Digitalisation of society provides a treasure trove of data, based on an abundance of sensors and the endless possibilities for people to connect via websites and social media. Data science and artificial intelligence offer many possibilities to interpret data properly, learn from it and use it to achieve specific goals and tasks. In this way, new, smart services can be developed that offer solutions to current social challenges in a large variety of domains including intelligent digital personal assistants in smartphones, health, engineering, safety and security, business and science.
However, this development comes with challenges. Of course, better solutions in the field of data science and artificial intelligence need to be developed for various applications. On the one hand, this calls for an application-oriented approach and, on the other hand, for a fundamental understanding of the basic principles embedded in computer science, mathematics and image and signal profiling. At the same time, concepts such as fairness, privacy, and trust, as well as threats such as fake news must be addressed. This requires a fundamental interdisciplinary approach connecting the social and technical sciences. Moreover, as the sources of data become more dispersed and heterogenous, we see a strong trend toward moving data analyses close to those sources, leading to a stronger integration of artificial intelligence solutions and hardware at the edge of the network, as well as much more demand for resource-efficient solutions.
Various groups at the University of Twente conduct research on data science and artificial Intelligence, including work on fundamental understanding of machine learning, sensors, efficient realisation of artificial intelligence in hardware, to development and application of artificial intelligence in fields such as health, safety and security, the geo-spatial domain, and manufacturing, to name a few. Central unifying themes are embedded and augmented intelligence.