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Maybe some current implementations lack the ability to make these connections but it is in now way even a small stretch to conceive a machine that understands "

Maybe some current implementations lack the ability to make these connections but it is in now way even a small stretch to conceive a machine that understands "Wheels are for moving" "Seats are for passengers" "Things that have both wheels and seats are probably vehicles". So when that machine learning algorithm recognizes wheels in a picture and recognizes seats in the same picture, it searches for results that include both wheels and seats. The human brain does not inject any magic in to this process.

mdorazio 10y ago
It sort of does, though. Let's say we train an ML implementation so that it can recognize things with wheels and seats as vehicles. Now we show it a hovercraft. What will it do? How about a helicopter? All the human brain needs is a single example of people getting in or on something, and it transporting them from point A to point B in order to infer that the thing is a vehicle of some sort. This is because we are able to infer purpose of an object even if we have never seen it before. ML is just statistics - it implies no meaning or comprehension whatsoever beyond "thing A is statistically most like thing B I have seen before". There's an important difference between recognition and understanding, and current ML techniques are solidly in the former camp.