The only reason he became popular is because he was the default example in the Disco Diffusion notebook ( where all this really started ) and people started copying from that. I don't know who came up with the tagging nonsense. There's thousands of other artists who have just as much work out there and their names produce just as consistent styles as Greg.

It doesn't work like that. So no, aren't going to find "samples". If you were to use an AI tool to see if it was simmilar to some other work, problem is putting human made works through that would also result in matches.

>But can't replace photoshop pros completely.

True, not completely but most of them, yes. Currently there's literally one paper coming out per day on text based editing.

I don't disagree there.

I won't go into the actual legalities since that is still untested in the US. But practically, what difference does it make? If it's good enough, how is the copyright/trademark office or judge going to know it's AI generated?

If we're talking about directly using the current text to image generators, thats true. But that's not what I'm talking about.

The real fun starts when you start connecting them to language models

https://twitter.com/jaukia/status/1567914039061217287

This is an example with GPT3. But will get better in the coming years ( year? ) with even more powerful LLMs, especially the multimodal ones that can directly inagine visuals instead of just text that is passed to the text-to-image renderer ( the rendering model might still be used for the final composition but it helps to have the designer model be able to think in actual visual terms ).

/r/DallE2 has the best comprehension, which means it requires the least amount of skill or fiddling, while also producing coherent works. No complicated setup either.

It's not free, but you do get 50 free credits when you start and 15 every month which should be enough to have fun with.

Exactly. Just because the printing press ( or printer, or word processor ) made the scribe obsolete as a profession doesn't mean people don't learn to handwrite things and put it to use.

Yes, the ability of anyone anywhere to generate anything they want or can imagine instantly and at almost no cost, is definitely just a fad.

You've definitely got it all figured out.

>So who's gonna direct it? The secretary?

The marketing team? Instead of communicating with a designer they'd communicate with an AI.

Depends on your definition of it. There's definitely a bit of this

>Or maybe they thought coming up with a copy of another AI would be more irrelevant than coming up with an AI that has something different to offer?

but another reason was that it's easy to get funding ( in the form of compute in this case ) from public institutions when there's a virtue signalling angle.

It's not open to anyone. He's putting on a show by recreating examples from their paper.

It's basically a fine-tuned variation of Chinchilla ( smaller than GPT3 with just 1/3rd the parameters but performs better since it was trained adequately data-wise ) to be more aligned, like how they modded GPT3 into the current InstructGPT variation.

It's not really a jack of all trades in that sense since it was trained on a dataset simmilar to GPT3 of mostly English text.

Most of the new models we'll be seeing ( like the topic of this post ) will definitely be following this path.

While this is the current consensus ( they went too broad ) , it's still a guess. These are all black boxes so we can't say for certain.

Basically, a "jack of all trades , master of none" type issue. As we all know from the Chinchilla paper, current models are already severely undertrained data-wise. They went even further by having even less data per language , even if the total dataset was comparable to GPT3.

There's nothing a Boston Dynamics Robot can do that couldn't be done by a cheap drone and a bomb.

We'd have already had a open source GPT competitor in Bloom ( same amount of parameters as GPT3 and open source / open model ) if they didn't decide to virtue signal. They trained it on too many diverse languages and sources and the AI came out an idiot ( significantly underperforms GPT3 in almost all metrics ).

https://en.wikipedia.org/wiki/Moravec%27s_paradox

This is just a research model simmilar to what VQGAN-clip was, not a production model. There are multiple models like OpenAI Jukebox that was trained with copyrighted music. It's fair use. Not an issue.