In recent years, deep learning has infiltrated every field it has touched,
reducing the need for specialist knowledge and automating the process of
knowledge discovery from data. This review argues that astronomy is no
different, and that we are currently in the midst of a deep learning revolution
that is transforming the way we do astronomy. We trace the history of
astronomical connectionism from the early days of multilayer perceptrons,
through the second wave of convolutional and recurrent neural networks, to the
current third wave of self-supervised and unsupervised deep learning. We then
predict that we will soon enter a fourth wave of astronomical connectionism, in
which finetuned versions of an all-encompassing 'foundation' model will replace
expertly crafted deep learning models. We argue that such a model can only be
brought about through a symbiotic relationship between astronomy and
connectionism, whereby astronomy provides high quality multimodal data to train
the foundation model, and in turn the foundation model is used to advance
astronomical research.

该文讨论深度学习在天文学领域的应用，以及在天文数据训练深度学习模型方面的优势，并预测将会进入第四波基础模型迭代的天文连接主义时代。

机器天文学：神经网络在天文学中的历史、基础知识与展望

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

This paper reviews the historical development of AI and representative
philosophical thinking from the perspective of the research paradigm.
Additionally, it considers the methodology and applications of AI from a
philosophical perspective and anticipates its continued advancement. In the
history of AI, Symbolism and connectionism are the two main paradigms in AI
research. Symbolism holds that the world can be explained by symbols and dealt
with through precise, logical processes, but connectionism believes this
process should be implemented through artificial neural networks. Regardless of
how intelligent machines or programs should achieve their smart goals, the
historical development of AI demonstrates the best answer at this time. Still,
it is not the final answer of AI research.

本文从研究范式的角度回顾了人工智能技术和代表性的哲学思想的历史发展，并从哲学视角探讨了人工智能的方法论和应用，预期其不断进步。Symbolism 和 connectionism 是 AI 研究中的两个主要范式。本文指出，不管智能机器或程序如何实现其智能目标，人工智能的历史发展证明了当前最好的答案，但这并不是 AI 研究的最终答案.

人工智能与哲学的历史交互

A Historical Interaction between Artificial Intelligence and Philosophy

Pater's target article builds a persuasive case for establishing stronger
ties between theoretical linguistics and connectionism (deep learning). This
commentary extends his arguments to semantics, focusing in particular on issues
of learning, compositionality, and lexical meaning.

该论文提出建立理论语言学和连接主义（深度学习）之间更紧密的联系的有力论据。通过扩展他的论点来关注语义学、学习、组合性和词汇含义等问题。