Scaling Up Analogical Innovation with Crowds and AI
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
Year of publication: |
2021
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Authors: | Kittur, Aniket ; Yu, Lixiu ; hope, tom ; Lifshitz-Assaf, Hila ; gilon, karni ; Ng, Felicia ; Kraut, Robert E. ; shachaf, dafna |
Publisher: |
[S.l.] : SSRN |
Description of contents: | Abstract [papers.ssrn.com] |
Saved in:
Extent: | 1 Online-Ressource |
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Series: | |
Type of publication: | Book / Working Paper |
Language: | English |
Notes: | Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments February 21, 2019 erstellt Volltext nicht verfügbar |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014107945
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