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naraburns  nihil supernum  1yr ago (text post)   35400 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).

Even if we assume the high-end of your range, and say that for the foreseeable future training a near-state-of-the-art deep learning model from scratch will cost around half a million dollars, that's still cheap enough to be considered fairly democratic. A lot of people and organisations have that sort of money, many of which exist outside of the Cathedral. And as you say, you can do a lot by tuning an existing model, which is feasible for hobbyists.

I think for the sort of controls you're worried about, it's not just a matter of who can afford to buy it, but also who can afford to sell it. Not just that there's a few limited companies doing this stuff, but in the sense that if you come up to the sorta companies that have and resell these resources, they demonstrably will start poking around at what you're doing, how you're paying them, and what you're doing.

((Not... uh, very effectively in an anti-fraud sense, given Amazon. But very effectively in a not-doing-things-they-don't-like, given Amazon.))

Eventually that stops being a problem as used past-generation tensor core GPUs trickle out into the used market (uh, assuming ITAR doesn't get involved), or as resellers are able to more heavily obscure stuff at larger scales, or as the relative scales decrease due to performance and efficiency gains.

But it is worth keeping in mind as a limit to the democratization of the space.

It's also possible that manufacturers could nerf GPUs for the purpose of ML except for customers with whom they have a special relationship. See e.g. the rate limiting NVIDIA did for crypto mining while still selling a higher priced card without the nerfing.

Yeah, that's another risk. It didn't work for the anti-mining stuff, but given politics and economics around ML that may have stronger incentives.

Crippling GPUs works very well in one context I've seen: FP64. Games don't use it so manufacturers don't get dinged for having lousy performance with it, and engineers/mathematicians/scientists won't flinch at paying through the nose for "professional" GPGPU cards, so with a few exceptions (Titan Black, Radeon VII, and even those were high-end) you get a pittance of FP64 support on consumer cards.

But there's a very well-delineated difference between 32-bit and 64-bit floats. What's the clear technical difference between "bad ML models, which we want to keep away from hobbyists" and "good ML models, which everybody's going to be throwing into their game engines as fast as studios can train them"? The difficulty of slowing down "bad" algorithms but not "good" ones was effectively the problem with crypto rate limiting, (which only brought the cards down to 50% speed and only worked on some crypto types and was quickly foiled via driver or BIOS changes), not any special societal support for cryptocurrency. Compare DRM, which despite massive political and economic support gets broken over and over again because from a technical standpoint the problem statement is almost a self-contradiction.

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