New approaches for Human/AI collaboration for the workforce of the future (RIA) (Made in Europe and AI, Data and Robotics partnerships)

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Expected Outcome: Industrial jobs are transformed through AI-based human-machine interactions (and skills linked to them) which enhance flexibility, inclusion, well-being, up-skilling, career evolution and knowledge sharing; Increased competitiveness and sustainability of advanced manufacturing industries by means of knowledge formalization and adaptability of the machines to workers and markets based on different cultures. Scope: Innovative AI approaches are poised to revolutionise human-machine collaboration in factories by fostering an environment where technology and human expertise synergistically enhance each other. AI can enhance the value of the companies by capturing and formalising the knowledge which is dispersed and not explicit. This allows companies to really own the knowledge and use it to reduce the onboarding time of new employees and support personnel upskilling to adapt to the evolving technological landscape. AI has a great potential to make task simpler by reducing the complexity offering intuitive interfaces and real-time feedback allowing workforce to be more efficient and effective while facilitating access to more complex tasks including those involving various forms of planning. AI can also adapt the interaction of automation with the worker taking into account particular needs of the human, including adaptation to the different abilities of the workers and facilitating inclusion. Finally, AI can be used to allow easier export of automation produced in EU by facilitating its interaction with workforce having different cultures adapting the interaction of the machines to the different needs. Proposals should produce dedicated innovative AI approaches for human-machine collaboration in advanced manufacturing to be applied in at least two of following fields: Human-AI Co-Learning and knowledge capture to share competences, capture expert knowledge, provide interactive mentoring to up-skill the workforce, and support re-qualification and continuous training – leading to increased knowledge at factory level and avoiding loss of know-how. Human-AI teamwork thanks to innovative natural interaction models (considering the e.g. related hardware interfaces and/or collaborative machine tools), enabling to control complexity in cognitive cooperating production systems, including planning activities at shop floor level. Interfaces with automation which automatically adapt to the need of the humans including different abilities and different cultural needs. Proposals should integrate a gender perspective and avoid any type of discrimination in the design and deployment of AI systems and human-machine interaction models, including addressing differences in user needs, such as needs of persons with disabilities, physical and cognitive ergonomics and training pathways. Proposals are also expected to identify and address other potential biases in AI systems to promote inclusive design that ensures safe and effective use by all workers.…