AI Systems Engineering & Lifecycle Management
As TNO, we look at the entire lifecycle of AI systems and seek technical and organisational solutions that improve AI applications’ lifecycle, reliability, and deployability.
To what extent can an AI system adapt to new laws and user requirements? And is such an AI system flexible enough to quickly respond to societal developments? For engineers, developing future-proof AI systems is a tremendous challenge.
To start with, engineers must consider the needs and requirements of an AI system’s stakeholder and the environment in which it will be deployed. Furthermore, the right moral, ethical, and legal choices must be made and recorded.
And the needs and requirements do not only need to be considered during the development phase, it is also very important to address these during the deployment of an AI system. After all, the goal is to arrive at AI solutions that can demonstrably quickly and safely adapt to the demands and requirements of the future and its changing environments.
Continuously keeping an AI system up to date requires multidisciplinary engineers working closely with all stakeholders andmaking both technical and organisational adjustments.
More info about this research theme can be found on the AI Systems Engineering & Lifecycle Management website.
- Frank Benders, Principal Systems Engineer / Architect, e-mail: email@example.com