Steve, A text based, would be semi-strong, AI program can 'experiment' with conceptual relations using text. I am not trying to create the equivalent of a 55 year old super-genius. I am trying to discover what has been missing in AI. I need to use simple experiments (my own simple experiments writing and improving my own AI program) to apply and develop my theories. One might complain that a text-only AI program is going to end up with stilted knowledge that does not have the vitality of human knowledge but so what? (A great deal of the knowledge that individuals possess is relatively stilted too.) I am looking for those methods that will acquire (admittedly limited) knowledge through something that appears like more natural language. So when I talk about using a Parallel Artificial Referent Language (PARL) I am not talking about using something that is like a purely artificial programming or a database language. I need to develop a program that can do a lot of learning on its own. So the PARL could be used to highlight relations between word-concepts in the text IO. This will not work perfectly because I need the AI program to be able to do some learning for itself. So at first it won't know anything but I can mark up the text with my PARL and it will start to make some trial and error experiments within the text-based IO to discover other relations that it can learn from both the text IO and from the PARL. And it will need to learn some syntax as well. So it will need to learn more than one thing from the exchanges.
At a next stage it will have noted some possible relations between particles of text but it will not really know anything else about them. But at this point it should begin to pick up some knowledge that it can relate to these relations. My program is going to be largely based on establishing categories and particulars of a kind. However, I have previously emphasized in my writing that these relations are relative and even relativistic. So there is really no such thing as a base category or a elementary particular except that they can be said to be these things relative to some other objects. For example, I plan to use my PARL to help the program acquire some information about syntax. So does syntax describe base relations of the IO? No, because you need to develop concepts about syntax in order to use language to 'talk' about it. So while a syntactic relation may be very basic to the program I need the program to be able to 'conceptualize' references to syntax in order to facilitate learning.. Even though the basics of my program are based on some very old AI paradigms, when you start to think this out a little more deeply you realize that 1970s Old AI really did not get down (so to speak using the lingo of the day.) So in a tight programming language you can use implicit relations that are defined without the program being able to understand anything else about them but if you want to achieve a more natural way to talk to the program about what you want it to do it has to be able to conceptualize fundamental relations. As it learns more about some simple subjects it should become more clear to the user what kind of mistakes the program is making. Here some simple PARL relations may help clarify the confusion and gradually simple text statements should be powerful enough to help to clarify the relations (that I want the program to note and work with.) Ambiguity and ambiguity like relations sometimes need to be resolved and sometimes they don't. This very simple fundamental philosophical insight seems to be something that few people ever acknowledge when they are discussing the problem of writing better AI programs with me. However, many linguists seem to clearly understand this principle in human communication. I have often said that in order to know one thing you have to know many hundreds of related things. So while studying probability and Bayesian Networks more carefully is on my to-do list, I have never suggested that weight-based reasoning would solve the contemporary AI problem. My program would, if I ever get that far, retain hundreds of pieces of knowledge related to some subject (to some subjects) and then it could analyze these collections in different ways as needed. I assume that I would use weighted reasoning when it works but the point is that the weighted analyses would not be the standard raw product of learning. Weight-based analyses would be one kind of derived product that could be used but they would be only one of many different kinds of derived products. The program would also be able to learn new ways to analyze related collections of data (pieces of knowledge.) Of course I realize that this will not be easy. But with