While artificial intelligence has the potential to process vast amounts of
data, generate new insights, and unlock greater productivity, its widespread
adoption may entail unforeseen consequences. We identify conditions under which
AI, by reducing the cost of access to certain modes of knowledge, can
paradoxically harm public understanding. While large language models are
trained on vast amounts of diverse data, they naturally generate output towards
the 'center' of the distribution. This is generally useful, but widespread
reliance on recursive AI systems could lead to a process we define as
"knowledge collapse", and argue this could harm innovation and the richness of
human understanding and culture. However, unlike AI models that cannot choose
what data they are trained on, humans may strategically seek out diverse forms
of knowledge if they perceive them to be worthwhile. To investigate this, we
provide a simple model in which a community of learners or innovators choose to
use traditional methods or to rely on a discounted AI-assisted process and
identify conditions under which knowledge collapse occurs. In our default
model, a 20% discount on AI-generated content generates public beliefs 2.3
times further from the truth than when there is no discount. Finally, based on
the results, we consider further research directions to counteract such
outcomes.

通过降低获取知识成本，人工智能可能对公众理解产生逆效应，最终导致知识塌缩，损害创新和人类理解与文化的丰富性。我们提供一个简单模型来研究学习者或创新者选择使用传统方法还是依赖打折的人工智能辅助过程的条件，并发现人工智能生成内容的打折将使公众信仰与真实事实相差 2.3 倍。最后，我们考虑进一步的研究方向来对抗这种结果。

人工智能与知识崩溃问题

AI and the Problem of Knowledge Collapse

Given the growing importance of AI literacy, we decided to write this
tutorial to help narrow the gap between the discourse among those who study
language models -- the core technology underlying ChatGPT and similar products
-- and those who are intrigued and want to learn more about them. In short, we
believe the perspective of researchers and educators can add some clarity to
the public's understanding of the technologies beyond what's currently
available, which tends to be either extremely technical or promotional material
generated about products by their purveyors.
Our approach teases apart the concept of a language model from products built
on them, from the behaviors attributed to or desired from those products, and
from claims about similarity to human cognition. As a starting point, we (1)
offer a scientific viewpoint that focuses on questions amenable to study
through experimentation; (2) situate language models as they are today in the
context of the research that led to their development; and (3) describe the
boundaries of what is known about the models at this writing.

给定人工智能素养的重要性，本研究撰写了本教程，旨在帮助缩小学习语言模型（如 ChatGPT 等产品的核心技术）的研究者与对其感兴趣并希望了解更多的人之间的差距。总之，我们相信研究人员和教育工作者的观点可以增加公众对这些技术的理解，超越目前仅有的极其专业或由产品供应商生成的宣传材料。通过将语言模型概念与基于其构建的产品、产品所表现的行为以及与人类认知相似性的主张区分开来的方法，我们提供了（1）以实验研究为研究对象的科学观点；（2）将当前的语言模型置于其发展研究的背景中；（3）描述了目前对这些模型的已知范围。