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Astronomia ex machina: a history, primer and outlook on neural networks in astronomy
In this review, we explore the historical development and future prospects of artificial intelligence (AI) and deep learning in astronomy. We trace the evolution of connectionism in astronomy through its three waves, from the early use of multilayer perceptrons, to the rise of convolutional and recu...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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The Royal Society
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10230190/ https://www.ncbi.nlm.nih.gov/pubmed/37266039 http://dx.doi.org/10.1098/rsos.221454 |
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author | Smith, Michael J. Geach, James E. |
author_facet | Smith, Michael J. Geach, James E. |
author_sort | Smith, Michael J. |
collection | PubMed |
description | In this review, we explore the historical development and future prospects of artificial intelligence (AI) and deep learning in astronomy. We trace the evolution of connectionism in astronomy through its three waves, from the early use of multilayer perceptrons, to the rise of convolutional and recurrent neural networks, and finally to the current era of unsupervised and generative deep learning methods. With the exponential growth of astronomical data, deep learning techniques offer an unprecedented opportunity to uncover valuable insights and tackle previously intractable problems. As we enter the anticipated fourth wave of astronomical connectionism, we argue for the adoption of GPT-like foundation models fine-tuned for astronomical applications. Such models could harness the wealth of high-quality, multimodal astronomical data to serve state-of-the-art downstream tasks. To keep pace with advancements driven by Big Tech, we propose a collaborative, open-source approach within the astronomy community to develop and maintain these foundation models, fostering a symbiotic relationship between AI and astronomy that capitalizes on the unique strengths of both fields. |
format | Online Article Text |
id | pubmed-10230190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-102301902023-06-01 Astronomia ex machina: a history, primer and outlook on neural networks in astronomy Smith, Michael J. Geach, James E. R Soc Open Sci Astronomy In this review, we explore the historical development and future prospects of artificial intelligence (AI) and deep learning in astronomy. We trace the evolution of connectionism in astronomy through its three waves, from the early use of multilayer perceptrons, to the rise of convolutional and recurrent neural networks, and finally to the current era of unsupervised and generative deep learning methods. With the exponential growth of astronomical data, deep learning techniques offer an unprecedented opportunity to uncover valuable insights and tackle previously intractable problems. As we enter the anticipated fourth wave of astronomical connectionism, we argue for the adoption of GPT-like foundation models fine-tuned for astronomical applications. Such models could harness the wealth of high-quality, multimodal astronomical data to serve state-of-the-art downstream tasks. To keep pace with advancements driven by Big Tech, we propose a collaborative, open-source approach within the astronomy community to develop and maintain these foundation models, fostering a symbiotic relationship between AI and astronomy that capitalizes on the unique strengths of both fields. The Royal Society 2023-05-31 /pmc/articles/PMC10230190/ /pubmed/37266039 http://dx.doi.org/10.1098/rsos.221454 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Astronomy Smith, Michael J. Geach, James E. Astronomia ex machina: a history, primer and outlook on neural networks in astronomy |
title | Astronomia ex machina: a history, primer and outlook on neural networks in astronomy |
title_full | Astronomia ex machina: a history, primer and outlook on neural networks in astronomy |
title_fullStr | Astronomia ex machina: a history, primer and outlook on neural networks in astronomy |
title_full_unstemmed | Astronomia ex machina: a history, primer and outlook on neural networks in astronomy |
title_short | Astronomia ex machina: a history, primer and outlook on neural networks in astronomy |
title_sort | astronomia ex machina: a history, primer and outlook on neural networks in astronomy |
topic | Astronomy |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10230190/ https://www.ncbi.nlm.nih.gov/pubmed/37266039 http://dx.doi.org/10.1098/rsos.221454 |
work_keys_str_mv | AT smithmichaelj astronomiaexmachinaahistoryprimerandoutlookonneuralnetworksinastronomy AT geachjamese astronomiaexmachinaahistoryprimerandoutlookonneuralnetworksinastronomy |