Objective
AI-DAPT brings forward a data-centric mentality in AI, that is effectively fused with a model-centric, science-guided approach, across the complete lifecycle of AI-Ops, by introducing end-to-end automation and AI-based systematic methods to support the design, the execution, the observability and the lifecycle management of robust, intelligent and scalable data-AI pipelines that continuously learn and adapt based on their context. AI-DAPT will design a novel AI-Ops / intelligent pipeline lifecycle framework cross-cutting the different business, legal/ethics, data, AI logic/models, and system requirements while always ensuring a human-in-the-loop (HITL) approach across five axis: “Data Design for AI”, “Data Nurturning for AI”, “Data Generation for AI”, “Model Delivery for AI”, “Data-Model Optimization for AI”. AI-DAPT will contribute to the current research and advance the state-of-the-art techniques and technologies across a number of research paths, including sophisticated Explainable AI (XAI)-driven data operations from purposing, harvesting/mining, exploration, documentation and valuation to interoperability, annotation, cleaning, augmentation and bias detection; collaborative feature engineering minimizing the data where appropriate; adaptive AI for model retraining purposes.
Overall, AI-DAPT aims at reinstating the pure data-related work in its rightful place in AI and at reinforcing the generalizability, reliability, trustworthiness and fairness of Al solutions.
In order to demonstrate the actual innovation and added value that can be derived through the AI-DAPT scientific advancements, the AI-DAPT results will be validated in two, interlinked axes:
I. Through their actual application to address real-life problems in four (4) representative industries: Health, Robotics, Energy, and Manufacturing;
II. Through their integration in different AI solutions, either open source or commercial, that are currently available in the market.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
151 25 Maroussi
Greece
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Participants (17)
80686 Munchen
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X91 K0EK Waterford
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2829-516 Caparica
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
08034 Barcelona
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3013 Limassol
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
14193 Berlin
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
174 56 ALIMOS
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
1678 Nicosia
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20149 MILANO
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
11632 Athina
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
10117 Berlin
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28359 Bremen
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28199 BREMEN
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
546 27 THESSALONIKI
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551 32 THESSALONIKI
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
20133 Milano Mi
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
24126 Bergamo
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.