site banner

Culture War Roundup for the week of July 15, 2024

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.

9
Jump in the discussion.

No email address required.

LLMs don’t generate pictures. I have no idea why people keep repeating the blatantly obviously incorrect claim that AI equals LLM.

Stable diffusion contains a text transformer. Language models alone don't generate pictures but they're a necessary part of the text-to-image pipeline.

Also some LLMs can use tools, so an LLM using an image generation tool is in a sense generating a picture. It's not like humans regularly create pictures without using any tools.

Yes, it has an input parser. If you’ve studied SD details, you’ll know that it’s very different from what people call LLMs and is only a small part of Stable Diffusuon (and not anything you could say that ”generates pictures”).

Yes, it has an input parser

Specifically OpenCLIP. As far as I can tell the text encoder is nearly a bog-standard GPT-style transformer. The transformer in question is used very differently than the GPT-style next token sampling loop, but architecturally the TextTransformer it's quite similar to e.g. gpt-2.

Still, my understanding is that the secret sauce of stable diffusion is that it embeds the image and the text into tensors of the same shape, and then tries to "denoise" the image in such a way that the embedding of the "denoised" image is closer to the embedding of the text.

The UNet is the bit that generates the pictures, but the text transformer is the bit which determines which picture is generated. Without using a text transformer, CLIP and thus stable diffusion would not work nearly as well for generating images from text. And I expect that further advancements which improve how well instructions are followed by image generation models will come mainly from figuring out how to use larger language transformers and a higher dimensional shared embedding space.

Tbh, I didn’t notice that Tomato had called those out in particular. The top level post talks about various applications which are definitely not LLMs.

LLMs specifically are horrible with arithmetic (as Tomato said). I don’t see why a math oriented AI couldn’t be made - it just wouldn’t be an LLM and quite possibly would have about as much in common with LLM as eg. image generators do (iow, very little beyond an input parsing stage).

I don’t know what it is about this site (other than people being infatuated with ridiculously long meandering posts) that makes users think LLMs are the modal example of AI when their actual productive uses are limited to a few text generating and parsing niches. Meanwhile eg. every photo 99% of people take has multiple layers of AI applied to it.