site banner
naraburns  nihil supernum  1yr ago (text post)   33252 thread views

Culture War Roundup for the week of February 20, 2023

This weekly roundup thread is intended for all culture war posts. 'Culture war' is vaguely defined, but it basically means controversial issues that fall along set tribal lines. Arguments over culture war issues generate a lot of heat and little light, and few deeply entrenched people ever change their minds. This thread is for voicing opinions and analyzing the state of the discussion while trying to optimize for light over heat.

Optimistically, we think that engaging with people you disagree with is worth your time, and so is being nice! Pessimistically, there are many dynamics that can lead discussions on Culture War topics to become unproductive. There's a human tendency to divide along tribal lines, praising your ingroup and vilifying your outgroup - and if you think you find it easy to criticize your ingroup, then it may be that your outgroup is not who you think it is. Extremists with opposing positions can feed off each other, highlighting each other's worst points to justify their own angry rhetoric, which becomes in turn a new example of bad behavior for the other side to highlight.

We would like to avoid these negative dynamics. Accordingly, we ask that you do not use this thread for waging the Culture War. Examples of waging the Culture War:

  • Shaming.

  • Attempting to 'build consensus' or enforce ideological conformity.

  • Making sweeping generalizations to vilify a group you dislike.

  • Recruiting for a cause.

  • Posting links that could be summarized as 'Boo outgroup!' Basically, if your content is 'Can you believe what Those People did this week?' then you should either refrain from posting, or do some very patient work to contextualize and/or steel-man the relevant viewpoint.

In general, you should argue to understand, not to win. This thread is not territory to be claimed by one group or another; indeed, the aim is to have many different viewpoints represented here. Thus, we also ask that you follow some guidelines:

  • Speak plainly. Avoid sarcasm and mockery. When disagreeing with someone, state your objections explicitly.

  • Be as precise and charitable as you can. Don't paraphrase unflatteringly.

  • Don't imply that someone said something they did not say, even if you think it follows from what they said.

  • Write like everyone is reading and you want them to be included in the discussion.

On an ad hoc basis, the mods will try to compile a list of the best posts/comments from the previous week, posted in Quality Contribution threads and archived at /r/TheThread. You may nominate a comment for this list by clicking on 'report' at the bottom of the post and typing 'Actually a quality contribution' as the report reason.

15
Top Bottom Old Controversial Comments
Jump in the discussion.

No email address required.

How does traditional machine learning even begin to address these problems? One way would be to say, feed it the sheet music for Beethoven's Fifth, and then show it as many recordings of that piece as you can until it figures out that the music lines up with the notation. Then do that for every other piece of music that you can. This would be a pretty simple, straightforward way of doing things, but does anyone really think that you could generate reasonably accurate sheet music to a recording it hadn't heard, or would you just get some weird agglomeration of sheet music it already knows?

Yes, I really think that. Artificial neural nets are really good at identifying higher-order structure from noisy, high-dimensional data. That's why they've had so much success at image-related tasks. All of these objections could just as easily be applied to the problem of identifying objects in a photograph:

After all, this method wouldn't give the computer any sense of what each individual component of the music actually does, just vaguely associate it with certain sounds. Alternatively, you could attempt to get it to recognize every note, every combination of notes, every musical instrument and combination of instruments, every stylistic device, etc. The problem here is that you're going to have to first either generate new samples or break existing music down into bite-sized pieces so that the computer can hear lone examples. But then you still have the problem that a lot of musical devices are reliant on context—what's the difference between a solo trumpet playing a middle C whole note at 100 bpm and the same instrument at the same tempo holding a quarter note of the same pitch for the exact same duration? The computer won't be able to tell unless additional context is added.

A cat can look completely different depending on the context in which it's photographed. Superficially, there's little in common between a close-up photo of the head of a black cat, a tabby cat lying down, a persian cat with a lime rind on its head, a cat in silhouette sitting on a fence, etc. You're telling me you can train an AI on such a messy diversity of images and it can actually learn that these are all cats, and accurately identify cats in photos it's never seen before? But yes, this is something neural nets have been able to do for a while. And they're very good at generalizing outside the range of their training data! An AI can identify a cat wearing a superman cape, or riding a snowboard, even if these are scenarios it never encountered during training.

You answered your own question as to why a good music transcription AI doesn't exist yet. There's little money or glory in it. The time of ML engineers is very expensive. And while the training process you described sounds simple, there's probably a lot of work in building a big enough labelled training corpus, and designing the architecture for a novel task.

What is this place?

This website is a place for people who want to move past shady thinking and test their ideas in a court of people who don't all share the same biases. Our goal is to optimize for light, not heat; this is a group effort, and all commentators are asked to do their part.

The weekly Culture War threads host the most controversial topics and are the most visible aspect of The Motte. However, many other topics are appropriate here. We encourage people to post anything related to science, politics, or philosophy; if in doubt, post!

Check out The Vault for an archive of old quality posts. You are encouraged to crosspost these elsewhere.


Why are you called The Motte?

A motte is a stone keep on a raised earthwork common in early medieval fortifications. More pertinently, it's an element in a rhetorical move called a "Motte-and-Bailey", originally identified by philosopher Nicholas Shackel. It describes the tendency in discourse for people to move from a controversial but high value claim to a defensible but less exciting one upon any resistance to the former. He likens this to the medieval fortification, where a desirable land (the bailey) is abandoned when in danger for the more easily defended motte. In Shackel's words, "The Motte represents the defensible but undesired propositions to which one retreats when hard pressed."

On The Motte, always attempt to remain inside your defensible territory, even if you are not being pressed.


New post guidelines

If you're posting something that isn't related to the culture war, we encourage you to post a thread for it. A submission statement is highly appreciated, but isn't necessary for text posts or links to largely-text posts such as blogs or news articles; if we're unsure of the value of your post, we might remove it until you add a submission statement. A submission statement is required for non-text sources (videos, podcasts, images).

Culture war posts go in the culture war thread; all links must either include a submission statement or significant commentary. Bare links without those will be removed.

If in doubt, please post it!


Rules


Recommended Posts And Communities

Recommended Realtime Chats