Re: [agi] Early Apps.
Gary Miller wrote: That being said other than Cyc I am at a loss to name any serious AI efforts which are over a few years in duration and have more that 5 man years worth of effort (not counting promotional and fundraising). No offense, but I suspect you need to read more of the literature. I still am rather clueless about the field, and I can name a few such projects. In Hofstadter's lab both the Metacat and Letter Spirit projects are each the product of roughly a man-decade of effort, one man (or woman) at a time. The Tabletop project might count as more effort in the same design, not to mention Copycat's precursors. It's likely that someone will be working on extending Metacat soon. Elsewhere, there's the ACT-R project at CMU, formerly ACT-*, about which I know very little, but it seems to have been around for a while. At Indiana University David Leake's case-based reasoning project seems to have multiple grad students, probably pushing it over 5 man years quickly, although if by serious AI you meant general AI now it might not qualify. -xx- Damien X-) --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] Early Apps.
As I posted to this mailing list a few months ago, I have a list (now including 10 projects) that: a.. Each of them has the plan to eventually grow into a thinking machine or artificial general intelligence (so it is not merely about part of AI); b.. Each of them has been carried out for more than 5 years (so it is more than a PhD project); c.. Each of them has prototypes or early versions finished (so it is not merely a theory), and there are some publications explaining how it works (so it is not merely a claim). Ben has a similar list at http://www.agiri.org/agilinks.htm. If by serious AI efforts you don't restrict the field to AGI (or strong AI, real AI, and so on), then there are hundreds of projects with more that 5 man years worth of effort. Pei - Original Message - From: Damien Sullivan [EMAIL PROTECTED] To: [EMAIL PROTECTED] Sent: Monday, December 30, 2002 5:57 PM Subject: Re: [agi] Early Apps. Gary Miller wrote: That being said other than Cyc I am at a loss to name any serious AI efforts which are over a few years in duration and have more that 5 man years worth of effort (not counting promotional and fundraising). No offense, but I suspect you need to read more of the literature. I still am rather clueless about the field, and I can name a few such projects. In Hofstadter's lab both the Metacat and Letter Spirit projects are each the product of roughly a man-decade of effort, one man (or woman) at a time. The Tabletop project might count as more effort in the same design, not to mention Copycat's precursors. It's likely that someone will be working on extending Metacat soon. Elsewhere, there's the ACT-R project at CMU, formerly ACT-*, about which I know very little, but it seems to have been around for a while. At Indiana University David Leake's case-based reasoning project seems to have multiple grad students, probably pushing it over 5 man years quickly, although if by serious AI you meant general AI now it might not qualify. -xx- Damien X-) --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED] --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] Early Apps.
Sorry, I forgot to mention that my list is at http://www.cis.temple.edu/~pwang/203-AI/Lecture/203-1126.htm. Happy New Year to everyone! Pei - Original Message - From: Pei Wang [EMAIL PROTECTED] To: [EMAIL PROTECTED] Sent: Monday, December 30, 2002 6:26 PM Subject: Re: [agi] Early Apps. As I posted to this mailing list a few months ago, I have a list (now including 10 projects) that: a.. Each of them has the plan to eventually grow into a thinking machine or artificial general intelligence (so it is not merely about part of AI); b.. Each of them has been carried out for more than 5 years (so it is more than a PhD project); c.. Each of them has prototypes or early versions finished (so it is not merely a theory), and there are some publications explaining how it works (so it is not merely a claim). Ben has a similar list at http://www.agiri.org/agilinks.htm. If by serious AI efforts you don't restrict the field to AGI (or strong AI, real AI, and so on), then there are hundreds of projects with more that 5 man years worth of effort. Pei - Original Message - From: Damien Sullivan [EMAIL PROTECTED] To: [EMAIL PROTECTED] Sent: Monday, December 30, 2002 5:57 PM Subject: Re: [agi] Early Apps. Gary Miller wrote: That being said other than Cyc I am at a loss to name any serious AI efforts which are over a few years in duration and have more that 5 man years worth of effort (not counting promotional and fundraising). No offense, but I suspect you need to read more of the literature. I still am rather clueless about the field, and I can name a few such projects. In Hofstadter's lab both the Metacat and Letter Spirit projects are each the product of roughly a man-decade of effort, one man (or woman) at a time. The Tabletop project might count as more effort in the same design, not to mention Copycat's precursors. It's likely that someone will be working on extending Metacat soon. Elsewhere, there's the ACT-R project at CMU, formerly ACT-*, about which I know very little, but it seems to have been around for a while. At Indiana University David Leake's case-based reasoning project seems to have multiple grad students, probably pushing it over 5 man years quickly, although if by serious AI you meant general AI now it might not qualify. -xx- Damien X-) --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED] --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
RE: [agi] Early Apps.
