ML Collective
This is an invited talk Rosanne Liu gave at Google in October 2020. It gives a glimpse of her personal path into co-founding and leading ML Collective.
Source
AI research: the unreasonably narrow path and how not to be miserable
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20 thoughts on “AI research: the unreasonably narrow path and how not to be miserable”
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great topic.
Great talk! Your story almost brings tears to my eyes. 一定要成功呀！
I’m glad that you are an extremely petty person because I am just the same. Thanks for bringing up this topic.
It is narrow when … all of them are trying to hire the same kind of people, with the same rigid rubric. Can not agree more on this, we call this "内卷" in chinese.
Fantastic !!! Quiet relatable, inspiring, and very helpful.
Thanks a lot, Rosanne 🙂
Incredibly brave and intelligent points to make. I hope it starts a lasting conversation, thanks for starting it.
Here fully watching from Jamaica 🇯🇲👍
Simply fabulous presentation!
I love the thematic connection between the career advice of changing approach to alter outcomes,
and the clever tweaking of the model to significantly change its output!
Nice video, thanks 🙂
Realistic, open, and brave! Thanks a lot for this brilliant talk.
With due respect, I do not buy the generalist argument for hiring. isn't there already so many people who know a little about everything (like RL, vision, gradient descent, conv nets, etc)? Even any fresh school graduate worked on ML should know a bit about these. Isn't it that, as a research community, we want to understand why deep learning works at the fundamental level rather than treating it as a black box, and that is where we need depth more than ever?
Hearing one of the ML community's rockstars share such an honest perspective on the struggles we likely all recognize is refreshing and motivating. Thank you for sharing this!!
Being open about personal experiences and vulnerabilities is still much too rare in tech. Thank you, Rosanne.
Regarding a minor point around 8:45 mark — I don't think that conference paper decisions are that correlated. Sure, strong papers get in, terrible papers get rejected. But for the mid-tier papers, re-submitting to different conferences is the action based on the belief that the reviewing process from one to the other is more independent (in a probabilistic sense) than correlated. Otherwise, if the reviewing processes are extremely correlated, a rejection from one conference is enough evidence that you shouldn't submit to somewhere else because they are all correlated.
This is one genuine talk.
Nice to see ML collective has a YouTube channel. Didn’t watch the whole vid but I know Rosanne is top notch from Twitter 🙂
very frank and insightful talk, i wish all top industry performers analyzed themselves in public like this. thank you!
These are great insights.
At a startup, would a generalist have greater value?
Saya termasuk terlambat menonton video ini, tapi benar-benar masih relevan, membuka mata, juga memberi semangat terutama bagi saya yang pemula di dunia riset AI. Senang bisa mengikuti lewat grup MLC, kadang mengikuti Zoom (meski tertatih dan zona waktu yang berbeda). Tapi saya mulai merasakan semangatnya dan semoga bisa lebih aktif untuk bisa terlibat dalam riset bersama di komunitas MLC. Terima kasih, Rossane Liu, salam dari Indonesia 🙂