Also, a more recent summary of why I think their view of the *brain* is oversimplified is here:
http://hplusmagazine.com/2012/07/20/how-the-brain-works/ -- Ben G On Mon, Nov 19, 2012 at 4:48 PM, Ben Goertzel <[email protected]> wrote: > > I believe my response to Hawkins' book, written in 2004, still holds as a > response to Kurzweil's book (with its similar theme) as well: > > > http://www.goertzel.org/dynapsyc/2004/OnBiologicalAndDigitalIntelligence.htm > > -- Ben Goertzel > > > On Mon, Nov 19, 2012 at 4:42 PM, Micah Blumberg <[email protected]> wrote: > >> YKY your approach does not personally make sense to me. Naturally I hope >> you are successful. Have you read either "On Intelligence" by Jeff Hawkins >> or "How to create a mind" by Ray Kurzweil. The latter book was recently >> published. I suspect that many people trying to build AGI will be able to >> consider a new approach to after reading it. Please let me know if you are >> aware of that kind of AI and what advantages your planned system may have >> over it. >> >> >> On Sun, Nov 18, 2012 at 9:20 PM, Aaron Hosford <[email protected]>wrote: >> >>> My matrix representation is based on the observation that AB != BA in >>>> the composition of concepts, for example "john loves mary != mary loves >>>> john". I'm still trying to determine if the matrix representation is sound >>>> and consistent with our common-sense view of concepts, but it looks >>>> promising... >>> >>> >>> I may be jumping to conclusions, but it sounds like what you're doing is >>> placing A/"john", B/"mary", etc. as labels for both rows and columns, where >>> the row label represent the first value and the column represents the >>> second (or maybe vice versa), so that you can represent "john loves mary" >>> by placing a nonzero value in entry indexed by row "john" and column >>> "mary". The most obvious interpretation for this nonzero value would be the >>> probability that "john loves mary" is true, or it could possibly be >>> certainty or some other similar measure. >>> >>> As you pointed out, this is a nice generalization of a basic adjacency >>> matrix, with nonzero entries marking adjacencies (directed links or edges) >>> in the conceptual graph. Matrices of this form could be mapped to directed >>> graphs with links not only labeled "loves", but having a secondary label >>> indicating the associated probability or certainty value of the link. In my >>> own system, I keep my internal representation directly in terms of nodes & >>> links rather than such a generalized adjacency matrix, but the specific >>> data structure is really only an implementation detail, not intrinsic to >>> the structure of the information being stored. We are basically doing the >>> same thing, in other words, provided my understanding of your >>> representational method is correct. >>> >>> I'm still trying to determine if the matrix representation is sound and >>>> consistent with our common-sense view of concepts, but it looks >>>> promising... >>> >>> >>> I have spent a lot of time thinking about this problem, albeit in terms >>> of semantic nets rather than the matrices that correspond to them. I think >>> your approach is consistent with how we internally work with concepts, but >>> it is probably incomplete. For example, what happens when you want to >>> represent the probability or certainty that "john" is the one participating >>> as the subject in this "loves" relationship independently from the >>> probability/certainty that "mary" is the direct object? Or what if you have >>> lower probability/certainty for the classification of the relationship as >>> "loves" (as opposed to say, "hates") than you do for the individuals >>> filling the subject and direct object roles? It seems now that you need 3 >>> probability/certainty numbers for each position in the matrix, one for >>> subject, one for direct object, and one for relationship type. >>> >>> Instead of multiple values in a single cell of the matrix -- or rather 3 >>> parallel matrices with the same row and column labels -- it seems more >>> natural to me to make the clause/predicate/act/relationship a separate >>> value in itself, so the original matrix is decomposed into (1) a "subject" >>> matrix, mapping the probability/certainty of "john" (among other people) >>> participating in the clause as the subject, (2) a "direct object" matrix, >>> mapping the probability/certainty of "mary" (among other people) >>> participating in the clause as the direct object, and (3) a "predicate" >>> matrix, mapping the probability/certainty that the clause's predicate is >>> "loves" (among other possible predicates). Each of these different >>> matrices, "subject", "direct object", and "predicate", maps to a link label >>> in the corresponding directed graph, and each link receives its own >>> probability/certainty value independent of the others. The representational >>> advantage of making the clause itself a row/column label only grows when >>> prepositional phrases, adverbs, indirect objects, and other clausal >>> modifiers start to come into play, and the utility of your matrix trick is >>> preserved by simply adding a new matrix for each constituent role in the >>> clause. >>> >>> >>> >>> On Sun, Nov 18, 2012 at 8:10 PM, YKY (Yan King Yin, 甄景贤) < >>> [email protected]> wrote: >>> >>>> On Sat, Nov 10, 2012 at 3:44 AM, Aaron Hosford <[email protected]>wrote: >>>> >>>>> My "matrix trick" may be able to map propositions to a >>>>>> high-dimensional vector space, but my assumption that concepts are >>>>>> matrices >>>>>> (that are also rotations in space) may be unjustified. I need to find a >>>>>> set of criteria for matrices to represent concepts faithfully. This >>>>>> direction is still hopeful. >>>>> >>>>> >>>>> I'm curious what your "matrix trick" is. Are you familiar with >>>>> adjacency matrices? They're the simplest and most common way of >>>>> representing directed graphs as matrices. >>>>> http://en.wikipedia.org/wiki/Adjacency_matrix Semantic nets typically >>>>> have sparse adjacency matrices, and there are a lot of good algorithms and >>>>> libraries out there for efficiently representing and manipulating sparse >>>>> matrices. >>>>> >>>> >>>> >>>> Sorry, almost missed your post. My method is to *represent* logical >>>> atoms as square matrices with real entries. This allows me to convert the >>>> matrices to vectors (by simply flattening the matrices) and calculate the >>>> distances (ie similarities) between them via the Euclidean norm. >>>> >>>> My matrices are different from adjacency matrices which has integer >>>> entries. The continuous values potentially allow learning via propagation >>>> of errors similar to back-propagation for neural nets. >>>> >>>> My matrix representation is based on the observation that AB != BA in >>>> the composition of concepts, for example "john loves mary != mary loves >>>> john". I'm still trying to determine if the matrix representation is sound >>>> and consistent with our common-sense view of concepts, but it looks >>>> promising... >>>> >>>> YKY >>>> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >>>> <https://www.listbox.com/member/archive/rss/303/23050605-bcb45fb4> | >>>> Modify <https://www.listbox.com/member/?&;> Your Subscription >>>> <http://www.listbox.com> >>>> >>> >>> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >>> <https://www.listbox.com/member/archive/rss/303/23601407-ccf7ca1d> | >>> Modify <https://www.listbox.com/member/?&;> Your Subscription >>> <http://www.listbox.com> >>> >> >> >> >> -- >> >> ~~ >> Warmly, >> >> >> Micah >> 7 1 4 ) 6 9 9 - 4 2 1 3 (voicemail and texting same digits) >> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >> <https://www.listbox.com/member/archive/rss/303/212726-c2d57280> | >> Modify<https://www.listbox.com/member/?&;>Your Subscription >> <http://www.listbox.com> >> > > > > -- > Ben Goertzel, PhD > http://goertzel.org > > "My humanity is a constant self-overcoming" -- Friedrich Nietzsche > > -- Ben Goertzel, PhD http://goertzel.org "My humanity is a constant self-overcoming" -- Friedrich Nietzsche ------------------------------------------- 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
