When it comes to the mobile app industry, businesses of all sizes and specialisations confront strong competition. This position compels them to keep up with all developing digital developments in order to maintain their worth. Recognizing the huge influence of artificial intelligence on business, top firms such as Amazon, eBay, and Tinder make extensive use of AI in their applications to generate tailored mobile user experiences and improve profitability. Start-ups also raise more investment for AI integrations, propelling them to high marketability and competitiveness. Annually, more AI apps go viral, bringing greater exposure and revenues to their owners.
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-of-life. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.
Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.
Living in New England, it seems like I can never catch a break from high utility bills. If paying your utilities hurts your wallet every month, there are a lot of different ways you can slash those bills down to a more manageable number. For one, smart home technology can help you be more efficient with both heating and cooling, as well as with water and electricity use. Here are 10 smart products that can help reduce your utility bills and put money back in your pocket. Are you forever leaving the living room light on?
The system alerts you when the meeting starts, and allows you to join or to indicate that you are running behind with a single click or tap. It provides a visual roster of attendees, late-comers, and those who skipped out entirely. It also provides broadly accessible mute controls in case another participant is typing or their dog is barking. Finally, Echo Show delivers high quality noise-cancelled audio and crisp, clear HD video that works across all user devices and with most conference room video systems. If you are not amiable to take the Video Call, the system will record a message and transcribe the audio into text, with the potential to overlay it in real time synchronization in the future.
Artificial Intelligence will remain a high profile topic for years to come and rightfully so: research firms expect the market to grow at a CAGR of 50% to reach $37 billion by 2025. This will generate game-changing productivity gains and transform entire industries. Since the first successful AI experiments in 1955, progress has accelerated dramatically. AI is now broadly used, providing tangible applications for many consumers daily. Examples include Amazon's algorithms making pointed recommendations, Siri answering questions with real-time data from the internet and Watson entering many commercial markets, where AI was absent yesterday.
The wizards at Microsoft have new hardware! They'll start unveiling at at 10 am Eastern, showing off what we hear is the next bit of Surface hardware and the next version of Windows 10. Microsoft will provide a live video stream, and we will liveblog it all. Because we love to liveblog! It's the best way to offer commentary and real-time analysis of all the announcements.