Good, I worked at WMF for a bit and the fundraising narrative was one of many reasons why I left. Wikipedia is doing just fine, but there are just way too many “special projects” that need to be reconsidered. Also way too much frivolous international travel for staff members.
Wow, Bungle's California tour is the stuff of legends. I've only seen videos but I still can't figure out how they managed to pull it off live. Never saw Bungle, but I did see Fantomas on the Suspended Animation tour, which was another one of those albums I have no clue how they managed to perform night after night.
Agreed! I saw Magma in Portland, OR on their 2016 tour. I remember how effortless they made everything look, all the songs seemed to blend in and out of each other, so surreal. It felt like I was watching some intergalactic opera/theater presentation, probably one of the best concerts I've ever seen. Wish I could understand Kobaïan...
agreed! org-mode is seriously the most lightweight tool i use on a daily basis. also the various exporters have helped me out more times than i can count now.
i recently moved over to mediawiki due to work, but it is a behemoth compared to org-mode.
Nice, I've been using org-roam/org-mode for the past year and really love it now. Also been using org-journal as a sort of zettlekasten box for fleeting notes and linking to other stuff. Org-mode is really fantastic for this IMO, it's definitely been worth the learning curve.
Interesting, can you go into more detail about how you discover new music? I used to use Discogs quite a bit, but I think there is more potential for "discoverability" on Wikipedia in some sense.
Are you me? I also work in ML and am raising a child in a small seaside town. Your comments are very similar to my experience. I was told going remote and leaving the city was career suicide, but it's been quite the opposite. The amount of assumed travel (pre-covid) was a bummer and often happened at inconvenient times at multiple companies, but I still wouldn't trade it. I never talk about tech outside of work, which is nice, although a little lonely at times. In some ways I think my worldview has broadened by not living in a tech hub anymore. Work-life balance is super healthy, I don't spend as much time in front of screens anymore either. The pros totally outweigh the cons in my opinion.
I think "participatory" means something similar here within an ML context. It favors building community-based algorithmic systems and focuses on lowering the barrier to participation, so that non-expert users can be involved during the machine learning development cycle.
I'm not aware of any seminal papers per-say, although here are a few that I've read recently... first one is something I maintain at $DAYJOB:
1) Halfaker, A., & Geiger, R. S. (2020). Ores: Lowering barriers with participatory machine learning in Wikipedia. ArXiv:1909.05189 [Cs]. http://arxiv.org/abs/1909.05189
2) Martin Jr. , D., Prabhakaran, V., Kuhlberg, J., Smart, A., & Isaac, W. S. (2020). Participatory problem formulation for fairer machine learning through community based system dynamics. ArXiv:2005.07572 [Cs, Stat]. http://arxiv.org/abs/2005.07572
Alot of IML seems to focus on building interfaces, so this one was pretty good:
1) Dudley, J. J., & Kristensson, P. O. (2018). A review of user interface design for interactive machine learning. ACM Transactions on Interactive Intelligent Systems, 8(2), 1–37. https://doi.org/10.1145/3185517
Three papers stick out for me in the IML / participatory machine learning space this year:
1) Michael, C. J., Acklin, D., & Scheuerman, J. (2020). On interactive machine learning and the potential of cognitive feedback. ArXiv:2003.10365 [Cs]. http://arxiv.org/abs/2003.10365
2) Denton, E., Hanna, A., Amironesei, R., Smart, A., Nicole, H., & Scheuerman, M. K. (2020). Bringing the people back in: Contesting benchmark machine learning datasets. ArXiv:2007.07399 [Cs]. http://arxiv.org/abs/2007.07399
3) Jo, E. S., & Gebru, T. (2020). Lessons from archives: Strategies for collecting sociocultural data in machine learning. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 306–316. https://doi.org/10.1145/3351095.3372829
Also a great read related to IML tooling for audio recognition:
1) Ishibashi, T., Nakao, Y., & Sugano, Y. (2020). Investigating audio data visualization for interactive sound recognition. Proceedings of the 25th International Conference on Intelligent User Interfaces, 67–77. https://doi.org/10.1145/3377325.3377483