Second, we could take condensation as inspiration and try to create new machine-learning models which resemble condensation, in the hopes that their structure will be more interpretable.
Condensation could also be applied to model scaffolding design or the interpretability of scaffolded systems. Some AI memory storage and retrieval systems already have structures that resemble the tagged-notebook analogy, with documents stored in a database along with tags or summaries. A condensation-inspired memory structure could potentially have low retrieval latency while also being highly interpretable. Condensation might also be useful for interpreting why a model retrieves a specific set of documents from its memory system when responding to a query.
Condensation could also be applied to model scaffolding design or the interpretability of scaffolded systems. Some AI memory storage and retrieval systems already have structures that resemble the tagged-notebook analogy, with documents stored in a database along with tags or summaries. A condensation-inspired memory structure could potentially have low retrieval latency while also being highly interpretable. Condensation might also be useful for interpreting why a model retrieves a specific set of documents from its memory system when responding to a query.