This paper presents three established theories of human decision-making and describes how they can be integrated to provide a model of purposive human action. Taking seriously the idea of language as action the model is then applied to the conversational user interfaces. Theory based AI research has had a hard time recently and the aim here is to revitalise interest in understanding what LLMs are actually doing other than running poorly understood machine learning routines over all the data the relevant Big Tech company can hoover up. When a raspberry pi computer for under 50USD is up to 400 times faster than the first commercial Cray super computer~\cite{crayVpi}, Big Tech can get really close to having an infinite number of monkeys typing at random and producing text, some of which will make sense. By understanding where ChatGPT's apparent intelligence comes from, perhaps we can perform the magic with fewer resources and at the same time gain some understanding about our relationship with our world.

翻译：本文提出了三种关于人类决策的成熟理论，并阐释了如何将其整合为一个有目的的人类行为模型。在认真对待"语言即行动"这一理念的基础上，该模型进一步被应用于对话式用户界面。近年来，基于理论的人工智能研究面临重重困难，本文旨在重新激发学界关注：除了通过扫描相关大型科技公司所能收集的全部数据、运行那些理解尚浅的机器学习程序之外，大语言模型究竟在做什么？当一台售价不足50美元的树莓派计算机的性能已比首台商用克雷超级计算机~\cite{crayVpi}快上400倍时，大型科技公司几乎可以实现"无限猴子定理"的电子版——随机敲击生成文本，其中总会有合乎逻辑的内容。通过理解ChatGPT表面智能的来源，或许我们能用更少的资源完成这种"魔法"，并同时加深对人类与世界关系的认识。