At the risk of venturing far afield of inspiration by biological mechanisms, I’d like to discuss a possible pairing of hints and helpers…
Hints: Categories, Keywords, Tags, etc.
As a Monty system explores its environment, sets of modules will learn to recognize categories of objects (e.g., cups, handles, logos) and develop a graph connecting them. However, nothing in the sensed or low-level inferred information can tell us what an object might be called or what a graph link might represent.
If our goal were solely to replicate the (neo)cortex, this might be a reasonable limitation. However, if this limitation could be resolved, it might help to provide more visibility into Monty’s methods and results. More pragmatically, it might make production Monty systems more useful.
So, if we know things about how a Monty system is being trained, it makes sense to record that information (e.g., in CMP messages). The specificity of this information will vary, ranging from tags through keywords to categories. Regardless, it could help with human and/or AI-based analysis.
Helpers: Graph Databases
Monty’s generated “graph” is represented as objects (i.e., nodes) and relationships (i.e., edges), stored in the memories of the Learning Modules (LMs). Although this may let the LMs make inferences and such, it may not provide convenient support for searching and/or traversing the graph.
In the thread About Displacement Cells, I discussed some graph databases. I’d like Monty to use one or two of these to backstop its in-memory graph.
P.S. If anyone is interested in an learning about biological mechanisms, from the perspective of philosophy of science, I’d recommend reading In Search of Mechanisms.