Closing the Machine Learning Gap with Jacob Andreas

Jacob Andreas is interested in language as a communicative and computational tool. People learn to understand and generate novel utterances from remarkably little data. Having learned language, we use it acquire new ideas and to structure our reasoning. Current machine learning techniques fall short of human abilities in both their capacity to learn language and learn from language about the rest of the world. His research aims to (1) understand the computational mechanisms that make efficient language learning possible, and (2) build general-purpose intelligent systems that can communicate effectively with humans and learn from human guidance.

Jacob is an assistant professor at MIT in EECS and CSAIL. he did his PhD work at Berkeley, where he was a member of the Berkeley NLP Group and the Berkeley AI Research Lab. He also spent time with the Cambridge NLIP Group, and the Center for Computational Learning Systems and NLP Group at Columbia.