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Structured Physics-Inspired Representations and dAta models for efficient Learning

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Structured Physics-Inspired Representations and dAta models for efficient Learning 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 architec

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recordType

award

status

SIGNED

region

EU

value

193643.28

unit

EUR