This week we have another longform episode! David got a little obsessed with the James Webb Space Telescope (JWST) a few months ago and spent hours and hours researching and talking to experts so that he can try to explain it here on the podcast for Marques and Andrew. This one is a little dense but if you're even remotely interested in the mysteries of our universe this one is for you. David explains why the JWST is such a big deal and what we hope to discover once it begins sending back pictures.Special thanks to:Paul GeithnerDr. Jonathan McDowellDr. Peter GaoTim DoddSources:How James Webb Orbits NothingWebb vs Hubble (NASA)Fine Guidance Sensor (NASA)JWST FAQ (NASA)Where is Webb? (NASA)Webb Orbit (NASA)Webb Sunshield (NASA)Webb Mirrors (NASA)Twitters:https://twitter.com/wvfrmhttps://twitter.com/mkbhdhttps://twitter.com/andymanganellihttps://twitter.com/adamlukas17https://twitter.com/DurvidImelInstagram:https://www.instagram.com/wvfrmpodcast/Shop the merch:shop.mkbhd.comJoin the Discord:https://discord.gg/mkbhdMusic by 20syl:https://bit.ly/2S53xlCWaveform is part of the Vox Media Podcast Network. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Peter Gao, a member of the Aquarium team, discusses the role of Aquarium in improving dataset quality for machine learning models. The podcast explores the challenges of understanding and improving datasets, addresses bias in machine learning models, and discusses the current state of machine learning and its future. It also touches on the customer onboarding process, the software stack used for data visualization, and the potential of machine learning in practical improvements.