"Dan Minette" <[EMAIL PROTECTED]> wrote:
>
>How are neural networks not algorithmic? Each node has a simple set of
>rules, a simple algorithm. The timing of them passing information can also
>be described by an algorithm. Perhaps timing difficulties exist, so the
>same answer will not be obtained twice with the same input, but that can
>also be described algorithmically, through the use of a pseudo-random
>number
>generator.
True - but only in the most pedantic way. You typically can't write an
algorithm for what a neural network does that doesn't involve "insert a
neural network here" as a step. For example, image, voice, or optical
character recognition. You can train a network to do things that we're thus
far unable to manually construct algorithms for. It may even be that the
simplest algorithm for the task (recognize X with some probability of
success in some limited number of steps) does involve a neural network.
Consider a computer performing addition with a binary adder vs. recognizing
a square with a neural network. We can describe the algorithm of the former
rather simply. What algorithm is the latter using? Can you describe it in
any simpler terms other than as a box containing the entire process itself?
Joshua
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