Debating the potential of Machine Learning in astronomical surveys

The 2021 IAP colloquium is dedicated to a critical analysis of Machine learning methods in astronomy. A major revolution is now underway in astrophysics with the constant arrival of ever-richer and more complete datasets. The next generation of surveys will produce orders of magnitude more data than previous ones. However, it is becoming increasingly clear that traditional techniques are not up to the challenge of fully exploiting these data. At the same time, in the computer industry, large-scale application of machine learning methods on vast quantities of data have been able to solve problems that until now have been intractable. These new techniques are being adopted enthusiastically by astronomers who see them as a way to extract the maximum amount of science from new surveys. However, some caution is required; the concerns of industrial players developing machine learning techniques are not the same as astrophysicists who seek to explain data in the framework of physical models. It is therefore very timely to survey the landscape of machine-learning techniques in astronomy and to critically evaluate their usefulness in solving astrophysical problems. Many advanced analysis techniques have been pioneered at IAP in order to analyse data from the Planck satellite and the avalanche of rich datasets coming from future missions like Euclid make the IAP a natural location to hold this conference.