TEAMING.AI Human-AI Teaming Platform for Maintaining and Evolving AI Systems in Manufacturing

Summary

Smart Manufacturing is believed to play a critical role in maintaining the competitiveness of organisations, by supporting them at different levels such as process optimisation, resource efficiency, predictive maintenance and quality control. Nevertheless, AI technologies which are currently and rapidly penetrating industrial sectors at those levels remain essentially narrow AI systems. This is due to the lack of self-adaptiveness in the AIs capability to assimilate and interpret new information outside of its predefined programmed parameters. This mean that AI systems are tailored for solving specific tasks on a specific predefined setting and changes in the underlying setting usually requires system adaption ranging from fine-grained parameter adaptations to fully-fledged re-design and re-development of AI systems.

TEAMING_AI project aims at a human AI teaming framework that integrates the strengths of both, the flexibility of human intelligence and scale-up capability of machine intelligence. Human AI teaming is equally motivated to meet the increased need for flexibility in the maintenance and further evolution of AI systems, driven by the increasing personalization of products and service, as well as tackling the barriers of user acceptance and ethical challenges involved in the collaborative environments where artificial intelligence will be used, in order AI can be considered as 'teammate' rather than as a threat.

The TEAMING.AI project will be run over 36 months with a work plan divided into 9 Work Packages. Work Packages from 1 to 5 are devoted to the development of new technology to enhance the interaction between human and machine. Furthermore, Work Packages 6 and 7 wrap the development of 3 use case scenarios. Finally, two final Work Packages (8 and 9) will work respectively on the dissemination, exploitation of results and coordination of the project in a transversally way to the above mentioned WPs.

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Web resources:	https://cordis.europa.eu/project/id/957402 https://www.teamingai-project.eu/
Start date:	01-01-2021
End date:	30-06-2024
Total budget - Public funding:	5 721 853,00 Euro - 5 721 853,00 Euro
Twitter:	@AiTeaming

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Original description

Smart Manufacturing is believed to play a critical role in maintaining the competitiveness of organisations, by supporting them at different levels such as process optimisation, resource efficiency, predictive maintenance and quality control. Nevertheless, AI technologies which are currently and rapidly penetrating industrial sectors at those levels remain essentially narrow AI systems. This is due to the lack of self-adaptiveness in the AIs capability to assimilate and interpret new information outside of its predefined programmed parameters. This mean that AI systems are tailored for solving specific tasks on a specific predefined setting and changes in the underlying setting usually requires system adaption ranging from fine-grained parameter adaptations to fully-fledged re-design and re-development of AI systems.

TEAMING_AI project aims at a human AI teaming framework that integrates the strengths of both, the flexibility of human intelligence and scale-up capability of machine intelligence. Human AI teaming is equally motivated to meet the increased need for flexibility in the maintenance and further evolution of AI systems, driven by the increasing personalization of products and service, as well as tackling the barriers of user acceptance and ethical challenges involved in the collaborative environments where artificial intelligence will be used, in order AI can be considered as “teammate” rather than as a threat.

The TEAMING.AI project will be run over 36 months with a work plan divided into 9 Work Packages. Work Packages from 1 to 5 are devoted to the development of new technology to enhance the interaction between human and machine. Furthermore, Work Packages 6 and 7 wrap the development of 3 use case scenarios. Finally, two final Work Packages (8 and 9) will work respectively on the dissemination, exploitation of results and coordination of the project in a transversally way to the above mentioned WPs.