Abstract: 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 to find analogies. In ideation experiments, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas.

Bio: Dafna Shahaf is an Associate Professor in computer science at the Hebrew University of Jerusalem. Dafna's research focuses on helping people make sense of massive amounts of data, with a special emphasis on unlocking the potential of the many digital traces left by human activity to contribute to our understanding (and computer emulation) of human capacities such as humor and creativity. She received her PhD from Carnegie Mellon University, and was a postdoctoral fellow at Stanford University and at Microsoft Research. Prof. Shahaf has won multiple awards, including best research paper awards at KDD 2010 and KDD 2017, an ERC starting grant, a Microsoft Research Fellowship, a Siebel Scholarship, a Magic Grant for innovative ideas and Wolf's Foundation Krill Award, as well as MIT Tech Review's "Most thought-provoking paper of the week".