I am not going to engage with your A* proposition. I believe it to be irrelevant.
> However, transformers do not sit on a "knowledge graph", since the space is not composed of discrete propositions set in discrete relationships.
This is the main point of contention. By all means, embeddings are a graph, as you can use a graph to represent its datastructure, but not a tree. Sure, they are essentially points in space, but a graph emerges as the architecture starts selecting tokens for use according to the learned parameters during inference. It will always be the same graph for the same set of tokens for a given data set which provides "ground truth". I know it sounds metaphoric but bare with me.
The above process doesn't result in discrete propositions like we have in prolog, but the point is, it is "relatively" meaningful, and you seed a traversal by bringing tokens to the attention grid. What I mean by relatively meaningful is that inverse relationships are far enough that they won't usually be confused, so there is less chance of meaningless gibberish emerging which is what we observe.
> However, transformers do not sit on a "knowledge graph", since the space is not composed of discrete propositions set in discrete relationships.
This is the main point of contention. By all means, embeddings are a graph, as you can use a graph to represent its datastructure, but not a tree. Sure, they are essentially points in space, but a graph emerges as the architecture starts selecting tokens for use according to the learned parameters during inference. It will always be the same graph for the same set of tokens for a given data set which provides "ground truth". I know it sounds metaphoric but bare with me.
The above process doesn't result in discrete propositions like we have in prolog, but the point is, it is "relatively" meaningful, and you seed a traversal by bringing tokens to the attention grid. What I mean by relatively meaningful is that inverse relationships are far enough that they won't usually be confused, so there is less chance of meaningless gibberish emerging which is what we observe.