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Distill: a modern machine learning journal
11–20 of 109 posts
Re: Distill: a modern machine learning journal
#12I don't see it written explicitly; can anyone confirm that this journal is fully open-access?
Re: Distill: a modern machine learning journal
#13I don't see it written explicitly; can anyone confirm that this journal is fully open-access?
Yes. Everything is published under Creative Commons Attribution.
(One of the members of our steering committee, Michael Nielsen, has a significant history advocating for open science. I think there's about a snowball's chance in hell he'd be involved if we weren't. :P )
Re: Distill: a modern machine learning journal
#14The announcements and About page indicate an emphasis on visuals and presentation, which I apprI've. But when I think of "modern machine learning," I think of open-source and reproducibility (e.g. Jupyter notebooks).
Will the papers published on Distill maintain transparency of the statistical process?
I see in the submission notes that articles are required to be a public GitHub repo, which is a positive indicator. Although the actual code itself does not seem to be a requirement.
Re: Distill: a modern machine learning journal
#15I've been trying to read more primary source information, sort of as my own way of combatting "fake news" but before that term was coined. There's a learning curve to it, but I've found that reading S1 filings and Quarterly Earnings Reports can be more enlightening than reading a news article on any given company. Likewise, reading research papers on biology and deep learning is significantly more valuable than reading articles or educational content on those topics.
As you'd imagine though, it's really hard. Reading a two page research paper is a very different experience from reading a NYTimes or WSJ article. The information density is enormous, the vocabulary is very domain specific, and it can take days or weeks of re-reading and looking up terms to finally understand a paper.
I'm really excited about Distill, there's a lot of value in making research papers more accessible and interesting. I've noticed that the ML/AI field has been very pioneering about research publication process, some papers are now published with source code on GitHub and the authors answering questions on r/machinelearning. This seems like a really great next step, I hope other fields of science will break away from traditional journals and do the same.
Re: Distill: a modern machine learning journal
#16I don't see it written explicitly; can anyone confirm that this journal is fully open-access?
Seems to be here: http://distill.pub/journal/
Passages like:
"Distill articles must be released under the Creative Commons Attribution license."
With a little more flexibility to keep things private before publishing: "You can keep it private during the review process if you would like"
Re: Distill: a modern machine learning journal
#17Various announcements: Google Research: https://research.googleblog.com/2017/03/distill-supporting-c... DeepMind: https://deepmind.com/blog/distill-communicating-science-mach... OpenAI: https://openai.com/blog/Distill/ YC Research: http://blog.ycombinator.com/distill-an-interactive-visual-jo... Chris Olah: http://colah.github.io/posts/2017-03-Distill/
As I said in Rob's thingy, I hope you get the tenure committees and job committees, because they don't have to respect it but they're the ones you have to get to respect
It varies heavily by institution and country, but CS is moving increasingly towards caring about citation metrics above anything else (with "selectivity", i.e. a high bar for peer review and low acceptance rate, being the main other factor). Unlike in most other fields, conference papers therefore hold weight, not only journal articles. This does sometimes cause trouble at higher levels of large institutions, where a CS dept strongly recommends a candidate for tenure, but when the case makes it up to the dean level, the dean, who is a physicist or biomed person, wonders how they could possibly recommend tenure for someone who has "just" a bunch of conference papers and few journal articles. But that is becoming rarer at places with top CS departments.
Anyway, as a result, I don't see a reason why an alternative-format journal would necessarily fare any worse than conferences have in terms of becoming accepted, if the reviewing standards are high and if it attracts citations.
For the hiring side (more than the tenure side), to some extent, oddly enough, the first-order decision here is in Google's hands. A lot of CS hiring committees nowadays unofficially do a first cut sifting of resumes by typing candidates into Google Scholar and looking at their Google-computed h-index, so what "counts" is basically up to Google.
Re: Distill: a modern machine learning journal
#18I sure hope this catches on, but we should all be aware of the hurdles:
- Little incentive for researchers to do this beyond their own good will.
- Most ML researchers are bad writers, and it's unlikely that the editing team will do the work needed (which is often a larger reorganization of a paper and ideas) to improve clarity.
- Producing great writing and clear, interactive figures, and managing an ongoing github repo require nontrivial amounts of extra time, and researchers already have strained time budgets.
- It requires you to learn git, front-end web design, random javascript libraries (I for one think d3 is a nuisance), exacerbating the time suck on tangents to research.
Maybe you could convince researchers to contribute with prizes that aligned with their university's goals. Just spitballing here, but maybe for each "top paper" award, get a team together to further clarify the ideas for a public audience, collaborate with the university and their department and some pop-science writers, and get some serious publicity beyond academic circles. If that doesn't convince a university administration that the work is worth the lower publication count, what will?
In the worst case it'll be the miserable graduate students' jobs to implement all these publication efforts, and they won't be able to spend time learning how to do research.
Re: Distill: a modern machine learning journal
#19Is there any concern about a web-native journal being less "future-proof"? I've come across quite a few interactive learning demonstrations in Flash/Java that no longer work.
Re: Distill: a modern machine learning journal
#20Various announcements: Google Research: https://research.googleblog.com/2017/03/distill-supporting-c... DeepMind: https://deepmind.com/blog/distill-communicating-science-mach... OpenAI: https://openai.com/blog/Distill/ YC Research: http://blog.ycombinator.com/distill-an-interactive-visual-jo... Chris Olah: http://colah.github.io/posts/2017-03-Distill/
Awesome.
How do I donate to this?