> 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.
Can you, with enough time, actually walk through the steps that are made in
a neural network? If not, why not? If so, then it is an algorithm.
>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.
How does the network work? Does each element of the network have a set of
rules. Is there a set of rules for passing information. It the timing well
established. Are the results repeatable? If not, can the physical
variations in timing be represented by a probability distribution?
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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