That toddler has already processed lots of visual image data, examples of objects, nonliving and living, animals, mammals, etc. Don't you think that constitutes a large, important dataset for the problem of elephant recognition?
Nevertheless, if you provide your machine system with the video of all the child's visual input, it still won't generalize well from single examples, the way children do effortlessly.
Humans generalize well from few examples because, well, they've already processed billions of examples. A toddler may have never seen an elephant before, but it may have seen cars, trucks, birds, dogs, people, trees, skies, buildings etc, giving it concepts for bigness, smallness, aliveness, humanness and much else. With all these concepts in place, then yes it becomes easy to see what makes an elephant distinct from a dog or a person. And it would be too for an artificial neural network.
An interesting fact is that newborns have very few concepts to begin with. It takes some months for them for instance to learn to differentiate between alive and dead things (the family cat vs a teddy bear for instance).
If you show a child 1000 images or animals. Then show different photographs of animals. And tell the child what animal each animal photograph is, you can now go back to the original 1000 and the explained ones will likely be recognized dispute them never beig initially sorted, or modeled as such.
Going from 5-10 to 1,000,000 is what computers have a problem with. They go from 1,000 to 1,000,000 easily, or even million to billions.
What I'm talking about is a child can see a photo of an unknown animal, I can show that child a cartoon elephant (which is the original animal). I then ask what the original animal is, the child likely responds correctly.
Reprocessing of already learned data as the scheme of the world changes based on new information.