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.

翻译：能够访问数百万篇研究论文的先进人工智能系统，可以激发人类单独难以构想的新研究思路。然而，这些由AI生成的创意究竟有多大的吸引力？我们又该如何提升其质量？本文介绍了SciMuse系统，该系统利用一个从超过5800万篇科学文献构建的动态演化知识图谱，并通过GPT-4接口生成个性化的研究创意。我们与来自马克斯·普朗克学会的100多位研究团队负责人进行了大规模人工评估，他们对超过4000个个性化研究创意依据其感兴趣程度进行了排序。此项评估使我们得以理解科学兴趣与知识图谱核心属性之间的关系。我们发现，数据高效的机器学习方法能够以高精度预测研究兴趣，从而使我们能够优化所生成研究创意的吸引力水平。这项工作标志着向人工科学缪斯迈出了一步，它有望催化意想不到的科研合作，并为科学家们提出具有吸引力的研究方向。