 In this paper, the authors examine the history and potential of artificial intelligence, AI, and deep learning in astronomy. They trace the evolution of connectionism in astronomy through three distinct phases, multilayer perceptron, MLP, convolutional neural networks, CNN, and generative adversarial networks, GAM. The authors then discuss how the exponential growth of astronomical data has enabled the emergence of new opportunities for AI and deep learning. Finally, they suggest the adoption of GPT-like foundation models for astronomical applications, which can be used to solve complex problems and generate insights from large datasets. The authors also propose a collaborative, open-source approach to develop and maintain these foundation models, which will benefit both AI and astronomy. This article was authored by Michael J. Smith and James E. Geach.