Hacker News Reader

Comment by 152334H | Hacker News Reader

No model, no code.

About as exciting as Imagen-video, which was released eons ago.

The approach is different, it can use pretrained models, i.e. stable diffusion, which is a pretty exciting research development. This means that it only requires 'fine-tuning' existing models to get this result.

I agree with that, but it's hard for me to get excited with the knowledge that it'll almost certainly be discarded and forgotten. I've seen too many papers that looked interesting from a theoretical perspective, but were simply never brought to the public because of the barrier of dev+training.

In this case, you need someone that can implement the method as described (hard!), and then you need someone with a setup better than a rented 8xA100 (expensive and not available on many cloud providers) to actually reproduce the model.

To put it in context, in almost all areas of research (physics, biology, chemistry, electronics, etc), running experiments is expensive. ML is in the category that there can still be advances done by amateurs at home. I don't think it's worth writing off everything that requires more resources than a hobbyist.

Yeah. You can rent 8xA100s, it's a steep price but you can. It's much harder for a hobbyist to rent electron microscopes or NMR machines.

Emad says they will release it.

https://twitter.com/emostaque/status/1648546670659923970

Ugh, that tweet is so confusing. The researchers did work for Stability - this work was done for NVIDIA. It's entirely unclear if the researchers are even still associated with stability.ai but Emad sure does imply that's the case.

> (Team working on our variant, will be done when it's done and they are happy with it).

I _think_ he's saying that _his_ team is working on a similar model - and that they will release _that_ model "when it's done" (and not to to expect that to happen any time soon).

Just super vague, bordering on taking credit for work that NVIDIA did. Seems like he typed it out on his phone and/or is Elon-levels of lazy about tweets.

Three of the authors of this paper work for Stability AI currently.

One of them even said: "Unfortunately we cannot release the weights. That's why I joined @StabilityAI to work on OS video models"

- https://twitter.com/andi_blatt/status/1648598423526932483

Thanks for the clarification!

I doubt you'll ever again see a model being publicly released for something as capable as this.

I can't say I fully understand the mechanisms by which they achieve that, but it's clear that the powers that be have decided that the public cannot be trusted with powerful AI models. Stable Diffusion and LLaMA were mistakes that won't be repeated anytime soon.

AI models with obvious, broad, real world applications always seem get reproduced in public. Nvidia’s result is obviously great, but it’s still a long way from being useful. It reminds me of image generation models maybe 4 years ago.

We need a killer application for video generation models first, then I’m sure someone will throw a $100k at training an open source version.

I think deep down, everyone already knows what the "killer application" for open source AI video generation is going to be.

I am going to guess: Making "dog-nature-like" humanoids that aim to please lonely people, who are nicer to be around than people, easier than real relationships.

Reducing human trafficking by generating artificial content ?

Not a thing.

Yeah, just visit civitai and see what's popular there.

Don't we have enough of that already?

Capitalism doesn't ask that question.

> AI models with obvious, broad, real world applications always seem get reproduced in public.

Really? GPT-3 was released almost 3 years ago. Where is the public reproduction?

And don't say LLaMA. I've used it and it isn't even close to GPT-3, nevermind GPT-4.

The current generation of GPT-3, which started with text-davinci-003, was actually released on November 2022, not quite 3 years ago. I'm not even sure the model that was released 3 years ago is still available to test, but it was much less impressive than more recent models - I wouldn't be surprised if LLaMa were actually better.

The model trained 3 years ago was only trained on 300B tokens, heavily undertrained (in terms of the Chinchilla scale), that's why LLaMa models can easily beat it on most benchmarks (they were trained on 1/1.4T tokens). About the current GPT-3.5 models, who knows, OpenAI is not very open about it.

Which Llama model have you played with? Unless it’s Llama 65B with 16f precision it’s not a fair comparison to gpt3.

The tragedy of the commons is at play here. We could get amazing ML models rivaling the best if people interested could pool together money for a $100k or $1 million training run. But there doesn't seem to be the willingness, so we humbly rely on the benevolence of companies like Stability and Meta to release models to the public.

As someone not very familiar with the field, what is wrong with this comment? Is the scale off?

Kickstarter is into the tens of millions these days [1]. I would assume some number of millions might be possible, if the right names were behind it.

[1] https://www.kickstarter.com/projects/dragonsteel/surprise-fo...

Kickstarters like the one you linked don't suffer from the tragedy of the commons because people are essentially just pre-paying for a product. With funding an open source ML model, there's little incentive to not be a free-rider.

I doubt there would be much funding for a kickstarter that didn't promise to be open source.

> people are essentially just pre-paying for a product

I think this is what the 4 nines of the user space wants. A pre trained, open source, model to work with.

> but it's clear that the powers that be have decided that the public cannot be trusted with powerful AI models.

The most dangerous AI model today (in a practical sense, as people are actually using for shady stuff) is ChatGPT, which is closed source, but open to the public so anyone can cheat on their exams, write convincing fake product reviews, or generate SEO spams, etc.

The fact that a model is closed source doesn't change anything as long as it's available for use. Bad actors don't care about running the code on their own machine…

But they're still showing us that the results exist. They're trying to have it both ways, by showing the results are tangible progress while implicitly admitting that that progress is too powerful in the hands of the public.

Is there anything that incentivizes Nvidia to publish these results? Is it just needing to get papers out in the public for the academic clout? Something tells me that all this accomplishes is setting the standards of everyone who sees the possibilities, that "this will be the future", and a third party without the moral framework of Nvidia will become motivated to develop and openly release their own version at some point.

That's just good marketing, isn't it? "Our product is amazing! In fact it's too good, no you can't have it. Unless just maybe, we might let you buy access to it." Oh wow if it's so good that they won't let me have it then I definitely want it!

Reply on news.ycombinator.com