Deep Neural Networks Don’t Need To Be All That Brain-Like

I came across an interesting post on Deep Learning / Deep Neural Networks which was related to my previous post. The interesting point about that post, which I had in the back of my mind while writing my previous post about neural networks, was that Deep Neural Networks aren’t designed for replicating the full extent of Human intelligence. What they are getting really good at is replicating only a part of what the Human brain does, but a part which is important and incredibly useful and effective for specific engineering problems such as classification.

One specific thing that Deep Neural Networks do well is the task of classification at very large scale. The Human brain is pretty slow and even though it packs in an enormous amount of neurons that work in parallel, way more than any computer or cluster of computers is able to model, it needs a lot of neurons to classify one image, and can only classify a small number of images at once. Deep Neural Networks can sift through millions or more images per day without getting tired or having to sleep and the process can be cheaply replicated across tens or more thousands of computers. They do need a steady diet of electricity, but that is in relative abundance (though limited and has it’s own set of issues).

Deep Neural Networks aren’t going to give us Strong AI anytime soon, but they are beginning to be able to perform certain tasks faster and more accurately than humans.


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