Gary Miller wrote: I agree that as humans we bring a lot of general knowledge with us when we learn a new domain. That is why I started off with the general conversational domain and am now branching into science, philosophy, mathematics and history. And of course the AI can not make all the connections without being extensively interviewed on a subject and having a human help clarify it's areas of confusion just as a parent answers questions for a child or a teacher for a student. I am not in fact trying to take the exhaustive approach one domain at a time approach but rather to teach it the most commonly known and requested information first. My last email just used that description to identify my thoughts on grounding. I am hoping that by doing this and repeating the interviewing process in an iterative development cycle that eventually the bot will eventually be able to discuss many different subjects at a somewhat superficial level much as same as most humans are capable of. This is a lot different from the exhaustive definition that Cyc provides for each concept. Gary, I respect the hypothesis you're making here: it is a scientific hypothesis in the sense of Karl Popper, i.e. it is pragmatically falsifiable. You can try with this approach and see how it works. It is not identical to the expert systems approach, though it has some commonalities. My own intuition is that this approach will not succeed -- that conversing with humans is not going to get across enough of the tacit, implicit knowledge that a mind needs to have to really converse intelligently in any nontrivial subject area. I think that even if the implicit knowledge seems to *us* to be there in the conversations, it won't be there *for the system* unless the system has had some experience gaining implicit knowledge of its own via nonlinguistic world-interaction. I don't think AI is absent sufficient theory, just sufficient execution. Well, here I profoundly disagree with you. I think that the generally-accepted AI theories are profoundly wrong, and extremely limited in their view of how intelligence must operate. I think AI's failure to execute is directly based on the failure of its theories to accept and encompass the full complexity of the mind. I feel like the Cyc Project's heart was in the right place and the level of effort was certainly great, but perhaps the purity of their vision took priority over usability of the end result. Is any company actually using Cyc as anything other than a search engine yet? That being said other than Cyc I am at a loss to name any serious AI efforts which are over a few years in duration and have more that 5 man years worth of effort (not counting promotional and fundraising). My Novamente project certainly fits this description. The Webmind AI project had about 70 man-years of effort go into it between 1997-early 2001. Novamente is Webmind's successor -- different code, different mathematics, different software architecture, but the same spirit, and building on Webmind's successes and mistakes. Novamente has had maybe 7 man-years of effort go into it since mid-2001. -- Ben G --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
RE: [agi] Early Apps.
