Industry 5.0 and Collaborative Intelligence AI-driven solutionsTOPIC-1 candidates shall experiment “test before invest” and “skills development” services in realistic DIH-driven testing and experimentation facilities, where Digital Twins and Digital Personas will be modelled using Open Standard approaches and their dynamic interaction implemented by AI technologies. The envisaged end-to-end solution shall encompass modelling, simulation, deployment, training, testing and experimentation phases and contribute to the AI REGIO Data4AI and AI4Manufacturing solutions, by enriching them with new tools and components, to be then inserted in the AI REGIO Marketplace. Experimental ‘laboratory’ environments are useful to consider AI ethical and regulatory issues under controlled conditions, as an example to enable machines to be aware of human behaviour of volunteers in real regulatory conditions. Development of a TEchnological REgulatory SAndbox as an evolution of the experiment, will be considered as optional, preferential feature in the evaluation.

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Manufacturing Data Spaces and Data4AI pipelinesThe main goal of TOPIC-2 proposals is to build embryonic Data Spaces for Manufacturing, while showing the benefits of enhancing the value of the manufacturing data, before they are used in advanced AI applications. Data originating from different data sources have different values for further analyses and are characterised by heterogeneous quality levels, so that a flexible and configurable Data Quality pipeline is needed. AI REGIO defines a high-level architecture for Data4AI applications and recommends the usage of end-to-end Data Pipelines. Proposals in this topic shall provide end-to-end business scenarios, reflecting real industry challenges and defining and measuring realistic data-driven business KPIs. In this perspective, it is expected that the application experiments provide their own datasets and the commitment of Manufacturing SMEs to define and measure the business benefits from Data4AI applications.

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