Very interesting! I just started reading the home page link. I was struck by this statement:
" HD/VSA addresses these challenges by providing a binding operator associating individual (John, Mary) with roles <https://en.wikipedia.org/wiki/Thematic_relation> (AGENT, PATIENT) and a superposition <https://en.wikipedia.org/wiki/Superposition_principle> operator that allows multiple associations to be composed into a coherent whole." The Topic Maps model for organizing knowledge has *topics *- a topic is anything that can be talked about - and *relationships*. A relationship type has a number of *roles*, and those roles are filled by topics. It sounds very similar, at a basic level. A Topic Maps relationship would be the equivalent of the HD/VSA binding operator. I have some reservations about using cosine similarity with vectors like this. I have experimented with them a little, not in the area of AI but for answering queries in a certain space of questions and knowledge. The trouble is that the components of a vector are not often orthogonal, so the simple ways to compute their projections are not valid. You can crank out the results, but they will not be correct, to a degree that depends on the other vector involved. I will be interested to learn how these investigators handle this. As an example of what I mean, consider a vector of words, and you want to know how similar it is to another vector of words. A simpleminded approach make each word into a vector component. So here are two sentences: "Which comes first, the chicken or the egg" "Evolutionarily speaking a bird can be considered to be the reason for an egg" Now make vectors of these two sentences, where every word is on its own axis. You take the cosine by multiplying the value of each component in one vector by the value of the same component in the other vector. Each component here has a value of 0 or 1 (since the word is either present or not). The only components that match are "the" and "egg". So the score - the cosine - will be very low. However, we can see that the two sentences are actually very similar in meaning. And how can we determine how orthogonal a bird is to a chicken? So this approach is too simple. It will be interesting to see what these folks are really doing. Personally, I expect that an approach using fuzzy logic would be promising. It would be similar to using cosines to project one vector onto another, but with fuzzy operators instead of multiplication. Why fuzzy logic? Because it matches how people (and no doubt animals) actually assess things in the real world. How you you judge how tall a person is? You don't divide up the arithmetic range into spans - 5 ft to 5'2", 5'2" - 5'4", etc. (sorry, non-US units people) and see which bin the person falls into. No, you have a notion of what "tall", "medium", "short" and "very short" mean, and you see how well the person *matches* each of them. So the person might be "somewhat tall but not far from medium". On Saturday, April 15, 2023 at 6:35:02 AM UTC-4 Edward K. Ream wrote: > On Saturday, April 15, 2023 at 5:31:41 AM UTC-5 Edward K. Ream wrote: > > The article describes NVSA: Neuro-vector-symbolic Architecture. Googling > this term found the home page for (HD/VSA) Vector Symbolic Architecture > <https://www.hd-computing.com/#h.zgreogawc8qc>. This must be one of the > best home pages ever written! > > > I recommend following *all* the links on this page. Most links point to > Wikipedia pages for *easy* math concepts. I bookmarked many of these > links. > > Edward > -- You received this message because you are subscribed to the Google Groups "leo-editor" group. To unsubscribe from this group and stop receiving emails from it, send an email to leo-editor+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/leo-editor/c3001d84-fca2-482f-8022-e8c6db3e54dfn%40googlegroups.com.
