I asked myself the same thing as I typed that comment, and I'm not sure what the answer is. I don't think models are specifically trained on this (though of course they're trained on how to generate SVGs in general), but I'm prepared to be wrong.

I have a feeling the most 'emergent' aspect was that LLMs have generally been able to produce coherent SVG for quite a while, likely without specific training at first. Since then I suspect there has been more tailored training because improvements have been so dramatic. Of course it makes sense that text-based images using very distinct structure and properties could be manipulated reasonably well by a text-based language model, but it's still fascinating to me just how well it can work.

Perhaps what's most incredible about it is how versatile human language is, even when it lacks so many dimensions as bits on a machine. Yet it's still cool that we can resurrect those bits at rest and transmogrify them back into coherent projections of photons from a screen.

I don't think LLMs are AGI or about to completely flip the world upside down or whatever, but it seems undeniably magical when you break it down.

Google specifically boast about their SVG performance in the announcement post: https://blog.google/innovation-and-ai/models-and-research/ge...

You can try any combination of animal on vehicle to confirm that they likely didn't target pelicans directly though.

They are, and it is very funny.

https://www.behance.net/gallery/35437979/Velocipedia

What’s your point? Yes, humans fail sometimes, as do AI models. Are you trying to imply that, in light of this, AI is now as capable as human beings? If so, that conclusion doesn’t follow logically.