The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence
- URL: http://arxiv.org/abs/2307.07522v3
- Date: Tue, 29 Aug 2023 18:24:44 GMT
- Title: The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence
- Authors: Hector Zenil, Jesper Tegn\'er, Felipe S. Abrah\~ao, Alexander Lavin,
Vipin Kumar, Jeremy G. Frey, Adrian Weller, Larisa Soldatova, Alan R. Bundy,
Nicholas R. Jennings, Koichi Takahashi, Lawrence Hunter, Saso Dzeroski,
Andrew Briggs, Frederick D. Gregory, Carla P. Gomes, Jon Rowe, James Evans,
Hiroaki Kitano, Ross King
- Abstract summary: Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
- Score: 67.70415658080121
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in machine learning and AI, including Generative AI and LLMs,
are disrupting technological innovation, product development, and society as a
whole. AI's contribution to technology can come from multiple approaches that
require access to large training data sets and clear performance evaluation
criteria, ranging from pattern recognition and classification to generative
models. Yet, AI has contributed less to fundamental science in part because
large data sets of high-quality data for scientific practice and model
discovery are more difficult to access. Generative AI, in general, and Large
Language Models in particular, may represent an opportunity to augment and
accelerate the scientific discovery of fundamental deep science with
quantitative models. Here we explore and investigate aspects of an AI-driven,
automated, closed-loop approach to scientific discovery, including self-driven
hypothesis generation and open-ended autonomous exploration of the hypothesis
space. Integrating AI-driven automation into the practice of science would
mitigate current problems, including the replication of findings, systematic
production of data, and ultimately democratisation of the scientific process.
Realising these possibilities requires a vision for augmented AI coupled with a
diversity of AI approaches able to deal with fundamental aspects of causality
analysis and model discovery while enabling unbiased search across the space of
putative explanations. These advances hold the promise to unleash AI's
potential for searching and discovering the fundamental structure of our world
beyond what human scientists have been able to achieve. Such a vision would
push the boundaries of new fundamental science rather than automatize current
workflows and instead open doors for technological innovation to tackle some of
the greatest challenges facing humanity today.
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