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We invite you to join the PDW Machine Learning in Innovation Research: From Measurement to Causal Inference, scheduled on Friday Aug 9 2024, 8:00AM - 9:30AM, in Sheraton Ballroom I at AOM Chicago. More info below:
Machine learning has gained increasing attention owing to the latest advancements in natural language processing, especially those based on transformers, such as OpenAI's ChatGPT and Google's BERT. Transformers are large language models built upon deep learning neural networks, harnessing some key underlying components, notably attention modules and contextual word embeddings. This PDW features several presentations by leading scholars who will share their knowledge and research insights into the latest developments of machine learning applied to text data, and address some of the key challenges for measurement and causal inference. The PDW is scheduled to run for one hour and thirty minutes, consisting of two parts: Part 1, general introduction and guest speakers (70 minutes), and Part 2, Q&A session and open debate (20 minutes). The goal of the PDW is to raise awareness on how scholars can leverage machine learning in innovation research.The session is open to anyone interested. Registration and additional fees are not required.
Participants
Organizer: Mariano Mastrogiorgio - IE Business School
Organizer: Mario Daniele Amore - HEC Paris
Panelist: Sam Arts - KU Leuven
Panelist: Natalie Carlson - The Wharton School, U. of Pennsylvania
Panelist: Arianna Marchetti - London Business School
Panelist: Milan Miric - U. of Southern California -Marshall School of Business
Moderator: LI Liu - U. Carlos III de Madrid (UC3M)
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