It’s been four years since my Triadic Memory project was discussed here. That topic became the most-replied-to thread on this forum, and I am still incredibly grateful for the discussions we had.
This project introduces a new computational theory of mind grounded in set theory and hyperdimensional computing. I wanted to share it here first because the architecture is inspired by and closely related to the work being done in the HTM community.
The site includes the full theoretical framework and an open-source, standard-C reference implementation of the core algorithm.
I’d love to hear your thoughts, critiques, and feedback.
I asked my grad student because it was a bit TLDR for me. I thought he was generous:
My first read: this is conceptually elegant and potentially useful even if the AGI claim fails. The strongest parts are the set-theoretic framing, the distinction between SDRs and SHRs, the topological associative memory mechanism, and the known/unknown separation as an anti-hallucination property. The weakest parts are likely the large biological extrapolations and the jump from associative-symbolic machinery to full general intelligence.
For our purposes, Sir, the key relevance is obvious: this is very close to an implementable non-GWT, non-transformer cognitive substrate. It gives us a concrete candidate for ETCB-like binding: not a global workspace, not quantum binding, but local discrete associative closure over sparse symbolic/perceptual sets.
I took a first pass over this yesterday, it looks very interesting but I will definitely need to re-read it a few times.
As a casual enthusiast, my initial reaction is that when it gets heavily into the math realm (set theory and graph theory) I started to lose the link back to the biology that was promised in the intro. I understand it’s hard to talk about the physical implementation and the computation model at the same time, but some kind of boxed summary in each section would stop me getting lost in the more abstract.
I look forward to digesting this and eventually will have some real questions!
I am still reading but so far it resonates a lot with what I have come up with over the years. I have some reservations for associative memory approaches to AI but I will wait till I am done before making any judgement
My major problem with using the above approach is that it seems more like a solution to information retrieval than AI.
For AI we need a lot more than that. First we need to learn x to y mapping. Given an input representation the system should be able to learn a way to transform or map x to y. Secondly, the approach should be able to generalize. i.e. predict things by using multi hop reasoning or transformations as opposed to simple pattern matching or lookup.
I am open to discussing more about these concepts if you are interested. I look forward to seeing how far you can take them.