| Literature DB >> 30718420 |
Aniket Kittur1, Lixiu Yu2, Tom Hope3, Joel Chan4, Hila Lifshitz-Assaf5, Karni Gilon3, Felicia Ng6, Robert E Kraut6, Dafna Shahaf3.
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.Entities:
Keywords: AI; analogy; crowdsourcing; innovation; machine learning
Year: 2019 PMID: 30718420 PMCID: PMC6369801 DOI: 10.1073/pnas.1807185116
Source DB: PubMed Journal: Proc Natl Acad Sci U S A ISSN: 0027-8424 Impact factor: 11.205