MSI Webinar: InnoVAE: Generative AI for Understanding Patents and Innovation
Lack of interpretability limits the use of common unsupervised machine learning techniques in contexts where they are meant to augment managerial decision-making. DK Lee will present a generative deep learning model based on a Variational AutoEncoder (“InnoVAE”) that converts unstructured text about patents into an interpretable spatial representation of innovation space to enable high-resolution exploration at the patent, innovation, and firm level. The same methodology can be applied to various multi-modal business objects such as companies (strategy space), jobs (skill space), products (feature space), and more.
Dokyun “DK” Lee is a Kelli Questrom Chair Associate Professor of Information Systems and Digital Business Fellow at Questrom School of Business with a secondary and fellow appointment at Computing & Data Science School. He studies the responsible application, development, and impact of AI in e-commerce and the digital economy with a focus on text and thus runs the Business Insights through Text Lab (www.dkBITLAB.com). Specific interests are: 1. Content extracting, understanding, and engineering 2. Generative AI 3. Quantifying economic impact of unstructured data 4. Unintended consequence of AI in business in the context of social media, advertising, user-generated data, e-commerce, digital consumer management, human-ai collaboration, and innovation. He is a recipient of ISS Gordon B David Young Scholar and Marketing Science Institute Young Scholar Awards. His research has been published in journals such as Management Science, Information Systems Research, Journal of Marketing Research, AAAI, AIES, and WWW. His work is supported by organizations such as Adobe, Bosch Institute, Google Cloud, Marketing Science Institute, McKinsey & Co, Nvidia, and Net Institute.