Imitation versus Innovation: What children can do that large language and language-and-vision models cannot (yet)?

Abstract

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.

Testing Theory of Mind in GPT Models and Humans

“…This is challenging for minds that have their own perspective, their own sense of self, and their own ability to track out-of-sight objects. However, if a mind does not have its own self-perspective because it does not need to navigate a body through an environment, as with GPT 31 , then tracking the belief of a character in a story does not pose the same challenge.…”