21–30 of 123 posts
What is the reason Google published their research details about Imagen? Why don't they just keep their findings to themselfes and build products on top of them? Public companies can't do stuff just for the fun of it, right? So there must be some commercial reasoning behind it?
It's less cynical, more incentive alignment.
What is the reason Google published their research details about Imagen? Why don't they just keep their findings to themselfes and build products on top of them? Public companies can't do stuff just for the fun of it, right? So there must be some commercial reasoning behind it?
What's it going to take to train this on porn? This is something that can be crowdfunded. Look deepnude is a thing and somebody is making money off it: https://app.deepnude.cc/upload
Earlier quoted context omitted.
You do realize web scraping has gotten very cheap at scale and easy now too. It's an after thought for me, I'm more concerned with the economics of training, it can't be cheap
In my experience, scraping the data is the easy part. Once you've scraped it you've got to get rid of all the garbage, which is where the issues arise, especially if you're just blindly scraping everything you can find. For example, in a generative model I'm working on, I have a dataset consisting of ~5M images just blindly scraped from a website. After filtering, this drops down to ~500k images, yet a model trained…
- lots of people are stimulated by it
- lots of people want DALL-E-2 for porn
- and lots of people are willing to work towards that common goal
The beauty of this is that people are just going to keep coming and coming to it.
Like I'm trying to be mature and serious about this. What's it going to take?
- Community responsible for scraping dataset, generating image dataset from moving pictures, upscaling said dataset.
- Crowdsourced labeling, cleaning, normalizing dataset
- Crowdfunding to train, host, and publish.
All of the above are not easy by any means but its much more achievable than trying to build a generic DALL-E that can create anything.
Really my point is that the scope of the dataset has narrower range in terms of desired output where as DALL-E-2 casts a far wider net.
Specialization is key here.
What is the reason Google published their research details about Imagen? Why don't they just keep their findings to themselfes and build products on top of them? Public companies can't do stuff just for the fun of it, right? So there must be some commercial reasoning behind it?
- being open is kind of just how things in ML generally work right now, it's in stark contrast to things like chemistry or physics where paywalls are pretty common
- it's a matter of clout, ML is moving ridiculously quickly, with work from just 5 years ago being considered outdated in terms of capability, if you don't publish, someone else will and they'll get the credit. This likely also matters for the researchers since they get credit too. In a sense this is just publish or perish culture from academia.
- it's also somewhat about hiring, which is related to the clout. By putting out this kind of research, they're attracting talented engineers to consider working for them. This of course is pretty relevant to the rest of their business, especially given how heavily Google leans on AI to handle moderation.
What is the reason Google published their research details about Imagen? Why don't they just keep their findings to themselfes and build products on top of them? Public companies can't do stuff just for the fun of it, right? So there must be some commercial reasoning behind it?
What is the reason Google published their research details about Imagen? Why don't they just keep their findings to themselfes and build products on top of them? Public companies can't do stuff just for the fun of it, right? So there must be some commercial reasoning behind it?
2. Deploying models in a cost effective way is hard
3. Lessons learned from building this model can indeed be monetized and many of them may be kept secret.
What's it going to take to train this on porn? This is something that can be crowdfunded. Look deepnude is a thing and somebody is making money off it: https://app.deepnude.cc/upload
And what is going to take to generate videos, not pictures?
I don't think the leap is too crazy if we are talking short moving pictures without sound. However, when sound gets involved, this is where it would become very tricky.
What is the reason Google published their research details about Imagen? Why don't they just keep their findings to themselfes and build products on top of them? Public companies can't do stuff just for the fun of it, right? So there must be some commercial reasoning behind it?
Publishing high impact research gives credibility to the ML teams, which helps recruiting and prestige. It's less cynical, more incentive alignment.