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

doi:10.1073/pnas.1807185116

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

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Contribution to journal › Article › peer-review

41 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 language	American English
Pages (from-to)	1870-1877
Number of pages	8
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	116
Issue number	6
DOIs	https://doi.org/10.1073/pnas.1807185116
State	Published - 2019

Bibliographical note

Funding Information:
The authors thank the Arthur M. Sackler Colloquium on Creativity and Collaboration: Revisiting Cybernetic Serendipity and National Academy of Sciences (NSF) community for their support and helpful feedback. This work was supported by NSF Grants CHS-1526665, IIS-1149797, IIS-1816242, IIS-1217559, and CMMI-1535539; Carnegie Mellon's Web2020 initiative; Bosch; Google; Israel Science Foundation Grant 1764/15; an Alon grant; the Hebrew University of Jerusalem Cyber Security Center in conjunction with the Israel National Cyber Bureau in the Prime Minister's Office; and the Industrial Research Institute.

Funding Information:
ACKNOWLEDGMENTS. The authors thank the Arthur M. Sackler Colloquium on Creativity and Collaboration: Revisiting Cybernetic Serendipity and National Academy of Sciences (NSF) community for their support and helpful feedback. This work was supported by NSF Grants CHS-1526665, IIS-1149797, IIS-1816242, IIS-1217559, and CMMI-1535539; Carnegie Mellon’s Web2020 initiative; Bosch; Google; Israel Science Foundation Grant 1764/15; an Alon grant; the Hebrew University of Jerusalem Cyber Security Center in conjunction with the Israel National Cyber Bureau in the Prime Minister’s Office; and the Industrial Research Institute.

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

Keywords

AI
Analogy
Crowdsourcing
Innovation
Machine learning

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10.1073/pnas.1807185116

Cite this

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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.",

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note = "Funding Information: The authors thank the Arthur M. Sackler Colloquium on Creativity and Collaboration: Revisiting Cybernetic Serendipity and National Academy of Sciences (NSF) community for their support and helpful feedback. This work was supported by NSF Grants CHS-1526665, IIS-1149797, IIS-1816242, IIS-1217559, and CMMI-1535539; Carnegie Mellon's Web2020 initiative; Bosch; Google; Israel Science Foundation Grant 1764/15; an Alon grant; the Hebrew University of Jerusalem Cyber Security Center in conjunction with the Israel National Cyber Bureau in the Prime Minister's Office; and the Industrial Research Institute. Funding Information: ACKNOWLEDGMENTS. The authors thank the Arthur M. Sackler Colloquium on Creativity and Collaboration: Revisiting Cybernetic Serendipity and National Academy of Sciences (NSF) community for their support and helpful feedback. This work was supported by NSF Grants CHS-1526665, IIS-1149797, IIS-1816242, IIS-1217559, and CMMI-1535539; Carnegie Mellon{\textquoteright}s Web2020 initiative; Bosch; Google; Israel Science Foundation Grant 1764/15; an Alon grant; the Hebrew University of Jerusalem Cyber Security Center in conjunction with the Israel National Cyber Bureau in the Prime Minister{\textquoteright}s Office; and the Industrial Research Institute. Publisher Copyright: {\textcopyright} 2019 National Academy of Sciences. All Rights Reserved.",

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Scaling up analogical innovation with crowds and AI

Abstract

Bibliographical note

Keywords

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