Yeah, I can’t see why Fuster is a big deal. He summarises what we *know* - and sure we know that the brain progressively abstracts – but we don’t know or have any consensus on *how*.
Abstracting from patterns is relatively simple. But the real world scenes and objects that confront the human brain aren’t patterned or easy to abstract – wh. is why B & B & other AGI-ers ignore them & stick to their artificial worlds.. If you want to put that mathematically, take a whole set of diverse patterns – Koch curve, Mandelbrot, herringbone, cellular automaton etc . etc. – and explain how the brain is able to abstract from *all of them together* and recognize them collectively as “patterns” (and not just as Koch curves/herringbones etc. etc). Where’s the pattern in a set of diverse patterns, B & B? And where’s the complexity, Jim? http://www.alexander-hamilton.net/assets/images/geometric_samples.jpg Loud silence. From: Jim Bromer Sent: Thursday, August 23, 2012 12:06 AM To: AGI Subject: Re: [agi] Boris Explains His Theory I found a short lecture by Fuster, Joaquin Fuster: Distributed Memory and the Perception-Action Cycle (2007) http://archive.org/details/Brain_Network_Dynamics_2007-13-Joaquin_Fuster On Wed, Aug 22, 2012 at 5:19 PM, Boris Kazachenko <[email protected]> wrote: > However, I probably won't be able to read it for a few weeks It will take you much longer to actually read through it :). See esp. chapter 3: Functional Architecture of the Cognit (buzzword alarm). From: Jim Bromer If you want a mainstream source, read "Cortex & Mind" by Joaquin Fuster, he is a paramount authority on the subject. If it was convenient I would get it tonight. However, I probably won't be able to read it for a few weeks. Jim On Wed, Aug 22, 2012 at 10:52 AM, Boris Kazachenko <[email protected]> wrote: 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 | Modify Your Subscription AGI | Archives | Modify Your Subscription AGI | Archives | Modify Your Subscription AGI | Archives | Modify Your Subscription ------------------------------------------- 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
