Artificial Intelligence Group
Welcome to the Artificial Intelligence Group
The Artificial Intelligence (AI) Group at the David R. Cheriton School of Computer Science conducts research in many areas of artificial intelligence. Faculty members of the group have active interests in: models of intelligent interaction, multi-agent systems, natural language understanding, constraint programming, computational vision, robotics, machine learning, and reasoning under uncertainty.
The AI Group also has a particular investment in Societal AI.
- Jan. 11, 2019
Professor Shai Ben-David and his colleagues Pavel Hrubes, Shay Moran, Amir Shpilka and Amir Yehudayoff have shown that a simple machine learning problem — whether an algorithm can extract a pattern from limited data — is mathematically unsolvable because it is linked to inherent shortcomings of mathematics discovered by Austrian mathematician Kurt Gödel in the 1930s.
- Dec. 3, 2018
Cheriton School of Computer Science Professor Shai Ben-David, his former PhD student Hassan Ashtiani, now an Assistant Professor at McMaster University, along with colleagues Christopher Liaw, Abbas Mehrabian and Yaniv Plan, have received a best paper award at NeurIPS 2018, the 32ndAnnual Conference on Neural Information Processing Systems.
- Oct. 29, 2018
Professors Olga Veksler and Yuri Boykov joined the David R. Cheriton School of Computer Science earlier this year. Previously, both were full professors in the Department of Computer Science at Western University, where they were faculty members for 14 years.
Their research interests are in the area of computer vision. In particular, Olga’s interests are in visual correspondence and image segmentation, and Yuri’s also include 3D reconstruction and biomedical image analysis.
- Apr. 5, 2019
Wei Yang, Master’s candidate
David R. Cheriton School of Computer Science
In recent years, we have witnessed many successes of neural networks in the information retrieval community with lots of labeled data. Yet it remains unknown whether the same techniques can be easily adapted to search social media posts where the text is much shorter. In addition, we find that most neural information retrieval models are compared against weak baselines.
In this thesis, we build an end-to-end neural information retrieval system using two toolkits: Anserini and MatchZoo. In addition, we also propose a novel neural model to capture the relevance of short and varied tweet text, named MP-HCNN.