I agree that understanding is the process of integrating different models, different meanings, different pieces of information as seen by your model. But this integrating just matching and not extending the own model with new entities. You only match linguistic entities of received linguistically represented information with existing entities of your model (i.e. with some of your existing patterns). If you could manage the matching process successfully then you have understood the linguistic message.
Natural communication and language understanding is completely comparable with common processes in computer science. There is an internal data representation. A subset of this data is translated into a linguistic string and transferred to another agent which retranslates the message before it possibly but not necessarily changes its database. The only reason why natural language understanding is so difficult is because it needs a lot of knowledge to resolve ambiguities which humans usually gain via own experience. But alone from being able to resolve the ambiguities and being able to do the matching process successfully you will know nothing about the creation of patterns and the way how to work intelligently with these patterns. Therefore communication is separated from these main problems of AGI in the same way as communication is completely separated from the structure and algorithms of the database of computers. Only the process of *learning* such a communication would be AI (I am not sure if it is AGI). But you cannot learn to communicate if there is nothing to communicate. So every approach towards AGI via *learning* language understanding will need at least a further domain for the content of communication. Probably you need even more domains because the linguistic ambiguities can resolved only with broad knowledge . And this is my point why I say that language understanding would yield costs which are not necessary. We can build AGI just by concentrating all efforts to a *single* domain with very useful properties (i.e. domain of mathematics). This would reduce the immense costs of simulating real worlds and additionally concentrating on *at least two* domains at the same time. -Matthias Vladimir Nesov [mailto:[EMAIL PROTECTED] wrote Gesendet: Sonntag, 19. Oktober 2008 12:59 An: [email protected] Betreff: [agi] Re: Meaning, communication and understanding On Sun, Oct 19, 2008 at 11:58 AM, Dr. Matthias Heger <[EMAIL PROTECTED]> wrote: > The process of outwardly expressing meaning may be fundamental to any social > intelligence but the process itself needs not much intelligence. > > Every email program can receive meaning, store meaning and it can express it > outwardly in order to send it to another computer. It even can do it without > loss of any information. Regarding this point, it even outperforms humans > already who have no conscious access to the full meaning (information) in > their brains. > > The only thing which needs much intelligence from the nowadays point of view > is the learning of the process of outwardly expressing meaning, i.e. the > learning of language. The understanding of language itself is simple. > Meaning is tricky business. As far as I can tell, meaning Y of a system X is an external model that relates system X to its meaning Y (where meaning may be a physical object, or a class of objects, where each individual object figures into the model). Formal semantics works this way (see http://en.wikipedia.org/wiki/Denotational_semantics ). When you are thinking about an object, the train of though depends on your experience about that object, and will influence your behavior in situations depending on information about that objects. Meaning propagates through the system according to rules of the model, propagates inferentially in the model and not in the system, and so can reach places and states of the system not at all obviously concerned with what this semantic model relates them to. And conversely, meaning doesn't magically appear where model doesn't say it does: if system is broken, meaning is lost, at least until you come up with another model and relate it to the previous one. When you say that e-mail contains meaning and network transfers meaning, it is an assertion about the model of content of e-mail, that relates meaning in the mind of the writer to bits in the memory of machines. From this point of view, we can legitemately say that meaning is transferred, and is expressed. But the same meaning doesn't exist in e-mails if you cut them from the mind that expressed the meaning in the form of e-mails, or experience that transferred meaning in the mind. Understanding is the process of integrating different models, different meanings, different pieces of information as seen by your model. It is the ability to translate pieces of information that have nontrivial structure, in your basis. Normal use of "understanding" applies only to humans, everything else generalizes this concept in sometimes very strange ways. When we say that person understood something, in this language it's equivalent to person having successfully integrated that piece in his mind, our model of that person starting to attribute properties of that piece of information to his thought and behavior. So, you are cutting this knot at a trivial point. The difficulty is in the translation, but you point on one side of the translation process and say that this side is simple, then point to another than say that this side is hard. The problem is that it's hard to put a finger on the point just after translation, but it's easy to see how our technology, as physical medium, transfers information ready for translation. This outward appearance has little bearing on semantic models. ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=117534816-b15a34 Powered by Listbox: http://www.listbox.com
