| "What does a neural network actually do?" This is a fundamental Question:
Can we really say and predict what a neural network does? Contrary to an engineered / constructed algorithm a neural network is 'trained'. Whenever we will present a 'known' input pattern it will reposnd with a 'learned' response. This however introduces interesting problems: How can we _debug_ a neural network? How can we debug a correlation? Sure we can tune its paramters, we can train it some more to again show the desired response. But reaching this point we just abandoned knowing how the intrinsic algorithm works in favor to just focussing on the result. Okay - now if we follow this argument - this would lead to: If we simulate the whole brain by simulating the neural network, we won't gain any knowlege about the intrinsic workings of the brain. We won't find any enlightenment about the innermost algorithm _represented_ by the neural network we call our brain. |
Neural networks, Support Vector Machines, Hidden Markov Models and other stuff (Markov Networks, etc) do something like linear regression on some huge space - they draw a curve/plane between groups of things on this feature space. The tendency is for this division to make sense and to correspond to our common sense categorization of these things.
The problem is that once that happens, you can't really reason about the division you've drawn. It's just there. You can tweak for various purposes but that's a manual process.
You categorize animals by shape or particular adaptation or by genetic makeup. You can teach one of these algorithm each of these categorizations. But you can't do something like have the thing categorize for one purpose and then tell it to "change it's outlook" and categorize for a different purpose.
In this sense, despite seeming impressive, the products of these processes are dead-ends that we can't reason about, that lack the flexible intelligence of a human being.