Imitation versus Innovation: What children can do that large language
  and language-and-vision models cannot (yet)?
Eunice Yiu Eliza Kosoy Alison Gopnik
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.