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The European Commission (EC) has launched a preparatory action to create a European Network of Digital Innovation Hubs with focus on AI. To achieve this goal, the action - managed by PwC, together with CARSA and Innovalia - is intended to lead to the development of a blueprint for cross border collaboration based on a thorough assessment of hub business models, common systems, collaboration and governance structures (including financial and legal aspects of the collaboration). By the same token, the action will support the development of a concrete action plan, including a business case, for the collaboration and networking of DIH and will help the chosen DIHs to unlock their collaboration and networking potential through mentoring and coaching activities. Further, participating DIHs will have the chance to network with other hubs, and will be involved in the definition and, possibly in the signature, of a Cooperation Agreement among (at least) 10 hubs.
Upon identification of the most suitable collaboration schemes, the project will provide assistance in the modelling of a cross-border cooperation blueprint for DIHs and will support the creation of a network of DIHs allowing for the transfer of technical knowledge and the development of an integration and cooperation plan between hub/networks with DIHs and stakeholders at the EU level. Selected DIHs will take part in a coaching and mentoring programme providing support for the management of legal and financial issues concerning cooperation. The programme will be designed according to the training needs of selected DIHs and will be delivered through a combination of tools, including webinars, regional workshops, on-site training, coaching and mentoring sessions through a dedicated platform. The platform will also foster cooperation for it allows co-creation of documents, communication among DIHs and live interactions (including the opportunity to hold mentoring sessions).
DIHs located in one of the EU 28 Members States or countries associated to Horizon 2020 and listed in the European Catalogue with technological focus on AI and, eventually, on other technologies showing interactions with AI.
The project will support DIHs in the creation of a structured blueprint for cooperation, offering possibilities to network with other DIHs.
Via a mentoring and coaching programme and technical assistance delivering advice on legal and financial issues linked to collaboration.
The call for Expression of Interest will remain open until December 21. The selection process will be finalized by April 2019. The mentoring and coaching activities will take place after the selection of DIHs and will last until January 2020.
Today’s fast developments in digital technologies make it difficult for small and medium-sized enterprises (SMEs) to adopt and integrate them into their processes. Under the Digitising European Industry initiative, 'Digital Innovation Hubs' (DIHs) have been designed as tools to support businesses (in particular SMEs and non-technological industry) in their digital transformation. Acting as a one-stop- shop, they provide a series of support services to companies in their region and beyond by allowing them to access knowledge, methods and software, technology platforms, prototyping solutions and testing facilities. As per today, 379 DIHs in EU 28 are listed in the European catalogue, 210 of which focus on Artificial Intelligence and cognitive systems.
Artificial intelligence (AI) refers to systems displaying intelligent behaviour by analysing their environment and taking actions – with some degree of autonomy – to achieve specific goals. Specifically, any device that can “sense”, i.e. perceives the external environment, “think”, i.e. analyses the environment, and “act”, i.e. takes actions maximising the chance of successfully achieving its goals, can be considered as Artificial Intelligence. AI can be software based (e.g. voice assistants) or embedded in hardware devices (e.g. advanced robots). There are different research fields where AI is applied, including sentiment analysis, conversational systems, natural language understanding and question answering. The approaches used include machine learning and traditional symbolic AI.
Robotics is an interdisciplinary domain, including engineering and science that involves the use of technologies to develop machines replicating human actions. Robotics can be used in many fields and today it is mostly exploited in manufacturing processes. The combination of AI and Robotics can support different stages of production, from assembly (where the coupling of AI and advanced vision systems can support real-time course correction), to packaging (where AI permits to save and refine motions of robotic system in use) to customer service (with AI supporting robotics used in managing relationships with customers).
Robotics is an interdisciplinary domain, including engineering and science that involves the use of technologies to develop machines replicating human actions. Robotics can be used in many fields and today it is mostly exploited in manufacturing processes. The combination of AI and Robotics can support different stages of production, from assembly (where the coupling of AI and advanced vision systems can support real-time course correction), to packaging (where AI permits to save and refine motions of robotic system in use) to customer service (with AI supporting robotics used in managing relationships with customers).
