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ULTIMATE
mUlti-Level Trustworthiness to IMprove the Adoption of hybrid arTificial intelligencE
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.
Funding Call
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Duration
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OBJECTIVES
Artificial Intelligence (AI) has entered the business mainstream, opening opportunities to boost productivity and innovation but suffer limitations hindering wider adoption in industrial settings. Both model and data-driven AI approaches naturally complement each other. However, hybrid AI does not fully address the issue of trustworthiness (validity, explainability and ethics). ULTIMATE will pioneer the development of industrial-grade hybrid AI based on 3 stages to ensure trustworthiness and promote the widespread adoption of hybrid AI in industry.
Main objectives are the following:
· Develop data representation / visualisation models, and propose innovative architectures to construct and train hybrid AI algorithms.
· Design rigorous evaluation methodologies with appropriate properties (e.g. accuracy. robustness, safety) to strengthen their trustworthiness.
· Implement the developed hybrid AI algorithms under operational conditions (Robotics and Space) and fully assess them.
· Ensure the ethical compliance and trustworthiness through qualitative / quantitative approaches.
EXPECTED IMPACT
IMPACT 1: advance the current knowledge on the design, development, and deployment of production-grade hybrid AI and on rigorous evaluation methodologies (e.g. confidence estimation methods) to significantly increase the trustworthiness.
IMPACT 2: go beyond some existing standards as a reference in AI solutions to meet industrial requirements (related to safety for instance) to over AI systems trustworthiness more adequately including social and ethical issues.
IMPACT 3: ensure that AI development and implementation is human-centric and is a force for good in society whilst evaluating the consequences taking into account the criteria of people (compliance with appropriate legal, ethical and societal foundations) and the achine’s criteria.
IMPACT 4: support the creation of highquality jobs where humans are making informed decisions using AI outputs rather than simply executing tasks they do not understand.