the PARL it should be feasible to test crude prototypes of relations between data objects (concepts and such) which I might be able to use to further develop my program. When used with the right kind of AI program the Parallel Artificial Referent Language has the potential to be something very simple to use. That should make it more interesting to novices. However, it will not be a simple language to use in all cases because the utilization would be dependent on how the AI program subsequently used the artificial referent information. But if I can ever get anywhere with this project then I would be able to write a specialized AI program that would work in a more intuitive way with the PARL. This specialized AI program might not be a great AI program but if it is simple to use then a variety of young programmers might be interested in trying it. One other thing. I am interested in the generalization-particularization relations. For instance, I chose to try to imagine how my program would work using abstractions rather than particular examples. Why? Because my program will not be written about 'rain' or 'cars' or 'people'. It will be written at a more abstract level where the relations between individual concept-objects will be based on general methods of analysis and synthesis (or integration) that, for the most part, will be learned or derived from experience. Jim Bromer On Tue, Nov 10, 2015 at 1:08 AM, Steve Richfield <[email protected]> wrote: > Jim, > > You somehow completely misunderstood what I was attempting to communicate. > > Continuing... > On Mon, Nov 9, 2015 at 5:35 PM, Jim Bromer <[email protected]> wrote: > >> Steve, >> I understand what you are saying (even though the details of what you >> actually write are mostly irrelevant to me). >> > > ... which probably explains why you so misunderstood what I was > communicating. > > >> I have done many thought experiments about the AI problem. > > > Then please post some of them, e.g. what you might say, and what you might > expect in response. > > >> Your >> challenge in your last email is not something that is new to me, I >> have thought about that exact question many times. However, I finally >> figured out that instead of spending a lot of time trying to get you >> on a higher plane of discussion I should use the opportunity to my >> best advantage and try to explain my theories again. >> > > ... and MY point was that theories be damned, that there is only one known > way of extracting meaning from text at a practical rate of speed. If you > can't adapt THAT, then your quest is truly hopeless. > >> >> So the program does not need multiple IO facilities in order to >> understand some things. > > > I never said it did. I was addressing the ways that things can NOT work. > That can't be simply turned around to say that doing something else WILL > work. > > >> Why don't I think that multiple IO modalities >> are necessary for advancements in AI? I get that it would make a truly >> advanced AI program more sophisticated but trying to use sensors and >> robotics at this time would just make the contemporary programming >> more difficult. The data coming in through video, for example, needs >> to be analyzed before it can be used by an AI program. Stronger AI >> can't just take input from multiple IO modalities and automatically >> turn current AI into Strong(er) AI. >> >> But there is another side of the opinion that multiple IO modalities >> are necessary for Stronger AI. The most obvious one is that much or >> our human experience is based on multiple IO so of course a true AGI >> program would have to be adaptable to different IO situations. (I >> already get that.) However, the next question down is whether >> different senses and IO are absolutely necessary for any AGI program. >> > > My point was that EXPERIMENTATION is absolutely necessary to sort out the > countless possible realities that spring forth from textual explanations. > It is a bit difficult to do experimentation from inside a box. > > >> To make another effort to explain this point of view I will say once >> again in a new way that without certain advancements in AI no number >> if IO modalities are going to make the program work the way we would >> want to. So to highlight my area of interest in another way let me >> just restate that I want to do some study of how the AI part of the AI >> program can be improved in order to get it to work the way that I >> think it needs to work. >> >> I don't believe that current AI / AGI methods have Conceptual >> Integration methods that are sophisticated enough. >> I believe that my theories on Conceptual Integration, while being >> composed of methods that are mundane, should be powerful enough