> I'm not sure sure that originality is that different between a human and a neural network.
It is, yes. For example, a neural network can't invent a new art style on its own, or at least existing models can't, they can only copy existing art styles, invented by humans.
Hah. That could be a great way to use those models. "Talk to the AI until you know what you want, then I'll make it for you".
I'm totally with your wife, btw, the attitude of her customers sounds horrible. On the other hand, my experience is that one artist took my $25 and has still not produced what he agreed three months later, and yet asked me if I had more work for him. Another guy offered to do it for free and did it for free in a few days and then refused to accept my money when I explained that I was already paying another guy for the same task so it was only fair that I paid him, too. This was some cover art for a vanity project of mine and I was asking for free contributions but also paid the first artist because he was evidently trying to become a professional. Fat chance of that. Bottom line, if you want good art you have to find the people who are passionate about it.
Oh and image models can't create the art I want, because it's text-based art. Even if they could generate the images I want, they couldn't output them in ASCII or ANSI. In fact I tried and they give me kind of pixelated results, but not recognisably text-character based.
It's still stupid. This is like asking DALL-E to generate an image that solves a math equation step by step. Of course this is easier for a human to do.
Try getting a landscape in the style of vincent van gogh for 10$ on fiver though. AI will give you that in seconds easily, and that's what's amazing about it.
I was in a meeting on Cognitive AI at the Royal Society in London last week where a gentleman from Stanford presented work where GPT-3 was prompted to solve math equations step-by-step and did well (better than I would have expected). Point being, if GPT-3 can do it, DALL-E should also be able do it, and testing whether that is the case is not stupid, but interesting.
The big question with systems like those image generation models is to what extent their generation can be controlled, and how much sense it makes. This is exactly the kind of testing that has to be done to answer such questions. Just flooding social media with cherry-picked successes doesn't help answer any questions at all. Because cherry-picking never does.
To be honest, I don't get the defensiveness of the comments in this thread. Half the comments are trying to call foul by invoking some rule they made up on the spot, according to which "that's not how you should use it". The other half pretend they knew all along what the result would be, and yet they're still upset that someone went and tried it, and posted about it. That kind of reaction is not coming from a place of inquisitiveness, or curiosity, that is for sure. It's just some kind of sclerotic reaction to novelty, people throwing their toys because someone went and did something they hadn't thought about.
> Try getting a landscape in the style of vincent van gogh for 10$ on fiver though.
In another comment posted in this thread I tried to get Stable Diffusion to give me a graph with three lines in the style of van Gogh and other famous artists. I'd be very curious to see what that would look like and I can't imagine it easily. I'm left wondering, because Stable Diffusion can't do it. Maybe I should ask someone on fiverr.
What you said was that they weren't trained on chart images, not that they weren't the focus:
> The image generation models weren't trained on chart images, everyone already knows they're gonna be bad at that.
I have no idea how you could even know what was, or wasn't in those models training sets. Yet you posted with conviction as if you were sure you knew. What's the point of that?
Edit - Also, what do you mean "it obviously wasn't the focus"? The focus of what? The focus of training, or the focus of presenting the results on social media?
This is absurdly silly. These data sets contain millions of images at a bare minimum from web crawls, often billions, so of course there will be a non-zero number of charts in them. If you want to be pedantic about it be my guest I guess.
You could probably find a few driver's ed teachers who taught their students to do doughnuts too, but saying "driver's ed teachers don't teach their students to do doughnuts" would nonetheless be largely accurate.
Silly yourself. If there were simply a "non-zero" number of charts in them, the model wouldn't have, you know, modelled them. That the model can reproduce graphs is clear evidence that it saw enough graphs to reproduce them.
And don't call me silly just because you used imprecise language to try to make a vague point with great conviction as if you absolutely knew what you're talking about, when you absolutely didn't. Show some respect to the intellect of your interlocutor, will you?
And, seriously, you haven't answered my question: the focus of what? What do you mean by "it obviously wasn't the focus"?
I think you were emboldened by the downvoting of my comment and assumed you don't need to make sense, but I think the downvoters were downvoting something else than what you refuse to answer.
We already know what those models are good at. Everybody keeps posting their cherry-picked good results.
Why not get the chance to see some failures, too? Isn't it interesting to know what those models are bad at? There's too few examples of that around so that is definitely a very thing to know.
Wait, why is this the wrong comparison? Where does it say that those models can't generate graphs? Why is everyone so sure that this is not one of the intended applications of image generation?
Who made up those rules that drive nerds to rage when they're broken? Where are those rules written down? Can you point at them? Or have people just made up those rules in response to that post?
> Those tools are for generating images and art, not precise schematics.
Who says that? Where does it say that Dall-E and Stable Diffusion are only for generating images and art? Why are graphs not images? And why can't they be art?
Those are models that generate images from textual prompts. Aren't you just moving the goalposts by saying they can't generate specific kinds of images?
The pictures on the homepage are astronauts and flamingos and stuff.
The homepage defines the process: "starts with a pattern of random dots and gradually alters that pattern towards an image when it recognizes specific aspects of that image"
So based on that description and context clues, I wouldn't expect it to generate a precise schematic.
If I understand your interpretation correctly, you wouldn't expect it to generate anything else precise, either. For instance, you wouldn't expect it to be able to generate the precise contours of a face, correct?
After reading your comment I asked Stable Diffusion to create photorealistic images of a graph with three lines (similar to the smallest prompt in the article).
Here's the results of three attempts with slightly different prompts:
OK, so people complain that Stable Diffusion and friends are trained on art so it's not fair to ask them to produce graphs because graphs are not art. So I asked them to produce artistic graphs.
Here are the results of the prompts "a graph with 3 lines in the style of X" where X in {Rembrandt, jackson pollock, studio ghibli, escher, van gogh}:
It is, yes. For example, a neural network can't invent a new art style on its own, or at least existing models can't, they can only copy existing art styles, invented by humans.