Abstract: Machine learning algorithms are everywhere, ranging from simple data analysis and pattern recognition tools used across the sciences to complex systems that achieve super-human performance on various tasks. Ensuring that they are safe--that they do not, for example, cause harm to humans or act in a racist or sexist way--is therefore not a hypothetical problem to be dealt with in the future, but a pressing one that we can and should address now.
Philip will discuss some of his recent efforts to develop safe machine learning algorithms, and particularly safe reinforcement learning algorithms, which can be responsibly applied to high-risk applications. He will focus on a specific research problem that is central to the design of safe reinforcement learning algorithms: accurately predicting how well a policy would perform if it were to be used, given data collected from the deployment of a different policy. Solutions to this problem provide a way to determine that a newly proposed policy would be dangerous to use without requiring the dangerous policy to ever actually be used.
A reception will be held at 3:40pm in the atrium, outside the presentation room.