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This post, while very interesting, attempts to draw a completely unwarranted parallel between deep nets and the human brain, as if layers of artificial neurons

This post, while very interesting, attempts to draw a completely unwarranted parallel between deep nets and the human brain, as if layers of artificial neurons running on a GPU and the cortical layers of your brain were two interchangeable things. So far, there has been no evidence that the brain works anything like an artificial neural network. Maybe it does, and there are several theories in that direction, but at the moment we have no solid reason to think so.

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lscharen 12y ago
The point of drawing comparisons to the human brain is that we know how quickly humans can perform visual recognition tasks and speed of signal propagation between neurons. Combining these two properties implies that the human brain is able to solve these tasks without feedback, i.e. no loops. Thus, a DNN should be able to perform similar tasks if it can be trained (which it can). Recurrent neural nets add feedback and are are whole different kettle of fish.
- LoSboccacc 12y ago
  The brain also appears to have dedicated network structures that are not trained, but constrained, i.e. programmed to one transformation, say, the equivalent of feeding both an image and it's edge enhanced version to the same DNN. Current approach of feeding raw bitmaps to DNN falls short of that and is very sensitive to training data[1] I remember an old paper I cannot find now about how to normalize image for NN processing in face recognition. Software extracted the face, centered it on a square and projected that square on a circle around the center to make face orientation irrelevant (hard to explain without images) Anyway, it is unfair to expect a DNN to perform vision recognition tasks from raw bi-dimensional image points. [1] http://www.i-programmer.info/news/105-artificial-intelligence/8064-the-deep-flaw-in-all-neural-networks.html http://www.i-programmer.info/news/105-artificial-intelligenc...
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lscharen 12y ago

The point of drawing comparisons to the human brain is that we know how quickly humans can perform visual recognition tasks and speed of signal propagation between neurons. Combining these two properties implies that the human brain is able to solve these tasks without feedback, i.e. no loops. Thus, a DNN should be able to perform similar tasks if it can be trained (which it can). Recurrent neural nets add feedback and are are whole different kettle of fish.

LoSboccacc 12y ago
The brain also appears to have dedicated network structures that are not trained, but constrained, i.e. programmed to one transformation, say, the equivalent of feeding both an image and it's edge enhanced version to the same DNN. Current approach of feeding raw bitmaps to DNN falls short of that and is very sensitive to training data[1] I remember an old paper I cannot find now about how to normalize image for NN processing in face recognition. Software extracted the face, centered it on a square and projected that square on a circle around the center to make face orientation irrelevant (hard to explain without images) Anyway, it is unfair to expect a DNN to perform vision recognition tasks from raw bi-dimensional image points. [1] http://www.i-programmer.info/news/105-artificial-intelligence/8064-the-deep-flaw-in-all-neural-networks.html http://www.i-programmer.info/news/105-artificial-intelligenc...

deleted 12y ago

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