Artificial Intelligence is accelerating at an unprecedented speed, and as such is poised to play an incredibly greater role in our lives over the next few years. Deep neural networks currently achieve state-of-the-art performance on a very wide range of tasks, from speech recognition and machine translation to image detection and visual understanding. With these advances in the background, we are now able to pay more attention to the difficult questions of creating more socially and psychologically sophisticated machines. In this talk, I discuss some of the relevant issues and introduce some of our recent work focused at creating ‘social’ machines.
Muhammad Abdul-Mageed is an is an Assistant Professor of Information Science in the School of Library, Archival, and Information Studies (iSchool), UBC.
Before UBC, Dr. Abdul-Mageed was a Visiting Assistant Professor in the School of Informatics and Computing, Indiana University (2015-2016) where he created and taught courses on Social Media Mining, Sentiment Analysis, Computer-Mediated Communication, and Python Programming. In 2015-2015 he was also a Visiting Scholar in the Department of Computer Science at the George Washington University. Dr. Abdul-Mageed completed a dual Ph.D. in Computational Linguistics and Information Science at IU in 2015. Between 2010 and 2012, he was a Visiting Scholar in the Center for Computational Learning Systems, Columbia University. He is a fellow of the Center of Computer-Mediated Communication in IU, a Visiting Scholar in the World Well-Being Project, the University of Pennsylvania, and serves as a member of the standing reviewing committee for Transactions of the Association for Computational Linguistics.
His interests lie at the intersection of natural language processing, applied deep learning, and social media mining. He is especially interested in creating ‘social’ machines.
