Jim, >> "You keep confusing source with destination, because you insist on >> operating within your declarative memory, which is a rather >> superficial subset of your cognitive model :)." > > Are you replying using your theory as a model of the mind (indeed, as > a model of my mind!)
It's not *my* theory, a mainstream position in neuroscience is that neocortex is a hierarchy of generalization, from primary sensory & motor areas to incrementally higher association areas. It's also well known that declarative memory is restricted to the latter. Besides, these things are tautologically self-evident to me. > with a smiley face to represent some humor about doing that? That mostly represents my self-satisfaction with putting things well :). > And, are you saying that declarative memory is a destination in your > model rather than a source? Is declarative memory derived? That is > what you are saying right? Yes, see the above. If you want a mainstream source, read "Cortex & Mind" by Joaquin Fuster, he is a paramount authority on the subject. > Is your theory a theory of how the brain works, a theory for > artificial general intelligence using computers or both? Both, but the artificial version is a whole lot cleaner, the brain is loaded with evolutionary artifacts. For example, I don't have this artificial distinction between implicit & declarative memory, between sensory & motor hierarchies, & a bunch of other things. > Do you regularly see the kinds of thinking that people do in the terms > of your model? Yes, except that "my" part of it is well below the surface (low-level processing), the mainstream part is usually sufficient to qualitatively explain declarative thinking. http://www.cognitivealgorithm.info/2012/01/cognitive-algorithm.html -------------------------------------------------- From: "Jim Bromer" <[email protected]> Sent: Wednesday, August 22, 2012 9:42 AM To: "AGI" <[email protected]> Subject: [agi] Boris Explains His Theory > Boris, > I am just not getting this. So let me try starting with some simple > questions. > I had said, "Forcing semantic values into 3-dimensional orthogonal > space seems amazingly confused to me." > You replied, > "You keep confusing source with destination, because you insist on > operating within your declarative memory, which is a rather > superficial subset of your cognitive model :)." > > Are you replying using your theory as a model of the mind (indeed, as > a model of my mind!) with a smiley face to represent some humor about > doing that? Did you think that my statement about forcing semantic > values was made in reference to something in your theory? Because > that is not what I meant. I was just saying that I have read papers > about using semantic vectors and my thoughts on that is that trying to > force semantic vectors into 3-dimensional space seems confused. > > And, are you saying that declarative memory is a destination in your > model rather than a source? Is declarative memory derived? That is > what you are saying right? > > Is your theory a theory of how the brain works, a theory for > artificial general intelligence using computers or both? > > Do you regularly see the kinds of thinking that people do in the terms > of your model? > Jim Bromer > > > > --------------- Previous Messages --------------- > Jim, >> I don't understand your comments about detecting patterns. You said: > > This is interactive pattern projection, but you have to discover those > patterns first. Technically, you simply multiply all the vectors in a > pattern by a relative distance to a target coordinate. And then you > compare multiple patterns projected to the same coordinate, & multiply > the difference by relative strength of each pattern. That gives you a > combined prediction, or probability distribution if the patterns are > mutually exclusive. > > That comment was about projecting patterns, not detecting them. > >> What kind of patterns are you talking about? How do the elemental >> observations (from the sensory device) get turned into vectors? > > Comparisons generate derivatives. A vector is d(input) over > d(coordinate). Conventionally, it's over multiple coordinates > (dimensions), & the input can be a lower coordinate, but that's not > essential. > >> Are you saying that the "higher level of search and generalization" are >> where/how the pattern vectors are created? > > No, all levels. > >> Why or how would you pick out a particular target coordiate to use to >> combine a prediction? > > Well, coordinate resolution is variable, so I am talking about a > min->max span. Basically, vector projection is part of input selection > for a higher-level search. The target coordinate span is a feedback > from that higher level, or, if there aren't any, current_search_span * > selection_rate: preset lossiness / sparseness of representation on the > higher level. > >> Are you saying that all predictions have individual coordinates? > > Individual coordinate span. It's what + where, you got to have both. > >> That alone means that they would have to exist in dynamic virtual space of >> many dimensions. Forcing semantic values into 3-dimensional orthogonal space >> seems amazingly confused to me. > > You keep confusing source with destination, because you insist on > operating within your declarative memory, which is a rather > superficial subset of your cognitive model :). > > We *derive* all our "semantic" values from 4D-continuous observation, > no need