Far Right and Islamist groups seeking to recruit people to their particular political cause promote ‘black and white’ ideologies that lead to polarization, hatred, intolerance and violence. Often cherry-picking from religious doctrines, they rely on superficial understanding and interpretation of such doctrines. The activities of such groups can lead to disruption of social cohesion, diminished civic capacity, social tensions, hate speech, intolerance, discrimination and even violence. This project builds on existing knowledge and policy experience with a view of developing better analytical and policy tools for the design of more efficient resilience policies that counteract polarization and prevent violent extremism. The project starts with a critical reading of existing scholarly literature and with a critical mapping of existing policy approaches to develop a preliminary impact assessment of these approaches. It continues with a further survey of good practices in counteracting polarization and violent extremism and builds an integrated database of such practices. The project develops a Resilience Hub that engages with three types of factors that can promote or mitigate polarization and violent extremism in society: notably historical and cultural factors; socio-economic conditions; the role of the social media and networking. We develop stakeholder workshops in relation to these three sets of factors that affect radicalization in society, and follow up with digital forums with the participation of a large number of stakeholders. Each stakeholder dialogue builds a tool of resilience in their field: notably inter-faith education training for secondary school teachers; a proposal for a basic income policy that mitigates socio-economic inequalities; a guide to responsible social media design. The Resilience Hub further develops a Resilience Fair where arts-based community interventions to stop polarization and build resilience will be presented

AI has entered the business mainstream, opening opportunities to boost productivity and innovation but suffer limitations hindering wider adoption of model-based or data-driven AI algorithms in industrial settings. Both approaches complement each other and form a critical foundation for the adoption of AI in industry. However, hybrid AI does not fully address the issue of trust (validity, explainability, and ethics). ULTIMATE will pioneer the development of industrial-grade hybrid AI based on three stages to ensure trustworthiness, relying on interdisciplinary data sources and adhering to physical constraints (1st stage), as well as the development of tools for explaining, evaluating and validating hybrid AI algorithms and asserting their adherence to ethical and legal regulations (2nd stage). These will be exemplified using real-world industrial use cases (3rd stage) in the Robotics (collaboration between human and robots for logistics activities) and Space domains (Failure detection for satellites) to promote the widespread adoption of hybrid AI in industry. The breakthrough generic hybrid AI architectures with improved explainability and interpretability and the predictive model on trustworthiness developed in ULTIMATE will provide industrials with improved shopfloor efficiency (reduction of downtime by 30% and of operational costs) and empower their staff through trustful human/machine cooperation allowing highly skilled jobs and increasing decision power and safety. This will be beneficial to European industry to gain pre-emptive advantage in the market of industrial AI solutions and will eventually increase trustworthiness in the use of hybrid AI components by the wider public. Extending over 36 months, the ULTIMATE project brings together key industrial stakeholders, with relevant end-users from manufacturing sectors, leading academic and research institutions, and SMEs to collaboratively investigate and lead the development of hybrid AI approaches.