Gary Miller wrote: *** I guess I'm still having trouble with the concept of grounding. If I teach/encode a bot with 99% of the knowledge about hydrogen using facts and information available in books and on the web. It is now an idiot savant in that it knows all about hydrogen and nothing about anything else and it is not grounded. But if I then examine the knowledge learned about hydrogen for other mentioned topics like gases, elements, water, atoms, etc... And teach/encode 99% of of the knowledge on these topics to the bot. Then the bot is still an idiot savant but less so isn't it better grounded? A certain amount of grounding I think has occurred by providing knowledge of related concepts. If we repeat this process again, we may say the program is an idiot savant in chemistry. ... I will agree that today's bots are not grounded because they are idiot savants and lack the broad based high level knowledge with which to ground any given fact or concept. But if I am correct in my thinking this is the same problem that Helen Keller's teacher was faced with in teaching Helen one concept at a time until she had enough simple information or knowledge to build more complex knowledge and concepts upon. *** What you're describing is the Expert System approach to AI, closely related to the common sense approach to AI. Cycorp takes this point of view, and so have a whole lot of other AI projects in the last few decades... I certainly believe there's some truth to it. If you encoded a chemistry textbook in formal logic, fed it into an AI system, and let the AI system do a lot of probabilistic reasoning and associating on the information, then you'd have a lot of speculative uncertain intuitive knowledge generated in the system, complementing the hard knowledge that was explicitly encoded. If you encoded a physics textbook and a bio textbook as well, you could have the system generate uncertain, intuitive cross-domain knowledge in the same way. In fact, we are doing something like this in Novamente now, for a bioinformatics application. We're feeding in information from a dozen different bio databases and letting the system reason on the integrated knowledge right now we're at the feeding in stage. Unlike some anti-symbolic-AI extremists, I think this sort of thing can be *useful* for AGI. But I think it can only be a part of the picture. Whereas I think experience-based learning is a lot more essential I don't think that a pragmatically-achievable amount of formally-encoded knowledge is going to be enough to allow a computer system to think deeply and creatively about any domain -- even a technical domain about science. What's missing, among other things, is the intricate interlinking between declarative and procedural knowledge. When humans learn a domain, we learn not only facts, we learn techniques for thinking and problem-solving and experimenting and information-presentation .. and we learn these in such a way that they're all mixed up with the facts In theory, I believe, all this stuff could be formalized -- but the formalization isn't pragmatically possible to do, because we humans don't explicitly know the techniques we use for thinking, problem-solving, etc. etc. In large part, we do them tacitly, and we learn them tacitly.. When we learn a new domain declaratively, we start off by transferring some of our tacit knowledge from other domains to that new domain. Then, we gradually develop new tacit knowledge of that domain, based on experience working in the domain... I think that this tacit knowledge (lots of uncertain knowledge, mixing declarative procedural) has got to be there as a foundation, for a system to really deploy factual knowledge in a creative fluent way... *** I think we cut and paste what we are trying to say into what we think is the correct template and then read it back to ourselves to see if it sounds like other things we have heard and seems to make sense. *** I think this is a good description of one among many processes involved in language generation... I also think there's some more complex unconscious inference going on, than is implied by your statement. It's not a matter of cutting and pasting into a template, it's a matter of recursively applying a bunch of syntactic rules that build up complex linguistic forms from simpler ones. The syntactic buildup process has parallels to the thought-buildup process, and the two sometimes proceed in synchrony, which is one of the reasons formulating thoughts in language can help clarify them. I dealt with some of these issues -- on a conceptual, not an implementational level - in a chapter in my book from complexity to creativity, entitled Fractals and Sentence Production: http://www.goertzel.org/books/complex/ch9.html If I were to rewrite that chapter now, it would have a lot of stuff on probabilistic inference unification grammars -- richer and better details, enhanced by the particular
RE: [agi] Early Apps.
Ben Goertzal wrote: I don't think that a pragmatically-achievable amount of formally-encoded knowledge is going to be enough to allow a computer system to think deeply and creatively about any domain -- even a technical domain about science. What's missing, among other things, is the intricate interlinking between declarative and procedural knowledge. When humans learn a domain, we learn not only facts, we learn techniques for thinking and problem-solving and experimenting and information-presentation .. and we learn these in such a way that they're all mixed up with the facts What you're describing is the Expert System approach to AI, closely related to the common sense approach to AI. ... I agree that as humans we bring a lot of general knowledge with us when we learn a new domain. That is why I started off with the general conversational domain and am now branching into science, philosophy, mathematics and history. And of course the AI can not make all the connections without being extensively interviewed on a subject and having a human help clarify it's areas of confusion just as a parent answers questions for a child or a teacher for a student. I am not in fact trying to take the exhaustive approach one domain at a time approach but rather to teach it the most commonly known and requested information first. My last email just used that description to identify my thoughts on grounding. I