited to have Calldesk join the Talkdesk AppConnect partner community. Outstanding AI technology and industry expertise from Calldesk will strengthen and expand the Talkdesk offering," said John Youri, senior vice president, global channels and alliances.

In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, motivated by various practical applications, from medical diagnosis to dialog systems, where due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration; however, the agent has a freedom to choose which variables to observe. We derive a novel algorithm, called Context-Attentive Thompson Sampling (CATS), which builds upon the Linear Thompson Sampling approach, adapting it to Context-Attentive Bandit setting. We provide a theoretical regret analysis and an extensive empirical evaluation demonstrating advantages of the proposed approach over several baseline methods on a variety of real-life datasets.

Traditional recommender systems encounter several challenges such as data sparsity and unexplained recommendation. To address these challenges, many works propose to exploit semantic information from review data. However, these methods have two major limitations in terms of the way to model textual features and capture textual interaction. For textual modeling, they simply concatenate all the reviews of a user/item into a single review. However, feature extraction at word/phrase level can violate the meaning of the original reviews. As for textual interaction, they defer the interactions to the prediction layer, making them fail to capture complex correlations between users and items. To address those limitations, we propose a novel Hierarchical Text Interaction model(HTI) for rating prediction. In HTI, we propose to model low-level word semantics and high-level review representations hierarchically. The hierarchy allows us to exploit textual features at different granularities. To further capture complex user-item interactions, we propose to exploit semantic correlations between each user-item pair at different hierarchies. At word level, we propose an attention mechanism specialized to each user-item pair, and capture the important words for representing each review. At review level, we mutually propagate textual features between the user and item, and capture the informative reviews. The aggregated review representations are integrated into a collaborative filtering framework for rating prediction. Experiments on five real-world datasets demonstrate that HTI outperforms state-of-the-art models by a large margin. Further case studies provide a deep insight into HTI's ability to capture semantic correlations at different levels of granularities for rating prediction.

The introduction of artificial intelligence into activities traditionally carried out by human beings produces brutal changes. This is not without consequences for human values. This paper is about designing and implementing models of ethical behaviors in AI-based systems, and more specifically it presents a methodology for designing systems that take ethical aspects into account at an early stage while finding an innovative solution to prevent human values from being affected. Two case studies where AI-based innovations complement economic and social proposals with this methodology are presented: one in the field of culture and operated by a private company, the other in the field of scientific research and supported by a state organization.