Theory about the purpose of neural networks

submitted by trident765 from self.programming

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[–]thefirststone 3 insightful - 1 fun3 insightful - 0 fun4 insightful - 1 fun -  (3 children)

The point is for it to learn itself based on external stimuli.

Its peculiar initial structure allows it to modify itself. Its inscrutable final structure comes from it finding solutions by random walks. Obfuscation is a side effect of automation that only requires declarative requirements, rather than true programming.

That they are inscrutable replacements for human labor is mostly a happy accident, though inevitable.

What they call "AI" and "ML" in the media is just this: https://en.wikipedia.org/wiki/Genetic_programming

[–]Zapped 1 insightful - 1 fun1 insightful - 0 fun2 insightful - 1 fun -  (0 children)

Wouldn't there need to be some sort of "evolution" event, and not just a learning curve, that allows programming to become sapient, and therefore use imagination as a tool for advanced thought and problem solving? I was just having a conversation today with a medical doctor about this very subject. He brought up the fact that computers are able to do so much more computations because they are able to use so much more energy than the human brain. The brain is capped at 2.5 watts due to overheating.

[–]yoke 1 insightful - 1 fun1 insightful - 0 fun2 insightful - 1 fun -  (0 children)

well now it's more than GP though...

[–]saidittwice 1 insightful - 1 fun1 insightful - 0 fun2 insightful - 1 fun -  (0 children)

'to learn itself' is not quite accurate, as the developer has to supply the judgement of right or wrong (correct/incorrect) to the lessons being learned (programmed). That means lessons have to be composed (training data) with many examples - which requires human interaction at some level within the knowledge domain the NN is being built for.

The more complex the knowlsdge is, the more the NN machine can be confused and have to be carefully guided so that it will operate correctly. This also includes the shape of the Neural Net - (the layers and neuron counts) which are estimated for the kind of inputs and outputs of the knowledge task the NN is supposed to do.

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