Astronomia ex machina: a history, primer and outlook on neural networks in astronomy

Michael J. Smith, James E. Geach

Research output: Contribution to journalReview articlepeer-review

67 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number221454
Pages (from-to)1-53
Number of pages53
JournalRoyal Society Open Science
Volume10
Issue number5
Early online date31 May 2023
DOIs
Publication statusPublished - 31 May 2023

Keywords

  • astrophysics
  • machine learning
  • neural networks

Fingerprint

Dive into the research topics of 'Astronomia ex machina: a history, primer and outlook on neural networks in astronomy'. Together they form a unique fingerprint.

Cite this