论文标题
天文学Ex Machina：天文学中神经网络的历史，底漆和前景
Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy
论文作者
论文摘要
在这篇综述中，我们探讨了人工智能（AI）和天文学深入学习的历史发展和未来前景。我们通过其三波浪潮（从多层观察者的早期使用，到卷积和复发性神经网络的兴起，最后是当前无人监督和生成的深度学习方法的时代），追溯了天文学中连接主义的演变。随着天文数据的指数增长，深度学习技术提供了前所未有的机会，可以揭示有价值的见解并解决以前棘手的问题。当我们进入预期的天文连接主义第四波时，我们主张采用类似GPT的基础模型，用于天文应用。这样的模型可以利用大量高质量的多模式天文数据来服务于最新的下游任务。为了跟上大型技术驱动的进步，我们建议在天文学界进行协作，开源方法，以发展和维护这些基础模型，从而促进了AI和天文学之间的共生关系，以利用这两个领域的独特优势。
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.
