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Proc Natl Acad Sci U S A. 2019 Feb 5;116(6):1870-1877. doi: 10.1073/pnas.1807185116.

Scaling up analogical innovation with crowds and AI.

Author information

Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA 15213;
Robert Bosch, Research and Technology Center, Division 3, Pittsburgh, PA 15222.
School of Computer Science and Engineering, The Hebrew University of Jerusalem, 9190401 Jerusalem, Israel.
College of Information Studies, University of Maryland, College Park, MD 20742.
Department of Information, Operations and Management Sciences, Leonard N. Stern School of Business, New York University, New York, NY 10012.
Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA 15213.


Analogy-the ability to find and apply deep structural patterns across domains-has been fundamental to human innovation in science and technology. Today there is a growing opportunity to accelerate innovation by moving analogy out of a single person's mind and distributing it across many information processors, both human and machine. Doing so has the potential to overcome cognitive fixation, scale to large idea repositories, and support complex problems with multiple constraints. Here we lay out a perspective on the future of scalable analogical innovation and first steps using crowds and artificial intelligence (AI) to augment creativity that quantitatively demonstrate the promise of the approach, as well as core challenges critical to realizing this vision.


AI; analogy; crowdsourcing; innovation; machine learning

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