GSS EU Science Funding Gateway

Develop trustworthy AI technology key for acceptance: tis will require improvement in transparency: explainability, accountability and responsibility, safety, expected levels of technical performance (accuracy, robustness, level of ‘intelligence’ and autonomy, etc.) which are guaranteed/verifiable and with corresponding confidence levels. Build the next level of “intelligence” and autonomy: this will investigate approaches such as integration of both learning and reasoning, causality, contextualization and knowledge discovery, hybrid semi-parametric models (combining laws of physics with observations, aka physics-informed machine learning), human-in-the loop approaches, etc.

Proposals should clearly identify its focused research area among the 5 listed below:

• Novel or promising learning (such as unsupervised, self-supervised, representational learning capable of contextualization, transfer learning, life-long and continual learning, etc.) as well as symbolic and hybrid approaches. The objective is to advance “intelligence” and autonomy of AI-based systems. 
• Advanced transparency in AI including advances in explainability, in transparency investigating novel or improved approaches increasing users’ understanding of AI system behaviour, and therefore increasing trust in such systems.
• Greener AI, increasing data and energy efficiency.
• Advances in edge AI networks, bringing intelligence near sensors, in embedded systems with limited computational, storage and communication resources, as well as the integration of advanced and adaptive sensors and perception but also optimising edge vs cloud AI to maximise the capabilities of the overall system.
• Complex systems & socially aware AI: able to anticipate and cope with the consequences of complex network effects in large scale mixed communities of humans and AI systems interacting over various temporal and spatial scales.

Proposals could also address the development of scientific foundations for designing, modelling, analysing, operating, monitoring, integrating, maintaining and extending AI systems.