When pondering the probability of discovering technologically advanced extraterrestrial life, the question that often arises is, "if they're out there, why haven't we found them yet?" And often, the response is that we have only searched a tiny portion of the galaxy. Further, algorithms developed decades ago for the earliest digital computers can be outdated and inefficient when applied to modern petabyte-scale datasets. Now, research published in Nature Astronomy and led by an undergraduate student at the University of Toronto, Peter Ma, along with researchers from the SETI Institute, Breakthrough Listen and scientific research institutions around the world, has applied a deep learning technique to a previously studied dataset of nearby stars and uncovered eight previously unidentified signals of interest. Join Senior Scientist Franck Marchis in conversation with lead author Peter Ma, co-author Leandro Rizk, and their supervisor, SETI Institute astronomer Cherry Ng, as they discuss the usefulness of machine learning, their recent findings, and the potential for the future of SETI. (Recorded live 23 March 2023.) Paper: https://seti.berkeley.edu/ml_gbt/overview.html

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