Accelerating science with human-aware artificial intelligence
This work addresses the challenge of accelerating scientific progress for researchers by moving beyond content-focused AI to include human cognitive factors, though it appears incremental in its approach.
The paper tackles the problem of AI models ignoring human expertise in scientific discovery by incorporating simulated expert inferences, resulting in up to 400% improvement in predicting future discoveries, especially in sparse literature areas.
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.