Scaling up analogical innovation with crowds and AI

Aniket Kittur*, Lixiu Yu, Tom Hope, Joel Chan, Hila Lifshitz-Assaf, Karni Gilon, Felicia Ng, Robert E. Kraut, Dafna Shahaf

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

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.

Original languageAmerican English
Pages (from-to)1870-1877
Number of pages8
JournalProceedings of the National Academy of Sciences of the United States of America
Volume116
Issue number6
DOIs
StatePublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 National Academy of Sciences. All Rights Reserved.

Keywords

  • AI
  • Analogy
  • Crowdsourcing
  • Innovation
  • Machine learning

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