X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=sidebyside;f=README.txt;h=29344462a3eb95014b1a00c65d31cdd7fd83ea71;hb=336130cc923761658029a0af9d5862d59405d47a;hp=b5c410975a0408a4930475b9c301b2aab4431680;hpb=27dea0ab0448511236cb344c17162e84359a14ee;p=culture.git

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-For the stack experiments:
 
-./main.py --task=stack
+Trying to make GPTs build their own "culture".
 
-./main.py --task=stack --stack_fraction_values_for_train=0.75
+Francois Fleuret
+Jun 21st, 2024
 
-./main.py --task=stack --stack_fraction_values_for_train=0.75 --stack_nb_stacks=3
+* Motivation
 
-Each takes ~1h10min on a 4090.
+The original motivation of this experiment is the hypothesis that
+high-level cognition emerges from the competition among humans in the
+space of language and ideas.
+
+More precisely, communicating agents try to out-do competitors by
+creating stuff that is smart but doable, e.g. some other agents get
+it, but not all. Then, that smart thing is added to the "culture",
+they all learn and get to understand it, and it repeats.
+
+* Setup
+
+It starts with a "world model" that they got before they communicate,
+and from there, they try to "be smart" by proposing quizzes that can
+be solved but not by everybody.
+
+There are 5 competing GPTs.
+
+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. 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]:
+
+  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.
+
+The GPT generates 1000 of such quizzes, that are added to the
+"culture", i.e. the training set.
+
+We update the test accuracy of all the GPTs, and then we go to the
+next iteration.
+
+The hope is that interesting concepts emerge (connectivity, symmetry,
+interior/exterior, shape vocabulary, etc.)