Much discussion about large language models and language-and-vision models
has focused on whether these models are intelligent agents. We present an
alternative perspective. We argue that these artificial intelligence models are
cultural technologies that enhance cultural transmission in the modern world,
and are efficient imitation engines. We explore what AI models can tell us
about imitation and innovation by evaluating their capacity to design new tools
and discover novel causal structures, and contrast their responses with those
of human children. Our work serves as a first step in determining which
particular representations and competences, as well as which kinds of knowledge
or skill, can be derived from particular learning techniques and data.
Critically, our findings suggest that machines may need more than large scale
language and images to achieve what a child can do.

本文论述大型语言模型和语言 - 视觉模型是一种人工智能文化技术，它们通过增强文化传播和成为高效的模仿引擎来展现出其重要性。文章探讨了这些模型在 “模仿” 和 “创新” 方面的作用，以及它们的表现与人类儿童相比的异同，旨在深入研究从特定学习技术和数据中获取哪些具体的抽象表示、能力以及哪些具体的知识或技能。关键发现表明，机器可能需要比语言和图像的大规模更多的元素才能做到像儿童一样具备的能力。