to >> deal with many of the contemporary problems. My methods will not >> produce true human-like AGI but I believe I can produce AI that human >> beings will be able to communicate with using language that is much >> closer to natural human language. That does not mean that the program >> will be able to understand anything that is readable, but that it will >> be able to work with knowledge that it has acquired and use a more >> natural kind of language to communicate about it. Does that help you >> to better understand what I am interested in? >> > > I think everyone here agrees that presently known methods do NOT lead to > AGI, regardless of how far you might extend them with better hardware, etc. > >> >> I have talked about ambiguity in language many times in this group. I >> am surprised that you don't remember that I did so. > > > Perhaps you missed my thread about disambiguation being a really bad idea? > > >> I have also talked >> about the anaphoric-like reference problem. >> >> Simple ambiguity is a human problem. For instance, you thought I was >> one of the guys who was promoting the theory that a language-based AI >> program would just be able to learn from the internet. (I am not one >> of those guys.) That was an ambiguity-type problem. It was also a >> reference problem. You misunderstood what I was referring to when I >> was talking about AI / AGI. It can be resolved by communication using >> natural language unless there is something interfering with that >> communication. >> >> However, the problem with natural language AI is that the potential >> for massive ambiguity and referential confusion makes the AI problem >> seem intractable. I believe that I have some good theories to deal >> with that - but at the same time I realize that I am going to run up >> against the knowledge complexity problem very quickly. >> >> So I will try to explain what I want the program to do after I have >> taken a day or two to plan my response carefully and write it out as >> simply as I can. >> > > OK. I'll look for that. > > *Steve* > =================== > >> >> Jim Bromer >> >> >> On Mon, Nov 9, 2015 at 3:10 AM, Steve Richfield >> <[email protected]> wrote: >> > Jim, >> > >> > OK, maybe a simple thought experiment will help. Presume for a moment >> that >> > the system you imagine exists here and now. You have just turned it on. >> Now, >> > start typing (or talking) to make it do what you want it to do: >> > >> > >> > >> > >> > >> > Steve >> > >> > On Sun, Nov 8, 2015 at 11:58 PM, Jim Bromer <[email protected]> >> wrote: >> >> >> >> Steve, >> >> I do not understand how you can misunderstand what I am talking about >> >> so badly since I have posted hundreds if not thousands of messages on >> >> groups like this. >> >> I am not planning on getting a computer program to learn by reading >> >> the internet with the expectation of having it achieve human like >> >> abilities. The supposition that that is what I am talking about >> >> strikes me as nonsensical. >> >> >> >> Unfortunately, I probably will not be able to do much at all but I >> >> hope to at least experiment with a higher learning mechanism that, for >> >> example, can be taught through conversation. In order to achieve this >> >> I am planning on using a parallel referent language which will be >> >> designed to shape knowledge about some things in ways that would be >> >> like rough prototypes of the kinds of ideas that I have in mind for an >> >> AI / AGI program. >> >> >> >> I agree that in order to 'understand' text the program would need to >> >> build models about reality. However, I am in total disagreement with >> >> the idea that an AI program would need to have some sort of embodiment >> >> to understand text about the world. Such thinking strikes me as >> >> absurdly naïve. The reason is that I believe there are very basic >> >> parts of AI methods that are almost totally missing in contemporary AI >> >> programs and the fantasy that if you could just slap some robotics >> >> onto one of these primitive AI designs then the program would >> >> magically be more capable of understanding is somewhere near the >> >> height of absurdity. >> >> >> >> So I want to try to improve on the AI programming by working on a >> >> design that would be able to integrate simple knowledge or knowledge >> >> about simple things together. I don't think deep learning is good >> >> enough for this because deep learning neural networks have a such a >> >> crude way of going about this integration of related ideas. Of course, >> >> I am aware that there are other problems with complexity that are >> >> difficult to work around. I have been trying to find a polynomial time >> >> solution to SAT for the past