AI alignment research is the field of study dedicated to ensuring that
artificial intelligence (AI) benefits humans. As machine intelligence gets more
advanced, this research is becoming increasingly important. Researchers in the
field share ideas across different media to speed up the exchange of
information. However, this focus on speed means that the research landscape is
opaque, making it difficult for young researchers to enter the field. In this
project, we collected and analyzed existing AI alignment research. We found
that the field is growing quickly, with several subfields emerging in parallel.
We looked at the subfields and identified the prominent researchers, recurring
topics, and different modes of communication in each. Furthermore, we found
that a classifier trained on AI alignment research articles can detect relevant
articles that we did not originally include in the dataset. We are sharing the
dataset with the research community and hope to develop tools in the future
that will help both established researchers and young researchers get more
involved in the field.

通过分析现有的 AI 对齐研究，我们发现领域正在迅速发展，并出现了几个子领域。我们查看了子领域并确定了突出的研究人员、经常出现的话题和每种不同的交流方式。此外，我们发现，一个分类器可以检测出 AI 对齐研究文章中没有最初包含在数据集中的相关文章。我们希望向研究社区共享数据集，并希望未来能够开发工具来帮助既有研究人员和年轻的研究人员更多地参与这一领域。

研究对齐研究：无监督分析

Researching Alignment Research: Unsupervised Analysis

We investigate the community structure of physics subfields in the citation
network of all Physical Review publications between 1893 and August 2007. We
focus on well-cited publications (those receiving more than 100 citations), and
apply modularity maximization to uncover major communities that correspond to
clearly-identifiable subfields of physics. While most of the links between
communities connect those with obvious intellectual overlap, there sometimes
exist unexpected connections between disparate fields due to the development of
a widely-applicable theoretical technique or by cross fertilization between
theory and experiment. We also examine communities decade by decade and also
uncover a small number of significant links between communities that are widely
separated in time.

本文研究了《Physical Review》出版物在 1893 年至 2007 年 8 月之间的引文网络中物理学子领域的社区结构。作者使用模块度最大化来发现明确定义的物理学子领域所对应的重要社区，同时也考察了不同年代之间社区的联系。