am hoping that by doing this and repeating the interviewing process in an iterative development cycle that eventually the bot will eventually be able to discuss many different subjects at a somewhat superficial level much as same as most humans are capable of. This is a lot different from the exhaustive definition that Cyc provides for each concept. I view what I am doing distinct from expert systems because I do not yet use either a backward or forward inference engine to satisfy a limited number of goal states. The knowledge base is not in the form of rules but rather many matched patterns and encoded factoids of knowledge many of which are transitory in nature and track the context of the conversation. Each pattern may trigger a request for additional information like an expert system. But the bot does not have a particular goal state in mind other that learning new information unless a specific request of it is made by the user. I also differ from Cyc in that realizing the importance of English as a user interface from the beginning, all internal thoughts and goal states occur as an internal dialog in English. This eliminates the requirement to translate an internal knowledge representation to an external natural language other than providing one or response patterns to specific input patterns. It also makes it easy to monitor what the bot is learning and whether it is making proper inferences because it's internal thought process is displayed in English while in debug mode.. The templates which generate the responses in some cases do have conditional logic to determine which output template is appropriate response based on the AI's personality variables and the context of the current conversation. Variables are also set conditionally to maintain metadata for context. If the references a male in it's response [He] and [Him] get set vs. [Her] and [She] if a female is referenced. [CurrentTopic], [It], [There] and [They] are all set to maintain backward contextual references. I was able to find a few references to the Common Sense approach to AI on google and some of the difficulties in achieving it. And I must admit I have not yet implemented non-monotonic reason or probabilistic reasoning as of yet. I am not under the illusion that I am necessarily inventing or implementing anything that has not been conceived of before. As Newton says if I achieve great heights it will be because I have stood on the shoulders of giants. I just see the current state of the art and think that it can be made much better. I do not actually know how far I can take it while staying self-funded, but hopefully by the time my money runs out it will demonstrate enough utility and potential to be of value to someone. I think I like the sound of the Common Sense Approach to AI though. I can't remember the last time anyone accused me of having common sense, but I like the sound of it! I don't think AI is absent sufficient theory, just sufficient execution. I feel like the Cyc Project's heart was in the right place and the level of effort was certainly great, but perhaps the purity of their vision took priority over usability of the end result. Is any company actually using Cyc as anything other than a search engine yet? That being said other than Cyc I am at a loss to name any serious AI efforts which are over a few years in duration and have more that 5 man years worth of effort (not counting promotional and fundraising). The Open Source efforts are interesting and have some utility but are
Re: [agi] Early Apps.
Alan Grimes wrote: According to my rule of thumb, If it has a natural language database it is wrong, I more or less agree... Currently I'm trying to learn Italian before I leave New Zealand to start my PhD. After a few months working through books on Italian grammar and trying to learn lots of words and verb forms and stuff and not really getting very far, I've come to realise just how complex language is! Many of you will have learnt a second language as an adult yourselves and will know what I mean - natual languages are massively complex things. I worked out that I know about 25,000 words in English, many with multiple means, many having huges amounts of symbol grounding information and complex relationships with other things I know, then there is spelling information and grammar knowledge and I'm told that English grammar isn't too complex, but my Italian grammar reference book is 250 pages of very dense information on irregular verbs and tenses etc... and of course even that is only a high level ridged structure description not how the language is actually used. Natural languages are hard - really hard. Humans have special brain areas that are set up to solve just this kind of problem and even then it takes a really long time to get good at it, perhaps ten years! To work something that complex out using a general intelligence rather than specialised systems would require a computer that was amazingly smart in my opinion. One other thing; if one really is focused on natural language learning why not make things a little easier and use an artificial language like Esperanto? Unlike like highly artificial languages like logic based or maths based etc. languages, Esperanto is just like a normal natural language in many ways. You can get novels written in it, you can speak it, some children have even grown up speaking it as one of their first languages along side other natural languages. However the language is extremely regular compared to a real natural language. For example there are only 16 rules of grammar - they can fit onto an single sheet of paper! All the verbs and adverbs and pronouns and so on obey neat and tidy patterns and rules. I'm told that after two weeks somebody can become comfortable enough with the grammar to be able to hold a conversation and then after a few months of learning more words is able to communicate quite freely and read books and so on. Why not aim at this and make the job much easier? If you ever did build a computer that could hold a good conversation in Esperanto I'm sure moving to a natural language would only be a matter of taking what you already had and increasing the level of complexity to deal with all the additional messiness required. Enough rating for today! :) Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
RE: [agi] Early Apps.