few years because I thought that would >> >> help me with complexity. However, I haven't been able to make any real >> >> progress on that and I just don't see how the human mind could >> >> possibly be using p=np in some way given the propensity of 'organic' >> >> processes to generate unique manifestations of very irregular >> >> patterns. >> >> >> >> So back to the main topic of my thought. Interactions with the sensory >> >> world of human beings is not necessary for AI because the information >> >> that comes in from those senses would have to be integrated well to >> >> produce 'understanding' just as information that comes in through the >> >> keyboard would need to be. It is my belief that smart conceptual >> >> integration is an outstanding problem in contemporary AI and it is a >> >> problem I can work on. >> >> >> >> Unfortunately I won't be able to do much work on the problem. Perhaps >> >> I squandered too much time writing in groups like this and working on >> >> my p=np? project. On the other hand, I hadn't really settled down on >> >> what I think is the best way to approach the problem until the last >> >> few years. But I would at least like to try to do something. >> >> Jim Bromer >> >> >> >> >> >> On Sun, Nov 8, 2015 at 5:16 PM, Steve Richfield >> >> <[email protected]> wrote: >> >> > Jim, >> >> > >> >> > You seem to be suffering from a malady that many others on this forum >> >> > appear >> >> > to be suffering from. The primary symptom is the belief that simple >> >> > observation of HIGHLY noise-ridden text can lead machines to useful >> >> > epiphanies. >> >> > >> >> > The problem is that pretty much everything people write is, in a >> word, >> >> > wrong. Overbroad statements (and natural languages can NOT precisely >> >> > bracket >> >> > statements), statements made based on unstated presumptions (and no >> one >> >> > can >> >> > list all their presumptions), statements made based on faulty models >> >> > (and >> >> > ALL models are faulty, as every physicist knows), confused meanings >> or >> >> > words >> >> > (most words have multiple meanings), lies made for economic or other >> >> > gain, >> >> > etc., etc., etc. It is difficult to find ANY clear statements that >> are >> >> > unquestionably accurate in all of their potential meanings. >> >> > >> >> > OK, so suppose you accept the above and simply want to do the best >> you >> >> > can. >> >> > Still, you can NOT approach the capabilities of an average person, >> >> > because >> >> > we have a real world in which to test the many possible >> interpretations >> >> > of >> >> > what we read, whereas a machine can only accept, reject, or assign a >> >> > probability with NO other information. >> >> > >> >> > I used to believe as you do - until 2001 when I became very sick. It >> >> > took me >> >> > 3 months of every conscious minute to figure out what was wrong and >> what >> >> > might be done about it. Sure I got most of my information from books >> or >> >> > the >> >> > Internet, but there remained several very different models in which >> this >> >> > all >> >> > fit, which I resolved with phone calls to key researchers, and a >> little >> >> > of >> >> > my own primary experimentation to sort the flyshit from the pepper. >> >> > Still, I >> >> > remained unsure until what should work did work to cure my condition. >> >> > >> >> > When I went back to figure out how the Internet might be reorganized >> to >> >> > reduce my search from 3 months to a few minutes, the structure of >> >> > DrEliza >> >> > emerged, and later my patent. >> >> > >> >> > The problem is that to usefully solve problems you need MODELS, yet >> >> > natural >> >> > language (and most human thinking) predates this concept and only >> >> > provides >> >> > INFORMATION. Sometimes you can construct a model from information, >> but >> >> > this >> >> > is RARE. Making models requires highly qualified geniuses who can get >> >> > their >> >> > arms around entire fields and synthesize models that fit ALL known >> >> > observations. I have done this in several narrow areas, but it will >> take >> >> > a >> >> > LOT more of this to transform the Internet to a model-based system >> from >> >> > which an AI/AGI can usefully address problems of all types. >> >> > >> >> > Further, natural language is highly granular - there are far more >> things >> >> > that you can NOT say than things you CAN say, so people without even >> >> > thinking about it round to the nearest syntactically expressible >> meaning >> >> > in >> >> > EVERYTHING they say or write. This "rounding" completely destroys any >> >> > ability to construct accurate models. >> >> > >> >> > Some languages have weak workarounds to this, e.g. German