what is enough ?

I’ve been making some unexpected progress, I found a learning mechanism which is both simple and reliable and integrate 100% with all of my other ideas. Now, I’m again building a small test to show learning of colors with 3 input neurons. It’s taking a lot of time because since I changed the code to incorporate more C/C++, parts of the code are not working properly or failing completely… So perhaps another week maybe two till I can show my idea in practice. All the test I’ve done so far are in a sort of a manual network, I link by hand neurons, initiate them one by one and such..

Anyway the problem I have now is when to stop “learning”.. The way it works now is as follow : the network learns by itself up to values, then if I want to separate patterns and learn them separately even further, I have to tell it to learn.. Learning behaves like dopamine or serotonin influx, so some constants are altered by an external (or different) mechanism.. Trouble is that this learning would go on till the very limits of the input data.. Assume we have a 10 synapse pattern.. While I see it as a pattern, if I decide, I could also go deeper (up to my visual acuity) with dissecting that pattern into smaller patterns… So I may be able to discard 5 synapses (points) from that pattern and consider they do not meet all criteria to be part of that pattern… And then to the best of my sensory perception I can’t find other differences among the remaining 5 synapses.. My AI does just that at this point… discards everything up to those 5 remaining patterns because I’m not sure how to define criteria that would stop it before reaching that very end.. So how do we know enough is enough ? How do we decide what to learn ?

Anyway, this is now a very very different problem than the ones I had so far.. The important point is that under clearly defined conditions the system has a mechanism of learning. This is all very new to me, so I may get some ideas later on. Right now I’m focusing on building the whole system back to its original functionality and building the color discrimination demo along the way.

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