<rant>That's where graph neural nets come into place. They can learn relations, scaling to a large number of objects. A traditional approach would have to learn all possible combinations, hitting the combinatorial explosion. Graph neural nets can solve problems such as shortest path, sorting and dynamic programming. I think in the future if we are to get closer to human level we need graphs as the intermediate representation. Graphs could represent the objects in an image/phrase and their relations, then answer about the attributes of an object, the relation between two objects or classify the graph itself. All simulators are evolving graphs as well, and code/automata could be represented and executed as a graph. The transformer could be considered an implicit graph where the adjacency matrix is computed from the nodes at each iteration. The closest to AGI in my view would be model based RL implemented with graphs.</>