  Availability of powerful computation and communication technology as well as
advances in artificial intelligence enable a new generation of complex,
AI-intense systems and applications. Such systems and applications promise
exciting improvements on a societal level, yet they also bring with them new
challenges for their development. In this paper we argue that significant
challenges relate to defining and ensuring behaviour and quality attributes of
such systems and applications. We specifically derive four challenge areas from
relevant use cases of complex, AI-intense systems and applications related to
industry, transportation, and home automation: understanding, determining, and
specifying (i) contextual definitions and requirements, (ii) data attributes
and requirements, (iii) performance definition and monitoring, and (iv) the
impact of human factors on system acceptance and success. Solving these
challenges will imply process support that integrates new requirements
engineering methods into development approaches for complex, AI-intense systems
and applications. We present these challenges in detail and propose a research
roadmap.
