In this discussion, he talks about searching for the most “structureless structure” as the necessary substrate to fundamental reality. I don’t think he says it explicitly but there’s really no difference between having that structureless structure as the actual substrate of reality and merely using it as a model. There’s no difference since all you can experience is models anyway.
Anyway it struck a cord with me because for a few years I’ve been obsessed with the idea that you can model everything as a graph, a network of nodes and edges. I feel this way because you can then say, Ok, this set of nodes is a single node in a more macro-scale network.
I think that is essentially what he’s claiming - you can model all of reality as a hypergraph, which is really just the same thing as grouping nodes in a graph.
What do you think?
Don’t you think this has implications for what intelligence or AGI really is? He’s saying, you can model the whole universe, on the smallest scale as a hypergraph, that’s really saying you can understand it as a hypergraph.
It seems remarkably identical in its most basic principles to HTM, as HTM essentially asserts the brain is made of a bunch of nodes in a graph, which is arranged in minicolumns which are themselves nodes in a more macro-scale network, which aggregate into regions, yet more macro-scale nodes.
Furthermore, HTM argues that not only is the physical hierarchy structured this way, so is the conceptual hierarchy of shared patterns among areas of the brain, highly interconnected areas broadcast patterns and names that are highly detailed, but the further away those connections reach the more broad and time-invarient the patterns become. For more on that see this conversation I had on reddit.
It just seems like what he’s after - computational theory of everything - is the same as Intelligence itself, and it is the same as optimally efficient distributed computing (memory management computational resources management and bandwidth management across a network).
It seems like its all one thing to me. Like what we’re after here, making intelligence machines, basically is or requires the ultimate unifying theory of every discipline, the alchemist’s masterwork, and that all you need is to understand how a language ought to be employed: the language of nature. The language of networks. Because ultimately we don’t want the universe, we want a way to describe the universe that implies the boundary of every possible model that explains or predicts the data.
What do you think?