Noam Chomsky would argue it's because (nearly) all of us are born with some constrains in our brain designed for dealing with our world.
At the level of object recognition we are discussing now, the "constraint" (more accurately learning bias) comes in the form of very advanced neural architecture that is able to learn in spatial environment. Not in some kind of pre-trained neuron weight collection.
We have some very particular biases like fear of snakes or heights, but learning to recognize spatial objects is something very general.
Re:A New Twist on Neural Networks
by heavenlyblue on Friday November 03, 2017 @07:43AM
(#15618350)
Fear is neither prover or disproven to be a genetic trait.
Human brain receives roughly 25-50 images worth of data per second, so less than 50 samples per second. (consciously we observe only 25 images per second).
Short-term synaptic plasticity works on a timescale of 20ms to few minutes, so also roughly 50 times per second timescale.
If I try to translate this to deep learning framework, it would mean max 500 training steps in 10 seconds per neuron.
Learning to recognize cat using _unsupervised learning_ in just 10 seconds would be really impressive.