Social

Open Collaboration in ML Research

Rosanne Liu, Jason Yosinski, Brian Cheung, Janice Lan, Suzana Ilić, Ryan Teehan, Natalie Summers, Connor Leahy, Siddhartha Kamalakara

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Abstract:

https://mlcollective.org/iclr-2021-open-collab-social/

Making AI research more inviting, inclusive, and accessible is a difficult task, but the movement to do so is close to many researchers' hearts. Progress toward democratizing AI research has been centered around making knowledge (e.g. class materials), established ideas (e.g. papers), and technologies (e.g. code) more accessible. However, open, online resources are only part of the equation. Growth as a researcher requires not only learning by consuming information individually, but hands-on practice whiteboarding, coding, plotting, debugging, and writing collaboratively, with either mentors or peers.

Of course, making ""collaborators"" more universally accessible is fundamentally more difficult than, say, ensuring all can access arXiv papers, because scaling people and research groups is much harder than scaling websites.

Can we nevertheless make access to collaboration itself more open? Can we flatten access to peers and mentors so the opportunities available to those at the best industrial and academic labs are more broadly available to all entrants to our burgeoning field? How to kick start remote, non-employment based research collaborations more easily? This social is designed to help you meet potential collaborators, find interesting ideas, and kick start your next project.

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