  With the recent rise of widely successful deep learning models, there is
emerging interest among professionals in various math and science communities
to see and evaluate the state-of-the-art models' abilities to collaborate on
finding or solving problems that often require creativity and thus
brainstorming. While a significant chasm still exists between current
human-machine intellectual collaborations and the resolution of complex math
and science problems, such as the six unsolved Millennium Prize Problems, our
initial investigation into this matter reveals a promising step towards
bridging the divide. This is due to the recent advancements in Large Language
Models (LLMs). More specifically, we conduct comprehensive case studies to
explore both the capabilities and limitations of the current state-of-the-art
LLM, notably GPT-4, in collective brainstorming with humans.
