--Mihaela van der Schaar (Cambridge University) Interpretable AutoML: powering the machine learning revolution in healthcare in the era of Covid-19 and beyond Van der Schaar Lab COVID-19 site: https://www.vanderschaar-lab.com/covid-19/ (Starts at 00:01:45) Abstract: Medicine stands apart from other areas where AI can be applied. While we have seen advances in other fields with lots of data, it is not the volume of data that makes medicine so hard, it is the challenges arising from extracting actionable information from the complexity of the data. It is these challenges that make medicine the most exciting area for anyone who is really interested in the frontiers of machine learning – giving us real-world problems where the solutions are ones that are societally important and which potentially impact on us all. Think Covid 19! In this talk I will show how AI and machine learning are transforming medicine and how medicine is driving new advances in machine learning, including new methodologies in automated machine learning, interpretable and explainable machine learning, dynamic forecasting, and causal inference. I will also discuss our experiences in implementing such AI solutions nationally, in the UK, in order to fight the current Covid 19 pandemic as well as how they can be adapted for international use. --Michael Jordan (Berkeley) On Identifying and Mitigating Bias in the Estimation of the Covid-19 Case Fatality Rate Harvard Data Science Review Paper: https://hdsr.mitpress.mit.edu/pub/y9vc2u36/release/6 (Starts at 01:02:30)