Keynote: Obtaining Answers from Social Media Data
Content posted on social media is a rich set of data from which we would like to answer a broad set of questions. As one important example, keeping users safe on social networks requires that we identify content that contains misinformation or hateful speech and remove it. Similarly, users may be interested in getting additional value from their network such as finding friends who recently traveled to a particular destination or finding out which movies their friends are discussing. The typical machine learning approach in which we develop a model for every question we want to ask works well for questions we ask constantly (e.g., find hate speech), but not for ad-hoc questions that occur infrequently. In this talk I will advocate for an approach that combines the benefits of machine learning and database-style query answering. I will illustrate this approach through the idea of Neural Databases, a new kind of database system that leverages the strength of pre-trained language models to answer database queries over text and other modalities.
Bio
Alon Halevy has been a director at Facebook AI since 2019, where he works on Human Value Alignment and on the combination of neural and symbolic techniques for data management. Prior to Facebook, Alon was the CEO of Megagon Labs (2015-2018) and led the Structured Data Research Group at Google Research (2005-2015), where the team developed WebTables and Google Fusion Tables. From 1998 to 2005 he was a professor at the University of Washington, where he founded the database group. Alon is a founder of two startups, Nimble Technology and Transformic Inc. (acquired by Google in 2005). He received his Ph.D in Computer Science from Stanford in 1993. Alon co-authored two books: The Infinite Emotions of Coffee and Principles of Data Integration. He is a Fellow of the ACM and a recipient of the PECASE award and Sloan Fellowship. Together with his co-authors, he received VLDB 10-year best paper awards for the 2008 paper on WebTables and for the 1996 paper on the Information Manifold data integration system. In 2021, he received the Edgar F. Codd SIGMOD Innovations Award.