I'm not sure it's as simple as you say. The first time my very young son saw a horse, he made the ASL sign for 'dog'.

He had only ever seen cats and dogs in his life previous to that.

Did he require 9,999 more examples of horses before learning the difference?

In another comment I replied that 3D high fidelity images do end up being thousands of training samples, so the answer is yes.

I'm deeply skeptical that training AI on (effectively) thousands of images of one horse will perform very well at training to recognize horses in general.

I'll double down with you on this.

Then train the AI using a binaural video of a thoroughbred and see if it can distinguish a draft horse and a quarter horse as horse...

Are you suggesting that if a group of kids were given a book of zoo animals before going to the zoo, they would have difficulties identifing any new animals, because they only have seen one picture of each?

I think that's an interesting question, and a possible counter to my argument.

Certainly kids learn and become better at extrapolation and need fewer and fewer samples in general as they get more life experience.

Hah. My toddler gladly calls her former walking aid toy a "lawn mower". Random toys become pie and cakes she brings to us to eat.

But we have a lot more sensory input and context to verify all of that.

If you kept training LLMs with all that data, it would be interesting to see what the results would be.