Aaron said, I think there is a more comprehensive data structure used in the human mind to represent what a sentence actually means, and this is the data structure, the lingua franca of the mind, on which the mind operates in the act of thinking. What would that data structure look like, were we to reverse engineer it to work on a computer? Language is useful towards accomplishing this task, not because it is already in the proper form, but because its structure necessarily closely mirrors that form, due to its purpose of communicating knowledge in that form from one mind to another. Once we have a proper understanding of how meaning is represented in the mind, it should be possible to begin mapping sensory information to that format, just as can be done with natural language.
Language can be used to encode new representations and linguistic expressions can be found to detail what is being communicated in ways that are very versatile. I don't need to share all of your experiences to have a pretty good idea what it is that you are trying to say, we only need to be willing to make the effort to communicate clearly. So while our AGI programs would not know about a lot of things that we have experienced, neither would your smart robots. The evidence that iconic information, for example, is necessary for cognition is pretty weak. The evidence that extensive visual information would enhance an AGI's capability, once the major artificial cognitive processes were worked out, seems obvious. Jim Bromer On Wed, Apr 3, 2013 at 1:02 PM, Aaron Hosford <[email protected]> wrote: > PM said: > >> One suggestion is that you compile language into a "database of facts" >> using a propositional representation. >> In addition, you convert all sensory input to the AGI into the same >> propositional representation. >> Then you do inferencing within and generate behaviors from the aforesaid >> representation. > > > If by "propositional representation", a logical statement with a Boolean > true/false value, this will not be sufficient. The reason is, "facts" are > never certain, and you never know in advance which ones will later prove > wrong. Facts have associated confidence levels, based on supporting and > conflicting evidence. Boolean truth values are an idealization of this, > throwing away the ongoing accumulation of evidence and giving us only > whether a particular proposition is currently accepted as reliable or not. > The failure to recognize this has held back many seemingly promising AI > projects in the past. > > Rich said: > >> So, what the heck can we compile NL into that would support prospective >> AGI operation? > > > This is what I've been describing to you. Semantic networks, properly > structured, are up to the task. Any proposition from PM's "propositional > representation" can be represented in a semantic network. The advantage > that a semantic network then conveys is that the relationships between > elements contained within a proposition can themselves be given confidence > levels; the analysis of evidential support is no longer limited only to the > proposition as a whole. For example, suppose I am looking at the > proposition, *ate(Billy, Nicky's_Popsicle)*. In a standard propositional > representation like this, I can't analyze where the proposition is wrong, I > just have to accept it is either right or wrong as a whole. If I use a > semantic network-style representation, * > Billy<--SUBJECT--ate--OBJECT-->Nicky's_Popsicle*, I now have two separate > locations where I can attribute the failure of the proposition as a whole > to be true: the *SUBJECT *and *OBJECT *links. Propositions come so close > to doing this, but fail when we attempt to attribute failure to a > particular substructure, because they aren't generalized enough to permit > full analysis of the relationships of substructures to the parent structure. > > Andi said: > >> I would go with Todor on this one. More specifically, it's very clear to >> me that language cannot be the bottom or basis of representation. A >> language system has to be a piece on top of the basic system. It may be >> the most important piece to us, because for interaction with us, and >> ability to use our body of written knowledge and contribute to it, a system >> will need to use language. But, that need in no way implies that you could >> ever get any intelligent behavior if you just start at the level of >> language. There are plenty of reasons to think otherwise. > > > The problem Steve and I both agree needs to be solved is: What, inside the > mind, represents the *meanings *of natural language, and how do we go > about designing an analogous structure programmatically? When you say > someone understands a sentence, what happens in that person's mind? Is > there not some sort of internal structure to which that sentence gets > mapped through the act of understanding? In most AI/AGI projects to date, > there have been three basic approaches: (1) use it directly in text form, > (2) pull out what you need and stash it in "frames", (3) convert it to a > parse tree. I think each of these is inadequate to the task. I think there > is a more comprehensive data structure used in the human mind to represent > what a sentence actually means, and this is the data structure, the lingua > franca of the mind, on which the mind operates in the act of thinking. What > would that data structure look like, were we to reverse engineer it to work > on a computer? Language is useful towards accomplishing this task, not > because it is already in the proper form, but because its structure > necessarily closely mirrors that form, due to its purpose of communicating > knowledge in that form from one mind to another. Once we have a proper > understanding of how meaning is represented in the mind, it should be > possible to begin mapping sensory information to that format, just as can > be done with natural language. > > > On Wed, Apr 3, 2013 at 10:48 AM, Piaget Modeler <[email protected] > > wrote: > >> Steve Richfield: "So, what the heck can we compile NL into that would >> support prospective AGI operation?" >> >> One suggestion is that you compile language into a "database of facts" >> using a propositional representation. >> In addition, you convert all sensory input to the AGI into the same >> propositional representation. >> Then you do inferencing within and generate behaviors from the aforesaid >> representation. >> >> ~PM >> >> >> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >> <https://www.listbox.com/member/archive/rss/303/23050605-2da819ff> | >> 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/303/10561250-470149cf> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <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-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
