Dafna Shahaf - COLLOQUIUM LECTURE
Tuesday, 29.5.2018, 14:30
The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods.
In this work we explore the viability and value of learning simpler structural representations which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas.
Dafna Shahaf is an Assistant Professor in the School of Computer Science and Engineering at the Hebrew University of Jerusalem. Her research is about making sense of massive amounts of data. She designs algorithms that help people connect the dots between pieces of information and turn data into insight. She is especially interested in unlocking the potential of the many digital traces left by human activity to understand and emulate human characteristics (e.g., creativity). Her work has received multiple awards, including Best Research Paper at KDD'17 and KDD’10 and the IJCAI Early Career Award. She received her Ph.D. in Computer Science from Carnegie Mellon University. Prior to joining the Hebrew University, she was a postdoctoral fellow at Microsoft Research and Stanford University.