I mean, how many humans can draw art by writing out svg? If that's not in the training set, I don't even see how GPT-4 gets much better at this over time.
And so, if we see that it /does/ get better, over the next few years, will that not lead us to ask /how/?
Let's think about it:
1. It has to output SVG [1]
2. It is given a text based representation of what it must draw[2]
3. It must then somehow convert words -- the concept of a unicorn: equine with a horn, white, maybe rainbows? -- into SVG code, and attempt to convey both their location, shape, colour, appearance, with code.
And keep in mind, this is just a token predictor. I doubt there is much data in its training that is this specific.
So while it's quite far from science, for me, it's a bit of fun and I get emails every now and then remarking on things like the turd of May (2023-05-18) and it lightens the mood every now and then, which I think ultimately, is worth it.
[1] System: You are a helpful assistant that generates SVG drawings. You respond only with SVG. You do not respond with text.
[2] User: Draw a unicorn in SVG format. Dimensions: 500x500. Respond ONLY with a single SVG string. Do not respond with conversation or codeblocks.
GPT-4 is really great at transferring concepts between domains.
That's one the reasons why GPT when it works, feels magical. SVG art does not need to be in it's training set, as long as it knows how to present geometric concepts in SVG.
A good unicorn would require capabilities something like "the outline of unicorn is composed of lines {...}." -> "export lines as svg".
