Young children’s learning may be an important model for artificial intelligence (AI). Comparing children and artificial agents in the same tasks and environments can help us understand the abilities of existing systems and create new ones. In particular, many current large data-supervised systems, such as large language models (LLMs), provide new ways to access information collected by past agents. However, they lack the kinds of exploration and innovation that are characteristic of children. New techniques may help to instantiate childlike curiosity, exploration and play in AI systems.
Speaker: Alison Gopnik, UC Berkeley
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