Advanced artificial intelligence (AI) systems with access to millions of
research papers could inspire new research ideas that may not be conceived by
humans alone. However, how interesting are these AI-generated ideas, and how
can we improve their quality? Here, we introduce SciMuse, a system that uses an
evolving knowledge graph built from more than 58 million scientific papers to
generate personalized research ideas via an interface to GPT-4. We conducted a
large-scale human evaluation with over 100 research group leaders from the Max
Planck Society, who ranked more than 4,000 personalized research ideas based on
their level of interest. This evaluation allows us to understand the
relationships between scientific interest and the core properties of the
knowledge graph. We find that data-efficient machine learning can predict
research interest with high precision, allowing us to optimize the
interest-level of generated research ideas. This work represents a step towards
an artificial scientific muse that could catalyze unforeseen collaborations and
suggest interesting avenues for scientists.

使用超过 58 百万科学论文构建的知识图谱，并结合 GPT-4 接口生成个性化研究想法的 SciMuse 系统，通过与马克斯・普朗克研究所的 100 多位研究团队领导的大规模人工评估，我们发现数据高效的机器学习可以高精度预测研究兴趣，从而优化产生的研究想法的兴趣水平。这项工作是朝着一种人工科学启发的方向迈进，可以催生意料之外的合作，并为科学家提供有趣的研究方向。