The IoT is the network of physical devices embedded with elements enabling connection with other devices, collection and exchange of data over the Internet. Alternatively, devices can act as “actuator”, switching on and off a conditioning system or a problematic equipment. AI and IoT interaction permits to increase efficiency of industrial and managing process. For instance, in the domain of industrial manufacturing, they can optimize maintenance cost - predicting equipment failure and planning maintenance procedure - and to increase operational machinery efficiency - identifying and managing the parameters to be adjusted to maintain ideal outcomes. Combining IoT and AI enhances risk management, predicting and understanding a variety of risks and introducing automation for rapid response. IoT and AI are combining their strength also on the 5G technology allowing sensors to modify network settings real-time to ensure sufficient bandwidth and connectivity.
A cyber-physical system (CPS) is a mechanism controlled or monitored by computer-based algorithms, tightly integrated with the Internet and its users. Within CPS, physical and software components are deeply intertwined, each operating on different spatial and temporal scales, exhibiting multiple and distinct behavioural modalities, and interacting with each other in many ways that change with context. CPS have been recently applied in a number of sectors, from smart cities, to environmental monitoring and transportation systems or to smart grid. In manufacturing, information collected by CPSs combined with AI enable networked machines to perform more efficiently, collaboratively and resiliently.
A cyber-physical system (CPS) is a mechanism controlled or monitored by computer-based algorithms, tightly integrated with the Internet and its users. Within CPS, physical and software components are deeply intertwined, each operating on different spatial and temporal scales, exhibiting multiple and distinct behavioural modalities, and interacting with each other in many ways that change with context. CPS have been recently applied in a number of sectors, from smart cities, to environmental monitoring and transportation systems or to smart grid. In manufacturing, information collected by CPSs combined with AI enable networked machines to perform more efficiently, collaboratively and resiliently.
Digital art means technological art, encompassing artistic works made or presented with the support of digital technology. Digital art includes purely computer-generated art or art taken from other sources and digitally elaborated, as wells as 3D virtual sculpture renderings and projects combining a mix of technologies. AI can be combined to Digital art and design to enhance design systems (i.e. a series of patterns, modules and elements that, combined, build the design language of a brand or product), create generative visual styles - as AI learns about what users draw and support new creations - or to personalise user experience, as in the case of websites taking user data points to enable more personalized experiences for visitors.
Modelling and simulation (M&S) is used to solve real-world problems safely and efficiently, without need for physical experimentation. Combining this possibility with AI enables to redesign some stages of production and business. For instance, it can support the prediction of consumers’ behaviour based on the modelling of the data collected - or it can be used to optimize processes, via simulation using a number of parameters that could be hardly managed without AI. Last but not least, simulation models can be used to train AI components, generating the data sets necessary for neural network training.
Modelling and simulation (M&S) is used to solve real-world problems safely and efficiently, without need for physical experimentation. Combining this possibility with AI enables to redesign some stages of production and business. For instance, it can support the prediction of consumers’ behaviour based on the modelling of the data collected - or it can be used to optimize processes, via simulation using a number of parameters that could be hardly managed without AI. Last but not least, simulation models can be used to train AI components, generating the data sets necessary for neural network training.
Digital manufacturing is an integrated approach to manufacturing that is centred on a computer system. AI improves effectiveness and efficiency of digital manufacturing, by enabling to make more informed decisions in real time at each stage of the production chain. For instance, it enables an effective management of defect, which are immediately removed from the line and replaced, permitting to save costs due to recalls, repairs and lost business. Other key domains for the application of AI include generative design, fraud prevention, predictive ordering and opportunity assessment.
Medical technology includes any technology applied in the domain of diagnostics and cure of diseases, with the final goal of improving the quality of care, and the efficacy and sustainability of healthcare systems. Overall, AI can help to make medical equipment smarter, imaging and lab results faster, and examinations more precise. Specifically, the areas benefiting from the use of AI include predictive analysis - since machine learning can look back at a patient’s past medical records and find patterns that could suggesting illness -, integrated decision support, as collating multiple data sources in clinical workflows allows for more precise and cost-effective diagnostics and therapies - and medical imaging.
Medical technology includes any technology applied in the domain of diagnostics and cure of diseases, with the final goal of improving the quality of care, and the efficacy and sustainability of healthcare systems. Overall, AI can help to make medical equipment smarter, imaging and lab results faster, and examinations more precise. Specifically, the areas benefiting from the use of AI include predictive analysis - since machine learning can look back at a patient’s past medical records and find patterns that could suggesting illness -, integrated decision support, as collating multiple data sources in clinical workflows allows for more precise and cost-effective diagnostics and therapies - and medical imaging.
