Recommendation and Learning to Improve Personal Productivity

Artificial intelligence has the potential to improve productivity throughout the workplace by leveraging how people communicate to proactively connect a person to the right people and information. Providing this benefit requires key system capabilities, including understanding how language in communications relates to the actions people take, how behavioral traces can be used to measure personal productivity, how we can make recommendations from the personal web, and—critical to all of these—how we can learn from each person’s data in a privacy-preserving way. This breakout session will consist of 10-minute talks to review recent progress in related areas and a panel discussion on how research can address the challenges in this arena. We will explore how machine learning methods can be applied to customer-level data to improve personalization and facilitate productivity without sacrificing privacy and address such technical issues as data sources/feedback, modeling objectives, and accurate evaluation.

Séries:
Microsoft Research Faculty Summit
Date:
Haut-parleurs:
Paul Bennett, Jennifer Neville, Chris Re, Yejin Choi
Affiliation:
Microsoft Research, Purdue University, Stanford University, University of Washington

Taille: Microsoft Research Faculty Summit