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.
Monitoring elderly people in health care units is one of the most important problems in our society. Population is constantly ageing, leading to ever increasing medical costs. Thus, to reduce these costs and provide the best quality of life, the elderly should be maintained at home (Sixsmith 1994) and solutions are needed to follow these patients particularly if the person lives alone and has experienced a loss of autonomy. Based on this, many smart surveillance and tracking systems have been developed with a host of software and hardware architectures that make up the socalled "Intelligent home" (Rialle et al. 2001). Indeed, the increasing maturity of algorithms and techniques makes it possible to apply this technology to this sector (Tang and Venables 2000), (Ogawa and Togawa 2000), (Rialle et 1999), (Togawa et al. 1998), (Celler et al. 1994).
An app has been launched to help social distancing by showing beachgoers which areas are crowded. Developed by Bournemouth, Christchurch and Poole Tourism, the free BCP Beach Check app gives real time information for visitors. Thousands flocked to the area last month as lockdown restrictions eased, which council bosses said stretched services "to the absolute hilt". The app is available for Apple or Android devices. Council leader Vikki Slade said: "With 15 miles of coastline, there is more than enough space for people to be able to spread themselves around and maintain social distancing which is pivotal to ensuring the minimal spread of this pandemic."
Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.