I think that humans intertwine their thoughts with their language. Good automatic language translation has been stumped by the semantic problems etc. Context is also a huge problem for people working on computer based language problems.
I think AGI will be found by using models (I use this term is the most general way) that communicate in English at a high level where the language and knowledge is interpreted within the model. Any model could call any other model as needed and many models could be called even at the top level. Detail information would be known only by the specialist models but abstraction and patterns would be generated at every level to encourage analogies and finding appropriate models to use. The top most level would also be a model for determining the results from lower level models. In Schanks CD (conceptual dependency) language model, a relatively small set of primitive actions are necessary to represent the semantics of sentences. Although I don't necessarily advocate only using this set of techniques, I do believe that most language can be translated quite quickly into some useable semantic form. It could take a lot more effort to get fool proof language out of the AGI if you were intent on tricking the AGI. Many humans can be easily ticked by other humans as well. A naive AGI is better than no AGI at all, I believe. The interesting thing about a model versus entering a huge number of rules can be shown by the following analogy. If I have a function that takes a parameter and produces a result based on a linear algorithm, I only need to store the Y intercept and the slope to produce an infinite set of answers. If I am given some number of numerical pairs, I could create a best fit linear line but I wouldn't know that it was the best because of the small data set or that it was linear instead of geometric etc. Knowledge is like the line. Small differences in the input still produce a pretty good answer and this information can be stored very efficiently. Entering a bunch of rules into an inference engine is like the numerical pairs. The system still has to guess at the function to generate any useful information unless you can just look up the answer directly from previous experience. If you want consistency, then try to enter statements that don't contradict each other. It would probably be easy for a while but eventually it would be almost impossible to find the knowledge holes that need plugged (CYC). You would also probably find out just how inconsistent humans really are. The idea is to teach the AGI knowledge and not just meaningless symbols. This can be done using models which use data representations and algorithms that are appropriate to the domain the model was created for. Context can be had by making models that encapsulate language and other models for different contexts. This means no single dictionary with the meaning of every word. How quickly would humans learn if the teacher could reach right into their heads and place an appropriate analogy, algorithm and exceptions right into their brain structures? Instead, we use English to encourage a model to be created in the persons head while using repetition to make a deep enough groove for the memory to stick. Over time this model normally has to be thrown away and replaced to make way for more sophisticated information. In Math alone, how many times was your internal model thrown out and started over from kindergarten to grade 12? My estimate is at least 5 times. I think a combination of programming models directly, programming models that program other models and AGI created models from English language teaching will end up being the quickest way to AGI. Even given the genetic hardware that people have, it doesn't create an intelligent creature without extensive teaching from other humans. If spontaneous intelligence doesn't work for humans, why would we think we can create an AGI this way? I think that fuzzy logic and best guess given "experience and to work with insufficient knowledge" (NARS) is a useful technique but hardly the technique to use for everything. Many things are known. The name of the town I live in is not up for debate. There is only one answer and you know it or you don't. Many patterns also exist or not. Are close or not. Probabilities have a place but are not the whole answer. > However, I don't understand how smaller modules within the brain or mind > could communicate like this, in English. The module that deals > with the word ``word" for example, in order to deal with a > sentence including lots of other words, would have to invoke the > other modules themselves. This is discussed at more length in > my book What is Thought?, if memory serves in Ch. 13. If you can > propose a solution to this, I would be most interested. Sorry, I haven't read your book! "Word" doesn't have to be contained in only 1 model. Many jokes are made because the meaning of words are so context sensitive in our brains that we are surprised when other (legitimate in other contexts) meanings are later used instead. Context would be contained in a model that would contain the language and relations appropriate to that domain. The model could use stored experience for some results and use other techniques if there was significant changes or more detail necessary. I don't propose that a sentence would be syntactically parsed and then the models for each word called. I think the whole sentence would go to a context model and the semantic meaning of the sentence extracted using local and global tools (more models) as necessary. Previous sentences and other sources could be included in determining the semantic meaning of the sentence and adding that information to the model to be used further. The "English" communication at some levels could be like <Command word> <optional parameters> and not full sentences. Some models could be called that have access to the context model or other higher levels so that their output could change depending on how they were created. > (3) Cassimatis has another interesting proposal. He proposes that > all modules (at some high level of granularity) must support a stipulated > interlingua. This is exactly what I propose. I think this interlingua can be a subset of normal English but more likely a group of English subsets depending on the level of interaction. The highest levels could probably communicate in normal English while at the lowest of some levels it could be a matrix of numbers or <Command> <parameter> like I described above. -- David Clark ----- Original Message ----- From: "Eric Baum" <[EMAIL PROTECTED]> To: <[email protected]> Sent: Thursday, March 15, 2007 5:42 AM Subject: Re: [agi] Logical representation > (2) In any language, the words are going to have to invoke some stored > and possibly fairly complex code. In C, for example, instructions will > have to invoke some programs in machine language etc. In English, I > think the words must be labels for quite complex modules. The word > "word", for example, must invoke some considerable object useful for > doing computations concerning words. In this view, language can do a > very powerful thing: by sending the labels of a number of powerful > modules, I send a program, so you can more or less run the same > program, thus perceiving more or less the same thought. This picture > also, to my mind, explains metaphor-- when you "spend" time you invoke > a "spend" object/method within valuable resource management (or at the > least an instance of it created in your time understanding program). > However, I don't understand how smaller modules within the brain or mind > could communicate like this, in English. The module that deals > with the word ``word" for example, in order to deal with a > sentence including lots of other words, would have to invoke the > other modules themselves. This is discussed at more length in > my book What is Thought?, if memory serves in Ch. 13. If you can > propose a solution to this, I would be most interested. > > (3) Cassimatis has another interesting proposal. He proposes that > all modules (at some high level of granularity) must support a stipulated > interlingua. They take requests in this interlingua, perhaps translate > them into internal language, do computations, and then return results > in the interlingua. It is the responsibility of the module designer > (or presumably module creation algorithm) to produce a module > supporting the interlingua. > ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
