Abstract
We propose a vision for directing research and education in the ICT field. Our Smart and Sustainable World vision targets at prosperity for the people and the planet through better awareness and control of both human-made and natural environment. The needs of the society, individuals, and industries are fulfilled with intelligent systems that sense their environment, make proactive decisions on actions advancing their goals, and perform the actions on the environment. We emphasize artificial intelligence, feedback loops, human acceptance and control, intelligent use of basic resources, performance parameters, mission-oriented interdisciplinary research, and a holistic systems view complementing the conventional analytical reductive view as a research paradigm especially for complex problems. To serve a broad audience, we explain these concepts and list the essential literature. We suggest planning research and education by specifying, in a step-wise manner, scenarios, performance criteria, system models, research problems and education content, resulting in common goals and a coherent project portfolio as well as education curricula. Research and education produce feedback to support evolutionary development and encourage creativity in research. Finally, we propose concrete actions for realizing this approach.
Highlights
We are convinced that common research paradigms and goals, including shared visions and research problems, promote teamwork and result in coherent project portfolios, as suggested in [1], [2], and accelerate research and development
We argue that managing the complexity calls for studying system-level research problems - and this requires complementing the conventional atomistic reductive view for conducting research with a holistic systems view [18]–[20]
We are convinced that common goals, including shared visions and research problems, promote teamwork and result in coherent project portfolios, improving the research culture
Summary
We are convinced that common research paradigms and goals, including shared visions and research problems, promote teamwork and result in coherent project portfolios, as suggested in [1], [2], and accelerate research and development. As intelligent systems are often complex, they call for systems view and studying system-level research problems This approach is needed to meet the strict performance requirements in the presence of resource constraints (Section IV-A), when the systems form largescale systems of systems [17], [97]. Data science can contribute to realizing feedback for intelligent systems as it encompasses the principles that support and guide the extraction of information and knowledge from data [122], [123] It consists of various, somewhat overlapping concepts and includes machine learning, pattern recognition, data mining, knowledge discovery, and big data. We expect the concepts introduced to be crucial when intelligent resource-efficient systems are developed This challenge deserves attention – we need to master feedback to develop the intelligent systems that together realize the Smart and Sustainable World vision. The aim is that the learners can discover that knowledge is tentative and open to continued shifts and changes
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