Summary
RAIDO is a powerful framework solution designed to develop trustworthy and green artificial intelligence (AI). Trustworthy AI focuses on ensuring the reliability, safety, and unbiased optimization and deployment of AI systems, particularly in critical applications such as healthcare, farming, energy, and robotics. On the other hand, Green AI involves the development and deployment of energy-efficient and environmentally sustainable AI technologies, leading to reduced environmental impact and improved resource management.
RAIDO provides an array of automated data curation and enrichment methods, including digital twins and diffusion models, to create high-quality, representative, unbiased, and compliant training data. It also offers various data- and compute-efficient models and tools to create energy-efficient Green AI, such as few- and zero-shot learning, dataset and model search, data and model distillation, and continual learning.
To ensure the transparency, explainability, and reliability of the optimized AI models and data handling processes, RAIDO uses various XAI methods, decentralized blockchain, feedback-based reinforcement learning, novel KPIs, and visualization techniques. Additionally, the innovative AI orchestrator optimizes related tasks and processes, reducing the overall energy consumption and environmental footprint of the models during both development and deployment.
RAIDO emphasizes the development of dynamic interfaces that support the appropriate AI paradigms (central, distributed, dynamic, hybrid) and enable seamless adaptation to the needs of the use situation. Furthermore, RAIDO will be evaluated through four real-life demonstrators in key application domains, such as smart grids, computer vision-based smart farming, healthcare, and robotics, showcasing notable societal and market impact.
RAIDO provides an array of automated data curation and enrichment methods, including digital twins and diffusion models, to create high-quality, representative, unbiased, and compliant training data. It also offers various data- and compute-efficient models and tools to create energy-efficient Green AI, such as few- and zero-shot learning, dataset and model search, data and model distillation, and continual learning.
To ensure the transparency, explainability, and reliability of the optimized AI models and data handling processes, RAIDO uses various XAI methods, decentralized blockchain, feedback-based reinforcement learning, novel KPIs, and visualization techniques. Additionally, the innovative AI orchestrator optimizes related tasks and processes, reducing the overall energy consumption and environmental footprint of the models during both development and deployment.
RAIDO emphasizes the development of dynamic interfaces that support the appropriate AI paradigms (central, distributed, dynamic, hybrid) and enable seamless adaptation to the needs of the use situation. Furthermore, RAIDO will be evaluated through four real-life demonstrators in key application domains, such as smart grids, computer vision-based smart farming, healthcare, and robotics, showcasing notable societal and market impact.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101135800 |
| Start date: | 01-01-2024 |
| End date: | 31-12-2026 |
| Total budget - Public funding: | 8 034 707,50 Euro - 7 966 357,00 Euro |
Cordis data
Original description
RAIDO is a powerful framework solution designed to develop trustworthy and green artificial intelligence (AI). Trustworthy AI focuses on ensuring the reliability, safety, and unbiased optimization and deployment of AI systems, particularly in critical applications such as healthcare, farming, energy, and robotics. On the other hand, Green AI involves the development and deployment of energy-efficient and environmentally sustainable AI technologies, leading to reduced environmental impact and improved resource management.RAIDO provides an array of automated data curation and enrichment methods, including digital twins and diffusion models, to create high-quality, representative, unbiased, and compliant training data. It also offers various data- and compute-efficient models and tools to create energy-efficient Green AI, such as few- and zero-shot learning, dataset and model search, data and model distillation, and continual learning.
To ensure the transparency, explainability, and reliability of the optimized AI models and data handling processes, RAIDO uses various XAI methods, decentralized blockchain, feedback-based reinforcement learning, novel KPIs, and visualization techniques. Additionally, the innovative AI orchestrator optimizes related tasks and processes, reducing the overall energy consumption and environmental footprint of the models during both development and deployment.
RAIDO emphasizes the development of dynamic interfaces that support the appropriate AI paradigms (central, distributed, dynamic, hybrid) and enable seamless adaptation to the needs of the use situation. Furthermore, RAIDO will be evaluated through four real-life demonstrators in key application domains, such as smart grids, computer vision-based smart farming, healthcare, and robotics, showcasing notable societal and market impact.
Status
SIGNEDCall topic
HORIZON-CL4-2023-HUMAN-01-01Update Date
12-03-2024
Images
No images available.
Geographical location(s)
Structured mapping
Unfold all
/
Fold all
Search
Organisations
-
| INSTITUT JOZEF STEFAN (Coördinator)
-
| ADRESTIA EREVNITIKI IDIOTIKI KEFALAIOUXIKI ETAIREIA
-
| AXON LOGIC IDIOTIKI KEFALAIOUXIKI ETAIREIA
-
| AYESA ADVANCED TECHNOLOGIES SA
-
| BIOLYNX
-
| CHELENIC NTAINAMIKS MONOPROSOPI AE
-
| COMMUNAUTE D' UNIVERSITES ET ETABLISSEMENTS UNIVERSITE BOURGOGNE - FRANCHE - COMTE
-
| CYBERJAB LIMITED
-
| DIMOSIA EPICHEIRISI ILEKTRISMOU ANONYMI ETAIREIA
-
| EIGHT BELLS LTD
-
| ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXIS (CERTH)
-
| FUJITSU TECHNOLOGY SOLUTIONS (LUXEMBOURG) SA
-
| INDIAN INSTITUTE OF TECHNOLOGY, KHARAGPUR
-
| INTRASOFT INTERNATIONAL SA
-
| JESSA ZIEKENHUIS