All machine learning models that I've trained have involved a data augmentation step. These are artificial augmentations though, so not as effective as more real data. Looking at the same horse live from 100 different perspectives might be better for learning what a horse is than training on 100 static images of different horses.

Yes, data augmentation is standard. It isn't new data though, just different variations of the same data. You also tend to do multiple training passes over the data, even after augmentation.

None of that is the point though: a child can look at a single static image (not 100 different perspectives - just one perspective) of a horse, and learn to recognize a horse. A standard machine learning model cannot, no matter how much you augment the image.

> a child can look at a single static image

But in an important way, they can't.

You need to define "look". How many nanoseconds? How much is the lighting changing during that time? How still is the photo? How still is the person's head? In DL a training example in a batch is perfectly defined in bits. Once you try to define a training example for a human you see that a "single static image" means totally different things. A human seeing a static image is the equivalent of training a model on at least thousands, if not millions+ of training images.