Progressing yet.

I have been working on the so-called “Democratic approach project”. Under the project, I attempt to make a democratic AI system where AIs make decisions based on their voting outcome.

I started to make multiple models or AIs.

First AI I made was accidentally favourable. I call it Liselotte.

A problem, however, arose when I tried to create the second AI called Alma.

Issues that occurred during the process are below.

1.    Overfitting

2.    Outputs of 0 are too frequent (70%~100% of whole predictions it generates)

When I try to fix #1, the overfitting problem, by adding a Normalization Layer such as dropout, problem #2 appears and vice versa.

There are mainly three variables in the equation to solve this dilemma: 1. Amount and intensity of Normalization Layers. 2. Epochs. and 3. The chosen optimizer.

Other factors influence the results besides the training phase: 1. Data arrangement. And 2. Model or layer architecture (slightly overlapping with the three variables above)

Occasionally, I find a better combination of them and get the acceptable result as a model, in other words. I get another AI joining the AI democratic congress.

I have so far made three AIs called Liselotte, Alma and Vanessa. Alma and Vanessa seem much superior to Liselotte. Therefore, I plan to exile Liselotte from the assembly, but it is still under consideration.