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Recent advances in machine learning and AI, including Generative AI and LLMs,are disrupting technological innovation, product development, and society as awhole. AI's contribution to technology can come from multiple approaches thatrequire access to large training data sets and clear performance evaluationcriteria, ranging from pattern recognition and classification to generativemodels. Yet, AI has contributed less to fundamental science in part becauselarge data sets of high-quality data for scientific practice and modeldiscovery are more difficult to access. Generative AI, in general, and LargeLanguage Models in particular, may represent an opportunity to augment andaccelerate the scientific discovery of fundamental deep science withquantitative models. Here we explore and investigate aspects of an AI-driven,automated, closed-loop approach to scientific discovery, including self-drivenhypothesis generation and open-ended autonomous exploration of the hypothesisspace. Integrating AI-driven automation into the practice of science wouldmitigate current problems, including the replication of findings, systematicproduction of data, and ultimately democratisation of the scientific process.Realising these possibilities requires a vision for augmented AI coupled with adiversity of AI approaches able to deal with fundamental aspects of causalityanalysis and model discovery while enabling unbiased search across the space ofputative explanations. These advances hold the promise to unleash AI'spotential for searching and discovering the fundamental structure of our worldbeyond what human scientists have been able to achieve. Such a vision wouldpush the boundaries of new fundamental science rather than automatize currentworkflows and instead open doors for technological innovation to tackle some ofthe greatest challenges facing humanity today.

The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence