Yes, I am trying to understand a generalized (which also means simplified) and formalizable parallel to human cognition. Some of my thinking on this is inspired by predictive coding and adaptive resonance theory (although prettly loosely by the latter), and I am trying to figure out the implications of our most updated understanding of neurobiological principles, together with a notion of the “riverbeds of cognition”.
In other words, how can we design an architecture such that it is not pressured to take shortcuts or “work around” design decisions we made, as its cognition develops? Is there a “natural path” of cognitive development that avoids some of the common pitfalls and failure modes (i.e. can we aim inner alignment if we have proficiency in this area)? This has a direct bearing on interpretability, and goes together with the goal of a sort of “conceptual curriculum” that is intended to teach the system natural abstractions.
If I remember correctly, the centrality of “constraint satisfaction” fell out of considering causal (hyper/meta)graphs as sensible representational substrate (which was partially inspired by Ben Goertzel). I personally find it quite intuitive to think in graphs.
Thanks a lot for the encouragement :)
Yes, I am trying to understand a generalized (which also means simplified) and formalizable parallel to human cognition. Some of my thinking on this is inspired by predictive coding and adaptive resonance theory (although prettly loosely by the latter), and I am trying to figure out the implications of our most updated understanding of neurobiological principles, together with a notion of the “riverbeds of cognition”.
In other words, how can we design an architecture such that it is not pressured to take shortcuts or “work around” design decisions we made, as its cognition develops? Is there a “natural path” of cognitive development that avoids some of the common pitfalls and failure modes (i.e. can we aim inner alignment if we have proficiency in this area)?
This has a direct bearing on interpretability, and goes together with the goal of a sort of “conceptual curriculum” that is intended to teach the system natural abstractions.
If I remember correctly, the centrality of “constraint satisfaction” fell out of considering causal (hyper/meta)graphs as sensible representational substrate (which was partially inspired by Ben Goertzel). I personally find it quite intuitive to think in graphs.