We spend so much effort trying to engineer intelligence, when we would get a lot farther reverse engineering intelligence. Whenever AI makes a big advance the a

We spend so much effort trying to engineer intelligence, when we would get a lot farther reverse engineering intelligence. Whenever AI makes a big advance the analog was already known my neuroscientists. The problem with attempting to understand intelligence by reverse engineering the human brain is that we cannot know a priori which aspects of the human brain are necessary for intelligence to arise, and which are merely consequences/side effects of biology and chemistry. Once we discover some technique that works in a practical setting (e.g. on ImageNet), then it is fairly straightforward to find the biological analogy in the brain. In fact, Geoff Hinton explicitly advocates an approach of "try things, keep what works, and figure out how it relates to the brain". The inverse is like finding a needle in a haystack. There is also clearly no comprehension of the importance of the topological (circuitry) defining a neural network. We always assume a fully connected network, and draw the out as such, but we don't stop to consider that many of those Wijk interactions are completely spurious, meaning they have no information bearing role. The purpose of training a deep neural network from data is to automatically discover what the topological circuitry of the network should be, rather than engineering it by hand. In the brain, some prior knowledge is encoded via genetics, while the rest is learned. The effect of sparsity of the weights in deep neural networks is an active area of research [1]. If you strip them away you'll start to reveal the underlying circuit at work. I've published theoretical results using artificial gene networks, but the results should be similar for ANNs. Very interesting. If I understand correctly, the cost you are attempting to minimize is phenotypic variation, which you measure as the gross cost of perturbation (GCP). Would this cost be analogous to sensitivity to adversarial examples in the case of convolutional neural networks [2]? [1] http://www.jmlr.org/papers/volume14/thom13a/thom13a.pdf http://www.jmlr.org/papers/volume14/thom13a/thom13a.pdf [2] http://arxiv.org/abs/1412.6572 http://arxiv.org/abs/1412.6572