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.

翻译：本综述探讨了人工智能与深度学习在天文学领域的历史发展及未来前景。我们追溯了联结主义在天文学中的三次浪潮演进：从早期多层感知器的应用，到卷积与循环神经网络的兴起，直至当前无监督与生成式深度学习方法主导的时代。随着天文数据的指数级增长，深度学习技术为揭示重要洞见、攻克此前难以解决的问题提供了前所未有的机遇。在踏入天文学联结主义的预期第四次浪潮之际，我们主张采用面向天文应用微调的GPT类基础模型。此类模型可充分利用海量高质量多模态天文数据，服务于前沿的下游任务。为跟上大型科技公司驱动的技术进展，我们建议天文学界采用协作式开源方法，开发并维护这些基础模型，从而构建人工智能与天文的共生关系，充分发挥两个领域的独特优势。