Accelerating science with human-aware artificial intelligence | Nature Human Behaviour
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Nature Human Behaviour volume 7, pages 1682–1696 (2023)Cite this article 10k Accesses 72 Citations 134 Altmetric Metrics details Artificial intelligence (AI) models trained on published scientific findings have been used to invent valuable materials and targeted therapies, but they typically ignore the human scientists who continually alter the landscape of discovery. Here we show that incorporating the distribution of human expertise by training unsupervised models on simulated inferences that are cognitively accessible to experts dramatically improves (by up to 400%) AI prediction of future discoveries b
Data availability The DOIs of papers used for the electrochemical properties together with the PubMed identifiers of the MEDLINE entries used in our experiments can be found in our GitHub repository: https://github.com/jsourati/accelerate-discoveries. The abstracts of papers for electrochemical properties could not be shared due to copyright issues, but MEDLINE abstracts are accessible through their identifiers from the PubMed website. Source data are provided with this paper. Code availability All code for our algorithms can be found in the following GitHub repository:…
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