How convolutional neural networks see the world
SPOILER WARNING!<p>Nice read at the end of the article:<p><i>&quot;[...] Naturally, this does not qualify as &quot;seeing&quot; in any human sense, and from a scientific perspective it certainly doesn&#x27;t mean that we somehow solved computer vision at this point. Don&#x27;t believe the hype; we are merely standing on the first step of a very tall ladder.</i><p><i>Some say that the hierarchical-modular decomposition of visual space learned by a convnet is analogous to what the human visual cortex does. It may or may not be true, but there is no strong evidence to believe so. Of course, one would expect the visual cortex to learn something similar, to the extent that this constitutes a &quot;natural&quot; decomposition of our visual world (in much the same way that the Fourier decomposition would be a &quot;natural&quot; decomposition of a periodic audio signal). But the exact nature of the filters and hierarchy, and the process through which they are learned, has most likely little in common with our puny convnets. The visual cortex is not convolutional to begin with, and while it is structured in layers, the layers are themselves structured into cortical columns whose exact purpose is still not well understood --a feature not found in our artificial networks (although Geoff Hinton is working on it). Besides, there is so much more to visual perception than the classification of static pictures --human perception is fundamentally sequential and active, not static and passive, and is tightly intricated with motor control (e.g. eye saccades).</i><p><i>Think about this next time your hear some VC or big-name CEO appear in the news to warn you against the existential threat posed by our recent advances in deep learning. Today we have better tools to map complex information spaces than we ever did before, which is awesome, but at the end of the day they are tools, not creatures, and none of what they do could reasonably qualify as &quot;thinking&quot;. Drawing a smiley face on a rock doesn&#x27;t make it &quot;happy&quot;, even if your primate neocortex tells you so.</i><p><i>That said, visualizing what convnets learn is quite fascinating --who would have guessed that simple gradient descent with a reasonable loss function over a sufficiently large dataset would be enough to learn this beautiful hierarchical-modular network of patterns that manages to explain a complex visual space surprisingly well. Deep learning may not be intelligence is any real sense, but it&#x27;s still working considerably better than anybody could have anticipated just a few years ago. Now, if only we understood why... ;-)&quot;</i>