Project description
Solution for efficient and sustainable machine learning
The rapid growth and diversity of advancements in AI and machine learning have driven widespread adoption of AI technologies and the development of many Large Language Models, such as ChatGPT. While ChatGPT is widely used, it demands immense computational power and energy, highlighting challenges around efficiency and sustainability in these systems. Supported by the Marie Skłodowska-Curie Actions programme, the SPIRAL project aims to develop structured, targeted machine learning models that are both more efficient and easier to interpret. The project will deepen understanding of how structured data is processed and internally interpreted, while optimising current architectures for improved training efficiency and sustainability.
Objective
Artificial Intelligence (AI) is set to revolutionize technology and society. While the fast improvement and adoption of AI comes with tremendous potential, it also brings significant challenges. The rise of so-called ‘Large Language Models’ such as ChatGPT, which require tremendous computational resources, notably highlights the need for more efficient architectures to address sustainability and sovereignty concerns.
In this context, the SPIRAL project—Structured Physics-Inspired Representations and dAta models for efficient Learning—aims to create more interpretable, efficient, and targeted machine learning models by focusing on the role of structure. Indeed, despite advancements in Machine Learning research, the field has yet to fully understand how models process and build internal representations from structured data. SPIRAL seeks to close this gap by identifying how current architectures use structure in data and subsequently by developing better training protocols and tailored architectures, opening an alternative to the blind increase of model size and complexity.
To achieve its goals, SPIRAL will take a dual, physics-inspired approach. First, it will establish an in-silico laboratory to explore the role of structure in data by developing a tunable model of synthetic structured data and conducting targeted experiments on modern machine learning architectures. Second, it will leverage insights from the statistical physics of disordered systems to understand the emergent structure in the solution landscape of asymmetric neural networks, ultimately proposing innovative architectures that make use of this internal structure to build meaningful internal representations of data.
This interdisciplinary strategy, developed within the fast-growing Bocconi Institute for Data Science and Analytics, will provide new methods for building efficient models aligning with Europe’s goals of sustainability and technological independence.
Fields of science (EuroSciVoc)
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.
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.
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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.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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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-MSCA-2024-PF-01
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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.
20136 Milano
Italy
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.