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.
Жүктеу.....
Пікірлер: 21
@pw72252 жыл бұрын
Being open about personal experiences and vulnerabilities is still much too rare in tech. Thank you, Rosanne.
@Marcos10PT2 жыл бұрын
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!!
@swyxTV
Жыл бұрын
i’m new to her work and need a bit of context - what are you referencing when saying she is a rockstar? (ie what must we know about her?)
@priyamehta96992 жыл бұрын
Incredibly brave and intelligent points to make. I hope it starts a lasting conversation, thanks for starting it.
@sarahjamal862 жыл бұрын
Realistic, open, and brave! Thanks a lot for this brilliant talk.
@ly10522 жыл бұрын
Great talk! Your story almost brings tears to my eyes. 一定要成功呀!
@Anonymous-lw1zy2 жыл бұрын
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!
@swyxTV Жыл бұрын
very frank and insightful talk, i wish all top industry performers analyzed themselves in public like this. thank you!
@user-wr4yl7tx3w Жыл бұрын
These are great insights.
@Mutual_Information2 жыл бұрын
Nice to see ML collective has a KZread channel. Didn’t watch the whole vid but I know Rosanne is top notch from Twitter :)
@lennymaxmusic99452 жыл бұрын
Here fully watching from Jamaica 🇯🇲👍
@MewadaDeepak2 жыл бұрын
Fantastic !!! Quiet relatable, inspiring, and very helpful. Thanks a lot, Rosanne :)
@DrOsbert2 жыл бұрын
This is one genuine talk.
@Bianchi772 жыл бұрын
Nice video, thanks :)
@nikre2 жыл бұрын
great topic.
@dwightzz44492 жыл бұрын
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.
@leodu5612 жыл бұрын
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.
@user-wr4yl7tx3w Жыл бұрын
At a startup, would a generalist have greater value?
@ahmedrehab65712 жыл бұрын
I’m glad that you are an extremely petty person because I am just the same. Thanks for bringing up this topic.
@golabidoon3812 жыл бұрын
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?
@manncodes
2 жыл бұрын
I think she meant being jack of all trades, master of one. BUT your 'jack' being equivalent to others 'master'. Also, I do agree to your point on interpretability of AI!
Пікірлер: 21
Being open about personal experiences and vulnerabilities is still much too rare in tech. Thank you, Rosanne.
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!!
@swyxTV
Жыл бұрын
i’m new to her work and need a bit of context - what are you referencing when saying she is a rockstar? (ie what must we know about her?)
Incredibly brave and intelligent points to make. I hope it starts a lasting conversation, thanks for starting it.
Realistic, open, and brave! Thanks a lot for this brilliant talk.
Great talk! Your story almost brings tears to my eyes. 一定要成功呀!
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!
very frank and insightful talk, i wish all top industry performers analyzed themselves in public like this. thank you!
These are great insights.
Nice to see ML collective has a KZread channel. Didn’t watch the whole vid but I know Rosanne is top notch from Twitter :)
Here fully watching from Jamaica 🇯🇲👍
Fantastic !!! Quiet relatable, inspiring, and very helpful. Thanks a lot, Rosanne :)
This is one genuine talk.
Nice video, thanks :)
great 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.
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.
At a startup, would a generalist have greater value?
I’m glad that you are an extremely petty person because I am just the same. Thanks for bringing up this topic.
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?
@manncodes
2 жыл бұрын
I think she meant being jack of all trades, master of one. BUT your 'jack' being equivalent to others 'master'. Also, I do agree to your point on interpretability of AI!