Abstract

In this webinar the partners of the FAIRWork project will provide an overview on the requirements and scenarios that were identified for the further progress of the project. Therefore, they will provide a short introduction to the project as a whole and how the requirements were identified. Afterwards the defined scenarios, which will be used as guidelines for the rest of the project, will be introduced. Last but not least, the next steps of the project as a whole will be introduced.

The webinar is moderated by Dr. Wilfrid Utz (OMiLAB).

Abstract

In this webinar the partners of the FAIRWork project will provide an overview on the design thinking approach for creating user requirements. Afterwards, we explain the chosen overall methodology of creating complex decision-making. In this part we give an overview on relevant concepts for the research direction and implementation of such complex decision-making by using AI services. Technical concepts for a concrete implementation such as digital knowledge base, digital twin, and digital shadow will be discussed as well. Finally, an explanation of the orchestration of decision-making processes by using micro-services will be presented.

Abstract

The FAIRWork project partners will provide an overview of the proposed high-level architecture in this webinar. In addition, a mapping of the Democratized AI Decision Support System (DAI-DSS) to project's use cases will be presented, along with potential AI services. An overview of the proposed architectural components will be provided. Finally, the project's next stages will be discussed.

Abstract

AI poses ethical challenges. More than that, these ethical challenges are often not easily identifiable. The consortium of FAIRwork researches and works in borderline areas of technical development and collective lifeworlds. The webinar aims to take up the specifics of this socio-technical situation and to develop appropriate strategies for increasing ethical reflection capacity for the FAIRWork project. For this purpose, the so-called STIR method will be presented, which offers options to reflect the development of new ideas. We will bring it together with the core ideas of the Ethical Guidelines in the FAIRWork project.

Abstract

In this webinar, the consortium of the FAIRWork project will provide a status update on the latest project achievements. An overview focusing on the research methods and tools to enable an integrative “democratic” hybrid decision-making and proceedings in the development of the knowledge base is given. Therefore, an introduction to the research factors, then a short recap of the use case scenarios, and finally, a presentation of the knowledge base will be provided. To conclude the status report, the next steps of the project as a whole will be outlined.

Abstract

The OMiLAB Digital Innovation Environment provides capabilities to develop, evaluate and sustain novel ideas in an experimental laboratory setting. Within FAIRWork, the generic structure of DIEn is established as an environment to demonstrate and communicate early ideas with a low TRL as input for refinement and integration into solutions established in the context of the project. Fast prototyping and feasibility assessment is enabled through a common structure of experiments, building on models as an invariant and its model value as processing mechanisms and algorithms on various abstraction levels. Digital twinning is provided on design and physical level, whereas smart models act as mediators between these layers.
In this webinar, we will introduce an experiment showcase in the context of the FAIRWork.

Abstract

BOC will provide an overview of the current status of the initial prototype of the Democratized AI Decision Support System (DAI-DSS). Following a brief introduction to the system's architecture and the use case on which the demonstration is based, three building blocks are explained in further depth and shown in form of short live demos. The user interfaces, the configurator, and the orchestrator are the components that will be emphasized in this webinar. The use of the Microservice Framework OLIVE inside these components is highlighted in particular.

Abstract

MORE will provide an overview of the Multi-Agent Systems (MAS) in the FAIRWork project. Initially, we'll delve into the MAS definition and its characteristics. Following, we'll highlight how agent interaction is important for leveraging human participation in decision-making processes, and machine representation in scalable and complex environments. At the end, we'll show an example of a MAS application in a use case and an experiment with a robot arm. In this webinar, you'll get to know what agents are, what role they play in supporting decision-making, and how Multi-Agent Systems can be applied in industry case scenarios.

Abstract

Join us for a webinar that delves into AI Enrichment Services in the FAIRwork project. Learn how AI algorithms, including Neural Networks, Genetic Algorithms, Fuzzy Logic, Reinforcement Learning and Neural-guided Monte Carlo Tree Search are applied in the FAIRWork Use-Cases.

Abstract

This presentation by Jotne, delves into the Digital Twin’s implementation within the context of FAIRWork use case scenario CRF workload balance. We will start by introducing the concept of Digital Twin Systems and its components. The presentation progresses to illustrate practical steps on creating standards based digital representations using the DAI-DSS Knowledge Base (EDMtruePLM). Furthermore, in this webinar we’ll show how relevant data is collected and stored in Knowledge Base through the created digital representations. In the end the webinar provides insights into leveraging AI services for decision support powered by the data for optimal worker allocations.

Abstract

In this webinar, we give insights into our research on socio-technical aspects in the FAIRWork Project. More specifically, we’ll focus on the results from a first visit to our use-case partner Flex in Althofen, Austria. The reason for this visit lies in understanding the current working situation of the employees, their decision-making situations, and their attitude towards automated systems. To accomplish this, we conducted questionnaire studies and interviews with employees. The results of which will be presented in this webinar.