IMO something like this is the future of AI. I've been trying to crack it for a while as a hobby. I also use vision tasks as test but unfortunately my algorithm

IMO something like this is the future of AI. I've been trying to crack it for a while as a hobby. I also use vision tasks as test but unfortunately my algorithms get stuck in local maximums or coarse representation of images. I'm not sure this paper's algorithm is that powerful either. It seem to start with a squiggly line grammar that is already close the the final written text-like characters it is trying to learn. This narrows the model space very much and makes the problem easier. It would be more impressive if it started with an SVG like grammar and learned to draw any kind of image. This is the approach I try to take anyways even if I can't seem to get very far. The problem is that the search space is so large and so irregular with regards to output that you can't brute force it. You need to somehow cluster similar generative rules of different complexity together then get your algorithm to search the model space in a way that it tries simpler rules first, then goes on to try generating more complex rules but prioritize the ones that are known to have similar output to the simpler ones. Basically you have to cluster your generative rules into an taxonomy that your algorithm can navigate from the top. It's exponentially inefficient to try to generate "dog" until you have recognized that the simpler "animal" is a good approximation. I also think that in the ultimate solution, the output leaf statements of the grammar will be parametrized like a normal programming language, so that for example, the algorithm can generate a color, a radius and then generate 100 circles referencing this single color and radius to represent repeated patterns without having to learn their size and color individually when they are clearly homogeneous or invariant across a bunch. The bayesian, occam's razor, solution to a bunch of similar things that you don't have a category for yet is shared parameters. These parameters is how you learn with very few examples. You don't have to learn a full new category to make good predictions, the algorithm can simply notice the homogeneity in a part of a scene and extrapolate immediately.