Project description
Synthetic data for AI development
In recent years, critical advances in AI technology and production have led to the widespread adoption of AI solutions for a diverse range of tasks and operations. Unfortunately, the dependency of AI solutions on acquiring and studying data, which was often private, biased, inaccurate or irrelevant, led to a variety of problems. Synthetic data has emerged as a solution. Artificial data that mimics real-world data doesn’t risk privacy breaches and can be freely processed. The EIC-funded SydAi project will develop a synthetic data platform that provides AI companies and solutions with high-quality, privacy-preserving synthetic data, enabling faster, more ethical and efficient AI innovation and evolution.
Objective
The age of the artificial intelligence (AI) revolution has arrived, bringing with it numerous opportunities across key industries, including healthcare, banking, and retail. Recent studies indicate that AI has the potential to increase global GDP by EUR14.03 trillion by 2030. However, the effectiveness of AI systems has thus far been limited by a lack of access to high-quality data without infringing on privacy rights. Poor data quality—characterized by incompleteness, inaccuracies, outdated information, or irrelevance—can lead to biased predictions and erroneous decisions, affecting up to 80% of AI projects.
To address these issues, synthetic data has emerged as a potential solution. Synthetic data is artificially generated information that mirrors the statistical properties of real-world data. This data can be freely processed and used in AI model development without risking privacy breaches. However, until synthetic data can overcome its current limitations, its use remains restricted. These limitations include avoiding biases present in the original data, generating synthetic data that mimics very complex relational databases, and effectively satisfying deterministic rules.
At Aindo, we have developed SyDAi, a novel synthetic data platform that empowers companies to generate high-quality and high-privacy synthetic data. This breakthrough aims to democratize and accelerate AI innovation, enabling all industries to actively participate in the AI revolution. Unlike many existing solutions that focus on specific data types or lack the ability to handle certain statistical dependencies, our platform can generate synthetic data with advanced data formats. The innovation of this innovation has recently led to SyDAi being recognized independently by the United States National Institute of Standards and Technology (NIST) as best-in-class for optimizing the privacy-fidelity trade-off.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences databases
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.3.1 - The European Innovation Council (EIC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-EIC-ACC - HORIZON EIC Accelerator
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-EIC-2024-ACCELERATOR-02
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
34149 Trieste
Italy
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.