Cognitive systems are starting to be deployed as appliances across the technological landscape of modern societies. The increasing availability of high performance computing platforms has opened an opportunity for statistics-based cognitive systems that perform quite as humans in certain tasks that resisted the symbolic methods of classic artificial intelligence. Cognitive artefacts appear every day in the media, raising a wave of mild fear concerning artificial intelligence and its impact on society. These systems, performance notwithstanding, are quite brittle and their reduced dependability limips their potential for massive deployment in mission-critical applications -e.g. in autonomous driving or medical diagnosis. In this paper we explore the actual possibility of building cognitive systems using engineering-grade methods that can assure the satisfaction of strict requirements for their operation. The final conclusion will be that, besides the potential improvement provided by a rigorous engineering process, we are still in need of a solid theory -possibly the main outcome of cognitive science- that could sustain such endeavour. In this sense, we propose the use of formal ontologies as backbones of cognitive systems engineering processes and workflows.

Nowadays we find ourselves in what is called the fourth industrial revolution: Industry 4.0. This revolution is being fostered in many countries to get a more competitive industry. Industry 4.0 target is to make more efficient and flexible plants, reduce times and costs of process and products lifecycle. Under this framework, models appear as a core component in every new development. Model-Based Systems Engineering is a methodology that allows for traceability and guarantees model consistency covering the entire lifecycle of the system. The objective of this work is to develop a holistic model of a process plant. Such model will be available during the entire lifecycle of the system (process + product) communicating in every stage with the specific (existing) tools used to develop it. In this paper, we present the development of the SysML model of a process plant (the production of ethylbenzene from benzene and ethylene), the architecture and the methodology to transform the central model to application-specific ones. We also explain how to execute these specific models from the core model keeping consistency between models and data.