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naraburns  nihil supernum  1yr ago (text post)   35417 thread views

Culture War Roundup for the week of October 3, 2022

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This week's revolutionary AI advance:

Imagen Video

It's not really revolutionary, as people have been pointing out this is the obvious next step for ages months now. But it still is a milestone worth noting.

As for this:

While our internal testing suggest much of explicit and violent content can be filtered out, there still exists social biases and stereotypes which are challenging to detect and filter. We have decided not to release the Imagen Video model or its source code until these concerns are mitigated.

Google's made a habit of this. They announce an amazing advance, and then say no one can have access to it because it can be used for Evil. No matter: Stable Diffusion will have something comparable out in a couple months.

ETA:

Actually, this out of DeepMind might be the bigger advance today, if less flashy:

Press: Discovering Novel Algorithms with AlphaTensor

Paper: Discovering faster matrix multiplication algorithms with reinforcement learning

My greatest fear for AI content generation is it being dominated by woke megacorps, with independent creators permanently locked out of contributing to culture. It looks like Google is investing heavily in that dystopia.

Novelai and stable diffusion being mostly uncensored has been a big white pill so far, but it feels like the shoe is about to drop.

There is an easier solution to this, AIs can be trained by anyone with enough computing power and training AI isn't that expensive. AI is actually fairly democratic as everyone can make their own. Once the cat is out of the box it isn't hard for everyone to get it. Information spreads naturally. The big risk is big corporations and governments access to data to use the AI on. That can give them a tremendous advantage.

Tuning is relatively cheap, but initial training is (currently) expensive. The furry StableDiffusion tweaks probably cost 50-400 USD depending on vendor and management, but the initial StableDiffusion model they're based on reflects ~300k USD at official prices (although probably got at least some bulk discounting).

Some of that'll go down as GPU prices decrease and newer equipment becomes available, but there are some costs for bandwidth and energy that are slower to change. This might go from 'old condo' to 'new car', but it's not likely to go to 'vacation' or 'a couple weeks' savings' for a few years, maybe even the better part of a decade, without dramatic changes to the underlying code.

For data, it varies more. LAOIN's a lot of bandwidth, curation, and drive space, but it's... actually not that incredible for a single (if slightly nuts) person. Other data sources, probably less so, either due to scale (eg video), to availability (eg privacy), or to more esoteric causes (AI music is a legal clusterfuck).

but the initial StableDiffusion model they're based on reflects ~300k USD at official prices (although probably got at least some bulk discounting).

I mean... this is cheap as hell in the scheme of things. It means you only need one startup with a medium sized seed round who sees a strategic advantage in commoditizing that model, and presto, it'll be trained and released. In fact that's exactly the story behind Stable Diffusion.

The reason we don't see a lot of open source models yet is... well, actually, we do see a lot of open source models. GPT-2 is publicly available, Facebook released a large language model roughly equivalent to GPT-3, and the Eleuther crowd also trained and released a large language model. OpenAI just released an open-source speech-to-text model, they already released CLIP as open source (which powers Stable Diffusion and Craiyon among others), StyleGANs 1, 2, 2-ADA and 3 are all publicly available and open source, etc. These models are just a year or two behind the current research papers. Which is about how long it probably takes to reproduce a research paper. Some of them are even better than that, even cutting edge -- like Nvidia's StyleGANs when they were released, like OpenAI's Whisper, like Nvidia's new Get3D.

Yeah, that's fair. I do think it's meaningful if it requires a startup with a medium-sized seed round (or someone willing to risk their retirement), rather than a slightly nutty hobbyist or enthuisiast, but it's not a FAANG-only thing, at least at a lot of common levels.

Well, there are a fair number of wealthy machine learning hobbyists out there. None of them have actually funded this type of thing to date as far as I know, but I could totally imagine some centimillionaire setting up a few-million-dollar charity to just train models and release them based on research as it emerges.

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