X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=README.txt;h=1bd8dbc3bc648f75d7c7f8017a9452eadd83c708;hb=36a1440d01cc15643849f5ba421f89ac403ccd82;hp=af96ee9d73173cc05be312e3e4d0d95882a8f8ea;hpb=4ec52fe66419a6e1d2b231108ccbb45902395fcc;p=culture.git

diff --git a/README.txt b/README.txt
index af96ee9..1bd8dbc 100644
--- a/README.txt
+++ b/README.txt
@@ -20,22 +20,25 @@ be solved but not by everybody.
 
 There are 5 competing GPTs.
 
-The "world" is a 6x8 grid with one or two "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
+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.
 
 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.
 
-My home-baked GPT-37M trained with 250k solves this with ~99% success.
+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. 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
+and run one epoch.
+
+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]:
 
@@ -48,7 +51,8 @@ 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.
 
-Then training resumes.
+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.)