Building Agile Machine Learning Models Professor Richard Zemel

The last few years have seen significant advances and realworld applications in machine learning and artificial intelligence, which have also led to a myriad of challenges. In this talk I will focus on three fundamental issues that confront our attempts to build flexible and effective ML systems. How can new tasks be learned readily, with relatively few examples How can these systems maintain strong performance as the environment they are operating in differs from the training environment I will discuss current approaches to these problems, and some surprising links to a third problem: how can these systems avoid discriminating against individuals or groups I will highlight their successes and also current limitations and open problems. Richard Zemel is a Professor and Machine Learning Research Chair in the Department of Computer Science at the University of Toronto. He will be joining the faculty in the Computer Science Department at Columbia University this summer. He is a CoFounder and the