Accelerating science with human-aware artificial intelligence
- URL: http://arxiv.org/abs/2306.01495v1
- Date: Fri, 2 Jun 2023 12:43:23 GMT
- Title: Accelerating science with human-aware artificial intelligence
- Authors: Jamshid Sourati, James Evans
- Abstract summary: We show that incorporating the distribution of human expertise by training unsupervised models dramatically improves (up to 400%) AI prediction of future discoveries.
These models succeed by predicting human predictions and the scientists who will make them.
Accelerating human discovery or probing its blind spots, human-aware AI enables us to move toward and beyond the contemporary scientific frontier.
- Score: 2.7786142348700658
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 cognitively accessible
to experts dramatically improves (up to 400%) AI prediction of future
discoveries beyond those focused on research content alone, especially when
relevant literature is sparse. These models succeed by predicting human
predictions and the scientists who will make them. By tuning human-aware AI to
avoid the crowd, we can generate scientifically promising "alien" hypotheses
unlikely to be imagined or pursued without intervention until the distant
future, which hold promise to punctuate scientific advance beyond questions
currently pursued. Accelerating human discovery or probing its blind spots,
human-aware AI enables us to move toward and beyond the contemporary scientific
frontier.
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