<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.</>
But what bothers me is that the human animal is trying to create an artificial human mind, the most amazing piece of meat we have laying around.
If we could create the reasoning of a dog first, to then upscale to more complex logic, I'd be more confident research is going places (as in, we have biological evidence building blocks to understand first).
<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.</>