On Dec 26 Ben Goertzel said: One basic problem is what's known as symbol grounding. this means that an Ai system can't handle semantics, language-based cognition or even advanced syntax if it doesn't understand the relationships between its linguistic tokens and patterns in the nonlinguistic world. I guess I'm still having trouble with the concept of grounding. If I teach/encode a bot with 99% of the knowledge about hydrogen using facts and information available in books and on the web. It is now an idiot savant in that it knows all about hydrogen and nothing about anything else and it is not grounded. But if I then examine the knowledge learned about hydrogen for other mentioned topics like gases, elements, water, atoms, etc... And teach/encode 99% of of the knowledge on these topics to the bot. Then the bot is still an idiot savant but less so isn't it better grounded? A certain amount of grounding I think has occurred by providing knowledge of related concepts. If we repeat this process again, we may say the program is an idiot savant in chemistry. Each time we repeat the process are we not grounding the previous knowledge further because the bot can now reason and respond to questions not just about hydrogen, it now has an English representation of the relationship between hydrogen and other related concepts in the physical world.. If we were to teach someone such as Helen Keller with very limited sensory inputs would we not be attempting to do the same thing? Humans of course do not learn in this exhaustive manner. We get a shotgun bombardment of knowledge from all types of media on all manner of subjects. The things that interest us we pursue additional knowledge about. The more detailed our knowledge in any given area the greater we say our expertise is. Initially we will be better grounded than a bot, because as children we learn a little bit about a whole lot of things. So anything new we learn we attempt to tie into our semantic network. When I think. I think in English. Yes, at some level below my conscious awareness these English thoughts are electrochemically encoded, but consciously I reason and remember in my native tongue or I retrieve a sensory image, multimedia if you will. If someone tells me that A kinipsa is terrible plorid. I attempt to determine what a kinipsa and a plorid are so that I may ground this concept and interconnect it correctly within my existing semantic network. If A bot is taught to pursue new knowledge and ground the unknown terms with it's existing semantic net by putting the goals Find out what a plorid is and Find out what a kinipsa is on it's list of short term goals then it will ask questions and seek to ground itself as a human would! I will agree that today's bots are not grounded because they are idiot savants and lack the broad based high level knowledge with which to ground any given fact or concept. But if I am correct in my thinking this is the same problem that Helen Keller's teacher was faced with in teaching Helen one concept at a time until she had enough simple information or knowledge to build more complex knowledge and concepts upon. When a child learns to speak he does not have a large dictionary to draw on to tell him that mice is the plural of mouse. No rule will tell him that. He has to learn it. He will say mouses and someone will correct him. It gets added to his NLP database as an exception to the rule. A human has limited storage so a rule learned by generalizing from experience is a shortcut to learning and remembering all the plural forms for nouns. In a AGI we can give the intelligence certain learning advantages such as these dictionaries and lists of synonym sets which do not take that much storage in the computer. I also think that children do not deal with syntax. They have heard a statement similar to what they want to express and have this stored as a template in their minds. I think we cut and paste what we are trying to say into what we think is the correct template and then read it back to ourselves to see if it sounds like other things we have heard and seems to make sense. For people who have to learn a foreign language as an adult this is difficult because they tend to think in their first language and commingle the templates from their original and the new language. But because we do not parse what we here and read strictly by the laws of syntax we have little trouble understanding many of these ungrammatical utterances. -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]] On Behalf Of [EMAIL PROTECTED] Sent: Thursday, December 26, 2002 11:03 PM To: [EMAIL PROTECTED] Subject: RE: [agi] Early Apps. On 26 Dec 2002 at 10:32, Gary Miller wrote: On Dec. 26 Alan Grimes said: According to my rule of thumb, If it has a natural language database it is wrong, Alan I can see based on the current generation of bot technology why one
RE: [agi] Early Apps.
