  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.