to "force" them into it. > >> What kind of space would your vectors exist in, how do they get there and >> why do you choose a particular coordinate for a combination of predictions? > > As I said, hierarchical search generates incremental syntax, & > variables within it are individually evaluated for search on > successive levels. The strongest variable, whether it's an original > coordinate | modality or a derivative thereof, becomes a coordinate > for a higher level. The strength here must be averaged over higher > level span. > > It's hard to explain this on "semantic" level, which is profoundly > confused in humans anyway. But a good intermediate example is Periodic > Table. You take atomic mass (which is a derived, not an original > variable) as top coordinate, compare pH value along that coordinate, & > notice recurrent periodicity in it's variation. Since pH is a main > chemical property, you then use it as a primary dimension that defines > a period, & atomic mass becomes a secondary dimension that defines a > sequence of periods. Both dimensions are derived, they may seem kind > of a halfway between original & "semantic", but the same derivation > process will get you to the latter > > http://www.cognitivealgorithm.info/2012/01/cognitive-algorithm.html > > Boris, > > I don't understand your comments about detecting patterns. You said: > > This is interactive pattern projection, but you have to discover those > patterns first. Technically, you simply multiply all the vectors in a > pattern by a relative distance to a target coordinate. And then you > compare multiple patterns projected to the same coordinate, & multiply > the difference by relative strength of each pattern. That gives you a > combined prediction, or probability distribution if the patterns are > mutually exclusive :). > > What kind of patterns are you talking about? How do the elemental > observations (from the sensory device) get turned into vectors? Are > you saying that the "higher level of search and generalization" are > where/how the pattern vectors are created? Why or how would you pick > out a particular target coordiate to use to combine a prediction? Are > you saying that all predictions have individual coordinates? > > I have read papers on Semantic Vectors, (I do not need to be told that > the sources of semantic vectors are different than the sources of the > products of your system) and I have always felt that they were > absurdly inappropriate for semantics (or concepts) because they forced > the semantic concepts into a system that they did not fit into. As is > so obvious to Two-Door, concepts are relativistic. That alone means > that they would have to exist in dynamic virtual space of many > dimensions. Forcing semantic values into 3-dimensional orthogonal > space seems amazingly confused to me. > > What kind of space would your vectors exist in, how do they get there > and why do you choose a particular coordinate for a combination of > predictions? > > (Incidentally, just to remind you, my ideas of concepts are not > necessarily expressed as vectors although I am not close minded about > the idea.) > > Jim Bromer > > > On Tue, Aug 21, 2012 at 2:22 PM, Boris Kazachenko <[email protected]> wrote: > >> On the other hand I am interested in conjectures about conceptual vectors >> and stuff like that > > You can't formalize "conceptual" vectors, except in terms of > "conceptual" coordinates . > > Jim Bromer > > Thanks for the smiley faces Boris... > I disagree that you have to multiply all the vectors in a pattern by > a relative distance to a target coordinate in order to combine > imagined complex ideas and related observations. Our theories are > very different. (On the other hand I am interested in conjectures > about conceptual vectors and stuff like that.) > > I am interested in a continuation of the explanation of your theories > and I hope to get back to it soon. > Jim Bromer > > > On Tue, Aug 21, 2012 at 7:57 AM, Boris Kazachenko <[email protected]> wrote: > > Jim, > >>Where Boris and I disagree is that I feel that because of relativity the >>input source of an idea may not be the most elemental source of the idea >>that needs to be considered. > > Right, but that's the simplest assumption, you must make it unless > you know otherwise. And you only know otherwise if you've > discovered more "elemental" (stable) source on some higher level of > search & generalization. That would generate a focusing / motor > feedback, always derived from prior feedforward. As I keep saying, > complexity must be incremental :). > >> One simple example is that we can use our imagination and study of the >> subject of the concept in order to extend our ideas about the subject >> beyond those ideas which came directly from observations of it. > > This is interactive pattern projection, but you have to discover > those patterns first. Technically, you simply multiply all the > vectors in a pattern by a relative distance to a target coordinate. > And then you compare multiple patterns projected to the same > coordinate, & multiply the difference by relative strength of each > pattern. That gives you a combined prediction, or probability > distribution if the patterns are mutually exclusive :). > > > ------------------------------------------- > AGI > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/18407320-d9907b69 > Modify Your Subscription: https://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
