Many world-changing breakthroughs in science and technology were enabled by analogical transfer, as ideas from one domain were used to solve a problem in another. Observing water led the Greek philosopher Chrysippus to speculate that sound was a wave phenomenon; an analogy to twisting a cardboard box allowed the Wright brothers to design a steerable aircraft.
Despite its value for innovation, very little progress has been made towards automating the process of analogy-finding in real-world settings, and the problem has maintained a longstanding status as a “holy grail" in artificial intelligence.
The goal of this project is to tackle head-on this important problem and develop principled tools for automatically discovering analogies in large, unstructured, natural-language datasets such as patents and scientific papers. Such tools could revolutionize a variety of fields, allowing scientists and inventors to retrieve useful content based on deep structural similarity rather than simple keywords.
In particular, this project explores the multiple roles AI and machine learning can play in the analogical innovation pipeline. In ideation experiments, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas.
Best Research Paper and Best Student Research Paper, KDD'17