+[This file may describe an older version than the current code]
+
Trying to make GPTs build their own "culture".
+Francois Fleuret
+Jun 21st, 2024
+
* Motivation
The original motivation of this experiment is the hypothesis that
There are 5 competing GPTs.
-The "world" is a 7x9 grid with three "birds" moving in a straight line
-and bouncing on the world's borders. The colors correspond to a fixed
-"z-buffer order". It could be another "world", but this one has
-objectness, occlusion, and motion.
+The "world" is a 6x8 grid with three "birds" moving in a straight line
+and bouncing on the world's borders. It could be another "world", but
+this one has objectness and motion. There are ten colors and 4
+directions of motions, so roughly (6x8x4x10)**3 ~ 7e9 states.
Given a random world state, and the state after two iterations of
birds moving, a "quiz" is to predict the second frame, given the
-first, or the opposite.
+first, or the opposite. The starting and ending states are chosen, by
+rejection, so that there is no occlusion.
My home-baked GPT-37M trained with 250k solves this with ~99% success
[to be verified with the new setup].
At every iteration, we select the GPT with the lowest test accuracy,
and run one epoch.
+* Creating new quizzes
+
If its test accuracy got higher than 97.5%, it will create new
quizzes. To do so, it generates a large number of pairs of frames, and
checks which ones of these quizzes are hard but not too hard, which
-means
-
-[THIS IS THE IMPORTANT BIT]:
+means [THIS IS THE IMPORTANT BIT]:
-it can be solved, in both time directions, by all the other GPTs **but
-one**
+ it can be solved, in both time directions, by all the other GPTs
+ **but one**
The both time directions is to avoid a simple type of quizzes which is
simply to deal with noise in the first frame.
projects / culture.git / blobdiff