Josh explains how astronomy and machine learning have informed each other, their current limitations, and where their intersection goes from here. (Read more: http://wandb.me/gd-josh-bloom)

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Josh is a Professor of Astronomy and Chair of the Astronomy Department at UC Berkeley. His research interests include the intersection of machine learning and physics, time-domain transients events, artificial intelligence, and optical/infared instrumentation.

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0:00 Intro, sneak peek 1:15 How astronomy has informed ML 4:20 The big questions in astronomy today 10:15 On dark matter and dark energy 16:37 Finding life on other planets 19:55 Driving advancements in astronomy 27:05 Putting telescopes in space 31:05 Why Josh started using ML in his research 33:54 Crowdsourcing in astronomy 36:20 How ML has (and hasn't) informed astronomy 47:22 The next generation of cross-functional grad students 50:50 How Josh started coding 56:11 Incentives and maintaining research codebases 1:00:01 ML4Science's tech stack 1:02:11 Uncertainty quantification in a sensor-based world 1:04:28 Why it's not good to always get an answer 1:07:47 Outro