A possible way this solution wouldn’t hold is if we consider the case of a compressed lookup table. Meta-learning could have a shorter description length because it compresses knowledge as opposed to a classical lookup table which adds a new member for each new corresponding input. A compressed lookup table could potentially have a shorter description length than GLUT, even one that grows logarithmically, implying that the speed prior wouldn’t necessarily favour meta-learning, but this requires further investigation.
Why isn’t the learned ‘compression’ algorithm itself an inner algorithm that the meta-learner is learning and thus incentivized? “Compression = intelligence.”
A ‘dumb’ off the shelf compressor like xz isn’t going to be able to do much with a few petabytes of chess endgame databases; but there are generic algorithms which can compute endgame databases and those programs are more like kilobytes in size. A space-time weighted algorithm would then probably look something like endgame databases for the most frequent endgames (megabytes), and then on the fly dynamic programming to compute everything missing with speedups from the cached databases.
This is generic to many board games, and so a meta-learner tasked with many board games should learn the template and how to specialize it to specific games.
Why isn’t the learned ‘compression’ algorithm itself an inner algorithm that the meta-learner is learning and thus incentivized? “Compression = intelligence.”
A ‘dumb’ off the shelf compressor like xz isn’t going to be able to do much with a few petabytes of chess endgame databases; but there are generic algorithms which can compute endgame databases and those programs are more like kilobytes in size. A space-time weighted algorithm would then probably look something like endgame databases for the most frequent endgames (megabytes), and then on the fly dynamic programming to compute everything missing with speedups from the cached databases.
This is generic to many board games, and so a meta-learner tasked with many board games should learn the template and how to specialize it to specific games.