I have started on Andrew Ng's machine learning course. It seems that machine learning is learning correlations with known data based on as many parameters as possible. For example, if we collect data on existing property prices with information on the land area, built-in area, type of building, age of the building, etc, it is possible to predict the price of another property if we input the value of the various parameters of this property.
Similarly, if we keep the images (the black and white pixels) of cats, we can tell whether a new picture is a cat if it bears some resemblance to the pixels of existing labeled cat images.
This approach sounds great, but is it practical? How much effort and zettabytes of data do we have to keep just to reach the brainpower of, say, a 3-year old, who can recognize dogs, cats, tigers, a Mustang, trucks, a hamburger restaurant, and so on?
Why does everyone have to repeat the effort of learning the same things?
If Google has already learned cats, or if someone already has a program to recognize handwritten digits, can this knowledge be shared and re-used? Or is it just a matter of paying for them?