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AI-DAPT

Financiado

AI-Ops Framework for Automated, Intelligent and Reliable Data/AI Pipelines Lifec...

AI-Ops Framework for Automated, Intelligent and Reliable Data/AI Pipelines Lifecycle with Humans-in-the-Loop and Coupling of Hybrid Science-Guided and AI Models 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... ver más

30/06/2027

ATHENA - RESEARCH...

9M€

Presupuesto del proyecto: 9M€

Líder del proyecto

ATHINAEREVNITIKO KENTRO KAINOTOMIAS STIS TECH... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 18 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-10-23

HORIZON-CL4-2023-HUMAN-01-01: Efficient trustworthy AI - making t... ExpectedOutcome:Projects are expected to contribute to the following outcomes:

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18 Participantes

ATHENA - RESEARCH AND INNOVA...

916.88K€ | Lider

OHS Engineering GmbH

423.75K€ | Participante

S&D Consulting Europe S.r.l.

299.38K€ | Participante

SOUTH EAST TECHNOLOGICAL UNI...

515.63K€ | Participante

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Duración del proyecto: 44 meses Fecha Inicio: 2023-10-23
Fecha Fin: 2027-06-30

Líder del proyecto

ATHINAEREVNITIKO KENTRO KAINOTOMIAS STIS TECH... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 9M€

Descripción del proyecto 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.

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