BrainX & CrX

  [CrX Architecture Cont..] The return When the second network's output was returned as input to the first network, this was done to update the clusters in the first network. The next time when the input comes into the model, the updated clusters will correctly understand the input in a very short time. Math behind the return The model outputs the pixel values. The return of the output as input to the first network is to store the closely related clusters in the same layer of the first network, which would not be possible without returning the output. The returning-of-the-output stage contains the finalizer function where this function takes the output and finds similarities between the output for each output. If it checks with the previous accumulated outputs and if similarities are the same for at least 3 outputs from the second network, then this output is returned to the first network as input.   Ø Purpose of returning the output as input - This concept was...

 [CrX Architecture Cont..] The cluster It is hoped that clustered neurons represent the smallest truth, while others are just noise. So, by activating the smallest truth, we will always get an output, even if it is the wrong output, similar to the brain. By increasing the smallest truth, we can improve the output to more accurately manifest reality. And by allowing only the smallest truth/cluster to activate and move forward in the layer, we can obtain the right set of outputs. Math behind clustering algorithm The partitioned blocks serve as input for clustering. Clustering occurs for random neurons within any partitioned blocks that are close to each other. By clustering the closely arranged random neurons, the algorithm fixes them into that position, while random neurons that do not meet this criterion are set to normal again (they are not fixed). Only the values (scaled pixel values) of active chained neurons that are surrounded by clustered random neurons are allowed to m...

  Documentation of CrX architecture               This Documentation talks about the CrX – Cognitive Revolution X architecture with a detailed step-by-step explanation, which was developed from BrainX theory. This architecture will initially function as a general problem solver/AGI. Ø   N – network Ø   P – partition gives specification Ø   C – cluster gives direction / dynamics Ø   R – returning of output   Ø BrainX theory into CrX architecture The brain processes input and allows it to travel throughout its pathways, regardless of where it originated (it does not discriminate). Neurons serve as the paths for impulses to travel, with these paths branching in various directions, thereby altering the direction of the impulse at any given time. The impulse is not consciously choosing its direction; rather, it changes direction due to the laws of physics and biological assistance. Se...