The Fraunhofer Institutes IOSB and IAIS have published a new white paper together with the Fraunhofer Cluster of Excellence Cognitive Internet Technologies CCIT. The paper focuses on the use of AI-based components and their integration into reliable systems. The white paper is available for free download.
Getting a grip on optimisation problems with AI
Artificial intelligence promises to solve data-driven optimisation problems that are difficult to grasp with other methods - also in the field of industrial manufacturing and logistics. However, engineers are faced with the challenge that predictable and permanently reliable performance is expected when designing industrial plants and processes. These framework conditions must be considered and implemented in the development and operation of AI-based systems.
The new discipline of AI engineering provides a solution for this. Its subject is an engineering and systematic approach to the use of AI methods as part of a holistic systems engineering process. The new white paper of the same name by the Fraunhofer Institutes for Intelligent Analysis and Information Systems IAIS and for Optronics, System Technologies and Image Exploitation IOSB describes how AI engineering can be used beneficially in production.
AI applications must run reliably and predictably
"AI is both enabler and solution space in the edge-cloud continuum envisioned by the Fraunhofer Cluster of Excellence Cognitive Internet Technologies CCIT - a continuous data space in which the shift of computing power between edge and cloud is demand-driven and dynamically automated. AI generates new knowledge from the data and new business models from it. To leverage this potential, AI applications must run reliably and predictably. Our white paper shows how this can be implemented in production," says Michael Fritz, head of the Fraunhofer CCIT office.
This white paper, written by 14 experts from Fraunhofer IAIS and IOSB, outlines the current state of research: It spans the dimensions for AI engineering applications and outlines the qualitative requirements in development and operation from the perspective of users and decision-makers. It classifies different use cases into four levels of autonomy, starting with AI-based assistance functions up to autonomous and adaptive systems.
It classifies different use cases into four levels of autonomy, ranging from AI-based assistance functions to autonomous and adaptive systems.
The white paper can be downloaded free of charge
One chapter addresses the technical and organisational challenges of using AI methods. Here, the AI engineering procedure model PAISE® (Process Model for AI Systems Engineering) is presented and explained in the context of existing models from machine learning and software engineering. Finally, the white paper refers to further training and consulting offers, among others, within the framework of the Fraunhofer Big Data and AI Alliance and the Competence Centre for AI Engineering Karlsruhe (CC-KING), in which PAISE® was developed.
The whitepaper offers AI users in industry valuable insights and solution approaches for their challenges and is available for free download on the Fraunhofer CCIT website.
