Breakthrough discoveries and inventions involve unexpected combinations of contents including problems, methods, and natural entities, and also diverse contexts such as journals, subfields, and conferences. Drawing on data from tens of millions of research papers, patents, and researchers, we construct models that predict next year's content and context combinations with an AUC of 95% based on embeddings constructed from high-dimensional stochastic block models, where the improbability of new combinations itself predicts up to 50% of the likelihood that they will gain outsized citations and major awards. Most of these breakthroughs occur when problems in one field are unexpectedly solved by researchers from a distant other. These findings demonstrate the critical role of surprise in advance, and enable evaluation of scientific institutions ranging from education and peer review to awards in supporting it.

中文翻译：

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科技突飞猛进

突破性发现和发明涉及内容的意外组合，包括问题、方法和自然实体，以及各种背景，如期刊、子领域和会议。利用来自数千万篇研究论文、专利和研究人员的数据，我们构建了基于高维随机块模型构建的嵌入，以 95% 的 AUC 预测明年的内容和上下文组合的模型，其中新的概率为组合本身预测他们获得超额引用和主要奖项的可能性高达 50%。大多数这些突破发生在一个领域的问题被远方的研究人员意外解决时。这些发现提前证明了惊喜的关键作用，