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 trustworthiness (validity, explainability and ethics).

Promote the widespread adoption of hybrid AI in industry.

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.

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data representation and visualisation models for industrial-grade AI hybrid algorithms



innovative architectures for the construction and training of trustworthy hybrid AI algorithms Design evaluation methodologies for hybrid AI algorithms



the developed hybrid AI algorithms in industrial environments and assess their performance and,



the ethical compliance and trustworthiness of the developed hybrid AI algorithms and the wide uptake of the project results at EU and international level

Concept and Scientific approach

ULTIMATE is based on industrial use cases showcasing needs and requirements from real-world situations since the first steps of the project to ensure operational conditions will be considered all along the life cycle of the hybrid AI algorithms.

The approach of ULTIMATE is to investigate novel or promising learning (together with symbolic models) approaches to design and develop hybrid AI based algorithms with increased intelligence, autonomy, explainability and interpretability. Several hybrid approaches will be studied, resulting in hybrid AI algorithms for different intents, including state estimation, risk assessment, anomaly detection, decision-support, and control. The design of hybrid AI algorithms will be based on the addition of knowledge (e.g. physical laws) to data (observations) with the aim of improving the functional accuracy and alleviating the evaluation.
Then, these hybrid AI-based solutions will be evaluated and prepared for operational conditions through rigorous methodologies using statistical / experimental / formal methods and tools to consolidate trustworthiness.

Finally, the project will assess the behaviour of these AI algorithms under operational conditions (demonstrators) to check performance, trustworthiness, and autonomy (capability to detect any abnormal behaviour and ability to redirect to human if not in charge of processing) and the adequacy between the validated AI algorithm and its operational use.

In continuation of research projects


ULTIMATE commits to establish relationships and exchange results with different EU projects funded in topics relevant to AI, Data and Robotics.

Cohesion activities will be sought with the Public Private Partnership (PPP) on AI, Data and Robotics and funded actions related to this partnership, including the CSA HORIZON-CL4-2021-HUMAN-01-02, relevant European Institute of Innovation and Technology (EIT) and its Knowledge and Innovation Communities (KICs), in particular the EIT Digital.


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