I’m not sure if it will actually work in practice, but I have a loose adaptive predictive model I’m currently brainstorming:
A is either researched input or random input. B can be taken as a decision tree or predictive model. C is an adapted conditional from result of B. D is a direct result from C. Global else happen when danger exceeds danger threshold. The backed up repo is taken is a new seedling, and sprouted. If danger_level high Auto clone neuron to previous direction. Encrypt old processing. Else If a is word, definition, or related topics, adapt b to word, definition, or related topic: If prediction b is a word, c is send to word document. perform d Else if b is a definition, c is send to definition document. perform d Else if b is related topics, c is send to related topics document. perform d Check for danger if danger exceeds threshold go to high danger processing else repeat process
Is there any way I could maybe improve it?
Also, the idea is that if a certain amount of danger reaches a specified threshold, it goes into automatic cloning and encryption mode.