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I think we pretty much agree, identification can be done just from images, learning only from them asks for quite a lot. The issue is probably that annotating t

I think we pretty much agree, identification can be done just from images, learning only from them asks for quite a lot. The issue is probably that annotating thousands of images is a comparatively easy task when compared to doing this on years of video from a camera moving through the world. And who has the time to watch a neural network ingesting videos for several years? But something like that may be necessary if you want to achieve human-level performance by learning from scratch, watching billions of video frames. I guess you can take a shortcut, sort of, and get away with a lot less training examples but only if you engineer a lot of the fundamentals about the world into the system and avoid having to learn them, too, and all in one process.

ianai 9y ago
What I’m trying to say is AFAIK, AI is generally solving a problem through training something akin to a black box. It’s data -> function -> expected output. But I think we’re able to get past silly things like a pixel off or other simply “odd” images because our learning is much more involved. I’d liken it to be along the lines of data (with many data associated to our own, known input ie pushing against a car, petting a cat, looking in a mirror and moving our arms and realizing we “did that”, etc) -> contextualizing based on memories -> identifying relevant concepts (chair, car, color/blue, etc) -> thinking/doing something -> other steps -> repeat This is also where I think robots have the best chance at really becoming self aware (far down the road). They’re not going to be passive observers.