I think image generation is an interesting case, because even if a human is always in the loop, and you need to try several times before you get a good image fo

I think image generation is an interesting case, because even if a human is always in the loop, and you need to try several times before you get a good image for your prompt of interest, that's likely still faster and cheaper than photoshopping exactly what you want (or certainly faster than hiring an illustrator). And the images produced are sometimes really quite good. A model which produces some amount of really messed up images can still be 'useful'. _However_ the kinds of failures it makes highlight that these models still lack basic background knowledge. I'm willing to let the stuff about compositionality slide -- that's asking kind of a lot. But I do draw a very straight line from DeepDream in 2015 producing worm-dogs with too many legs and eyes, style-gan artifacts where the physical relationship between a person's face or clothing and surroundings was messed up, and the freakish broken bodies that stable diffusion sometimes creates. Knowing about the structure of _images_ only tells you a limited amount about the structure of things that occupy 3d space, apparently. It knows what a John Singer Sargent portrait looks like, but it's not totally sure that humans have the same number of arms when they're hugging as when they're not. In the same way, large language models know what text looks like, but not facticity. So I don't know that an AI winter is called for. But maybe we should lean away from the AI optimism that we can keep getting better models by training against the kinds of data that are easiest to scrape?