Duration: 1:17:54

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The explosion of available idea repositories — scientific papers, patents, product descriptions — represents an unprecedented opportunity to accelerate innovation and lead to a wealth of discoveries. Given the scale of the problem and its ever-expanding nature, there is a need for intelligent automation to assist in the process of discovery. In this talk, I will present our work toward addressing this challenging problem.

We developed an approach for boosting people’s creativity by helping them discover analogies — abstract structural connections between ideas. We learn to decompose innovation texts into functional models that describe the components and goals of inventions, and use them to build a search engine supporting expressive inspiration queries. In ideation studies, our inspirations helped people generate better ideas with significant improvement over standard search. We also construct a commonsense ontology of purposes and mechanisms of products, mapping the landscape of ideas.

I will also describe a novel machine learning framework we developed in order to identify innovation in patents, where labels are extremely hard to obtain. In our setting, called Ballpark Learning, we are only given groups of instances with coarse constraints over label averages. We demonstrate encouraging results in classification and regression tasks across several domains.

Talk slides: https://www.microsoft.com/en-us/research/uploads/prod/2019/09/Secure-Interpretable-AI.pdf

Learn more about this and other talks at Microsoft Research: https://www.microsoft.com/en-us/research/video/towards-secure-and-interpretable-ai-scalable-methods-interactive-visualizations-and-practical-tools/