Update README.md

README.md

@@ -53,4 +53,11 @@ Based on this random sample, I would estimate the following dataset statistics:
 * 5-7% of images may have minor edits or annotatations (timestamps, color grading, borders, etc.)
 * 1-2% of images may be copyright-constrained (watermarks or text descriptions cast doubt on the license metadata)
 * 1-2% of images may be non-wholesome (guns, suggestive poses, etc.)
-* 1-2% of images may be non-photos (paintings, screenshots, etc.)
+* 1-2% of images may be non-photos (paintings, screenshots, etc.)
+
+### Is 10 million images really enough to teach a neural network about the visual world?
+
+For the parts of the visual world that are well-represented in Megalith-10m, definitely!
+Projects like [CommonCanvas](https://arxiv.org/abs/2310.16825), [Mitsua Diffusion](https://huggingface.co/Mitsua/mitsua-diffusion-one), and [Matroyshka Diffusion](https://arxiv.org/abs/2310.15111)
+have shown that you can train useable generative models on similarly-sized image datasets.
+Of course, many parts of the world aren't well-represented in Megalith-10m, so you'd need additional data to learn about those.