"A human brain can learn to recognize cat pictures given a relatively small amount of input versus a convolutional neural net." is not really accurate, this is

"A human brain can learn to recognize cat pictures given a relatively small amount of input versus a convolutional neural net." is not really accurate, this is an apples and oranges comparison. A human brain that's already learned to see and recognize objects can learn to recognize cat pictures from a small amount of input data, and a neural network that's trained to see and recognize objects (which explicitly didn't include cats) can also learn to recognize cat pictures from the same amount of input data. One-shot learning or few-shot learning is a thing and gets reasonable results. And a human brain that's learning to see "from scratch" (i.e. infants) can't learn from a few pictures, it needs huge amounts (many months) of rich image data - i.e. not passive but interactive/experimental, which is known to be neccessary for visual system development in mammals (I recall a bunch of cat experiments) and is known to have much, much better sample efficiency for artificial systems. So as far as I know there's no good reason to assume that human brain can learn cat pictures from less input than neural networks. What we're seeing and misattributing to the "learning capabilities" here is actually the effectiveness of transfer learning, transferring knowledge and skills everyone's learned as a kid to new (but actually very related) problems.