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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Follow Gradient Dissent on Twitter: https://twitter.com/weights_biases

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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

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Josh Bloom — The Link Between Astronomy and ML