ou get new functions that's the point like you know if the main relationship you have to reality is adaptivity you have to be able to evolve which means you have to introduce variation difference but you have to select it down and integrate it together so complexification it gives you new functions that continue your ability to adapt to a constantly and changing environment yes that goes in with the fact that if you remember we talked about these higher states of consciousness like insight are pers are preceded often by disruptive strategies this goes into machine learning i talked about this last time like if you're doing neural networks and you're trying to get them to learn from the environment you periodically throw in noise and disruptive strategies because if you don't they'll over fit to the data they'll lose the ability to generalize yes okay so yeah okay go ahead go ahead i think i think this is very interesting and i think it's particularly interesting because um as as technical as you're trying to lay it out which is which is commendable and and a great effort in itself it's not simple to to to break something down in its concession point so so finally which is very commendable there's a there's a spinosistic element to that as well i think i think that i think i think that together with that there's also a deep um and i keep repeating this because it keeps occurring to me there's a deep um sense of of the realness of what you're talking about because because we can all think of examples in our own lives in the lives of others where we where where these things happen where for either for the positive negative and we're going to see how those things function so we can all know what it means like to have something fitting too tightly to data and therefore being useless so so i think i think that there's there's a relationship here as well which is an optimal grip between the the specific the specific specificity and then technicality of this layout and also the relatability to real world which are quite