Mini ListenBrainz update released today

Following up on our release from last week, we found a number of minor problems in production that were really hard to spot on our test setup. Sometimes you need to have real data flowing through your system before you can find the real problems.

The following pull requests were merged and released just now:

This should hopefully make the follow page work a little better for everyone. 🙂

Automating the voting system

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For the last several years, one of the things our community has struggled with is a lack of active voters. We’ve tried to implement various measures to decrease the need for voters and load for the wonderful ones that actually do actively look through edits and help vote on them—e.g., making more edits auto‐edits and decreasing amount of time edits stay open. However, the edit queue is still quite unwieldy and as such we’ve kept trying to come up with other ways to decrease the load on our contributors.

Over the past few months since our last summit, we’ve been working on training AIs, both for recommendation engines and data analytics, and for helping out with spam, but it soon appeared that we had another valuable dataset: our history of 15,693,824 votes from 16,336 voters and 56,374,198 edits from 2,007,134 editors. It turns out that this is an unintended side-effects of the editing and voting system in that it creates a paper trail of our habits as a community and our collective mind.

A paper trail that you could, say, train a neural network on. And that’s just what we did.

By feeding data from our top voters, we’ve been able to train our network to replicate with 96.4% accuracy the personality when using the other half as test data. That figure is the average for 300 bots each based on our top 300 voters.
We were really impressed with the results but the story doesn’t stop there…

Meet BrainzVoter

The next logical step was to create our own Frankenstein’s monster. By training on 70% of our entire set of votes, we gave birth to a voting bot that represents the essence of our community. “BrainzVoter”, as we dubbed it, is precise and scores a staggering 98.9% accuracy on test data and comparing with the other 30% of our dataset.

To quote the late Terry Pratchet:

Ankh-Morpork had dallied with many forms of government and had ended up with that form of democracy known as One Man, One Vote. The Patrician was the Man; he had the Vote.

Edit filters

In view of the recent developments on net neutrality taken by the European Union with articles 11 & 13/17, MusicBrainz is taking measures to protect against copyright infringement: we’re implementing automatic edit filters. BrainzVoter will use the latest in NLP technology to understand what you, the editors, write in your edit notes, and use this understanding to vote on your edit. It will also inspect any URLs included in the edit note to cross-reference the data. The aggregate data will not be available to the public.

Edits with better and clearer notes will become more likely to pass. Consider this a good opportunity to (re‐)read How to Write Edit Notes!

How will this affect me as an editor?

Not much will change, and you can continue doing what you were doing before! We recommend that you take the time to make clear statements in your edit notes.
You will also be able to use a system of tags to express intent, using for example #typo #correction in the content of your edit text. Syntax highlighting and shortcuts will be available in the text editor.

In the end, by removing the need for humans to look over edits, the bot should give you, the editor, more time to add and edit and fix data in MusicBrainz, without having to spend time checking everyone else’s edits or worry about other editors disagreeing with yours!

After a brief trial period on MusicBrainz, this system will be adapted and also rolled out to BookBrainz.

We hope you will share our excitement for the benefits of automation and help us improve our training models over time. I, for one, welcome our AI overlords.