On Dec. 26 Alan Grimes said: According to my rule of thumb, If it has a natural language database it is wrong, Alan I can see based on the current generation of bot technology why one would feel this way. I can also see people having the view that biological systems learn from scratch so that AI systems should be able to also. Neither of these arguments are particularly persuasive though based on what I've developed to date. Do you have other arguments against a NLP knowledge based approach that you could share with me. If you feel this is out of bounds for the list please just Email with your arguments. I am involved in such a project and certainly don't wish to to be wasting my time! -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]] On Behalf Of Alan Grimes Sent: Thursday, December 26, 2002 1:12 AM To: [EMAIL PROTECTED] Subject: [agi] Early Apps. According to my rule of thumb, If it has a natural language database it is wrong, many of the proposed early AGI apps are rather unfeasable. However, there is a very interesting application which goes streight to the hart to the main AI problem and also provides a very valuble tool for flexing the chips that we already have in our sweatty little hands. The area is COMPILERS. Today's compilers are notoriously bad. The leading free compiler is atrociously bad. Now, if there could be an AI based compiler that could both understand the source and the machine in a very human-like way the output code would be that much better. This would also be valuble for a bootstrap AI though I strongly caution against such an AI untill we have a _MUCH_ better understanding of what is going on. I expect to be preparing a proposal in a few months that will outline a complete strategy for an AI that should be both fesable and, through inhreant architectual constraints, be reasonably safe. -- pain (n): see Linux. http://users.rcn.com/alangrimes/ --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED] --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] Early Apps.
Neither of these arguments are particularly persuasive though based on what I've developed to date. !+ d03$n'7 vv0rk b3cuz $uch 4 $!st3m c4n'+ r34d m! 31337 +3x+. I am involved in such a project and certainly don't wish to to be wasting my time! I would be out of place to say anything about your project even if I did know the specifics of your goals. The only thing I'm saying is be realistic about the limitations of your approach. -- pain (n): see Linux. http://users.rcn.com/alangrimes/ --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] Early Apps.
On Thu, Dec 26, 2002 at 01:44:25PM -0800, Alan Grimes wrote: A human level intelligence requires arbitrary acess to visual/phonetic/other faculties in order to be intelligent. I'm sure all those blind and deaf people appreciate being considered unintelligent. -xx- Damien X-) --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] Early Apps.
Damien Sullivan wrote: A human level intelligence requires arbitrary acess to visual/phonetic/other faculties in order to be intelligent. I'm sure all those blind and deaf people appreciate being considered unintelligent. It depends. If their brains are intact they are no less intelligent than their peers. However there are some forms of blindness that involves cortical lesions... This form of blindness is accompanied by a loss of visual faculties and hence a partial loss of intelligence. -- pain (n): see Linux. http://users.rcn.com/alangrimes/ --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] Early Apps.
Gary Miller wrote: AG A human level intelligence requires arbitrary access to AG visual/phonetic/other faculties in order to be intelligent. By this definition of intelligence then we must conclude the Helen Keller was totally lacking in intelligence. You are confusing the visual faculty (a region of cortex) with the sense of sight (through the organ called the eye). I beleive that her actual faculties were intact but her senses were dammaged. -- pain (n): see Linux. http://users.rcn.com/alangrimes/ --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
RE: [agi] Early Apps.
On 26 Dec 2002 at 10:32, Gary Miller wrote: On Dec. 26 Alan Grimes said: According to my rule of thumb, If it has a natural language database it is wrong, Alan I can see based on the current generation of bot technology why one would feel this way. I can also see people having the view that biological systems learn from scratch so that AI systems should be able to also. Neither of these arguments are particularly persuasive though based on what I've developed to date. Do you have other arguments against a NLP knowledge based approach that you could share with me. One basic problem is what's known as symbol grounding. this means that an Ai system can't handle semantics, language-based cognition or even advanced syntax if it doesn't understand the relationships between its linguistic tokens and patterns in the nonlinguistic world. However, this problem doesn't totally rule out use of a linguistic DB. One could imagine supplying a system with a linguistic DB and having it learn groundings for the words and structures in the DB... Another problem is what I call the knowledge richness problem. The basic idea here is that if a system learns something through experience, it then is likely to know that something in an adaptable, adjustable way. Because it knows not only the thing itself, but a bunch of other things in the neighborhood of that thing, various useful components and superstructures of the thing, etc. it knows these other related things as side-effects of the learning process. On the other hand, if a system learns something through reading out of a DB, it doesn't have this surround of related things to draw on, so it will be far less able to adapt and build on that thing it's learned... My view is that a linguistic DB is not necessarily the kiss of death for an AGI system -- but I don't think you can build an AGI system that has a DB as its *primary source* of linguistic knowledge. If an AGI system uses a linguistic DB as one among many sources of linguistic information -- and the others are mostly experience-based -- then it may still work, and the linguistic DB may potentially accelerate aspects of its learning.. Ben G --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
