Yeah, but it&#x27;s not obvious if every single frame of the whole movie children have experienced is necessarily important.<p>To put it one way, what is the _rate_ of learning? How many _concepts_ do children learn per unit time?<p>Concepts: Our computer systems take whole images and train on them, but before a pixel-based image gets to the learning part of a child&#x27;s brain, it has probably been conceptualized in some way already.<p>Rates: And then, from this feature vector—maybe 1 feature vector per 10 seconds, for a child—how often is it incorporated in a learned concept? Maybe one concept per hour? Per day? Think about how challenging it is for children to learn vocabulary. A young child doesn&#x27;t know that much.<p>The important thing is that a child is a self-learning machine. It chooses what&#x27;s important and has a way to shift its attention, a critical enhancement of new (2015) AI approaches, deep learning and otherwise. It can explore its own world and somehow choose the &quot;training instances&quot; that matter.<p>By comparison, a neural network training for days looks at millions of images. A very high rate. But the most effective approaches (the massively-multilayer networks, the deep in deep learning) are making concrete what a child&#x27;s brain must do already: separate perceptual components from learning components, in a conclusively more structured and hierarchical way.