[ssdfsdf]

While I think the hypergraph is a great data structure, and I agree graph rewrite rules, written as graphs have a lovely symmetry, this approach feels too low level to me. Sure you can have graphs representing both data and process ala "To Dissect a Mockingbird", but it feels that this interesting interplay of data and process is sitting too high up in the hierarchy of structure, I think you want this happening at a very low level, giving more space for emergence (whatever that may be).

There is not enough symmetry, I think the correct solution to this problem is going to look obvious. I don't think this is an engineering problem, I think it is a radical re-imagining of what intelligence is. My hope is with reservoir computing.

I think what we need to do is a mashup of reward modulated hebbian learning and reservoir style techniques. We need to take a gigantic sledge hammer and smash apart the incoming stream of data, spray it as far across the space as possible and then linearly compose the pieces to construct something that looks right. Combine this with hebbian learning so that those mutated, fragmented, mutated pieces of the incoming object which are useful for the purpose of the device are made more likely to occur within the network.

So you need a structure where it is possible to enhance the probability of some perturbation of the data through some global learning rule. Then you need a way of bringing those pieces together to reconstruct either the object itself or an object of use. And you need lots of it, billions of active processing elements and trillions of sparse connections.

Just my rant, perhaps it will spark something in a mind elsewhere, just passing on the pieces of the puzzle that I have smashed apart in my head. Perhaps we need to flip this all around, the patterns come from the world, they take root in our head and use the substrate to evolve, before passing out into the world again. what an GAI needs to do is provide a place for these patterns to take root and evolve according to the GAI specific objective function...