Original 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

Research Findings

Published in August 2019 by Professor Joel Chan, Professor Aniket Kittur, and colleagues:

Scaling up analogical innovation with crowds and AI


Disruptive ideas largely stem from two contrasting approaches, research shows

Chinarut's Intentions

Possible Focus Areas

Request for Collaboration & Synthesis

Concepts in Chinarut's words