with its >> >> > concatenated words, or Arabic where they alter spellings for >> emphasis, >> >> > but >> >> > these measures only slightly reduce their granularity. >> >> > >> >> > All in all, each person here must either find a way to jump from >> >> > information >> >> > to models, or abandon this quest. Sure, information can help people >> >> > solve a >> >> > problems whose solution has already been stated, but there are >> already >> >> > plenty of experts around who do this quite well. It is the UNsolved >> >> > problems >> >> > that are interesting, and it is these UNsolved problems that can NOT >> >> > EVER be >> >> > solved by automated means based on people's writings. >> >> > >> >> > How can I say not EVER when writings continue to accumulate? Because >> >> > society's problems also continue to accumulate, so as people find >> >> > solutions >> >> > to past unsolved problems, even more new problems emerge to replace >> >> > them. >> >> > >> >> > I hereby proclaim your apparent quest to be theoretically >> unachievable >> >> > for >> >> > the many reasons outlined above. Sure it would be of astronomical >> value, >> >> > like the methodology to change lead into gold that so many people >> put so >> >> > much effort into, but why waste your time unless/until you can find >> SOME >> >> > way >> >> > around the above-listed barriers. >> >> > >> >> > Steve >> >> > ===================== >> >> > >> >> > On Sun, Nov 8, 2015 at 9:00 AM, Jim Bromer <[email protected]> >> wrote: >> >> >> >> >> >> Steve, >> >> >> It will take me some time to reply carefully so let me respond to >> >> >> something I feel strongly about. >> >> >> >> >> >> >>And because it is not an all encompassing >> >> >> language of communication it could be used to test the 'emergence' >> of >> >> >> insight that could arise if enough preparatory work had been done, >> >> >> even if I haven't figured out how that could be done without the >> >> >> artificial referent language. >> >> >> >> >> >> >> >> >> >> >There is a VAST chasm between being able to define language >> >> >> > constructions >> >> >> > and meanings, and "insight". >> >> >> > >> >> >> >> >> >> I believe there is a vast chasm between 'simple associations' or >> >> >> 'simple >> >> >> correlations' or associations derived from 'neural networks' and >> >> >> conceptual >> >> >> integration. Sophisticated artificial conceptual integration would >> make >> >> >> 'insight' feasible and simple examples across a wide range of >> subject >> >> >> matter >> >> >> should arise fairly quickly. But since AI programs are only capable >> of >> >> >> the >> >> >> simplest examples of 'insight' then declarations about the chasm >> >> >> between AI >> >> >> and 'insight' are expected. So I totally disagree with you about >> this. >> >> >> I >> >> >> feel that your feelings about this are historically accurate but >> have >> >> >> little >> >> >> to do with the potential near-future. As I say, I do not recall >> hearing >> >> >> about an AI program that is capable of learning via conversation >> except >> >> >> for >> >> >> extremely simple domains. I feel that I have a solution for this >> >> >> problem but >> >> >> the trial and error process of getting from where I am now and >> where I >> >> >> think >> >> >> I can get is so overwhelming a challenge that my decision to use the >> >> >> artificial referent para-language makes a sense. >> >> >> >> >> >> Jim Bromer >> >> >> >> >> >> On Sun, Nov 8, 2015 at 11:19 AM, Steve Richfield >> >> >> <[email protected]> wrote: >> >> >>> >> >> >>> Jim, >> >> >>> >> >> >>> FINALLY - SOMEONE who wants to discuss PRACTICAL implementations of >> >> >>> TAI/TAGI. >> >> >>> >> >> >>> Continuing... >> >> >>> On Sun, Nov 8, 2015 at 7:14 AM, Jim Bromer <[email protected]> >> >> >>> wrote: >> >> >>>> >> >> >>>> After I wrote that message I realized that I had tried to start >> >> >>>> discussions about an artificial language that could be used to >> shape >> >> >>>> a >> >> >>>> general AI program before. Many of these discussions were side >> >> >>>> tracked >> >> >>>> when people started talking about Esperanto or about lambda >> calculus >> >> >>>> based artificial languages and stuff like that. That is not what >> I am >> >> >>>> thinking of. >> >> >>> >> >> >>> >> >> >>> You mean, having syntax like: >> >> >>> >> >> >>> When that I write "xxxx" I mean "yyy". >> >> >>> >> >> >>> to define idioms, for more subtle things like: >> >> >>> >> >> >>> Consider that when I write "," I may mean ";". >> >> >>> >> >> >>> which expresses potential alternative interpretations of future >> >> >>> writings? >> >> >>>> >> >> >>>> >> >> >>>> The artificial language could be used with video or audio or other >> >> >>>> kinds of IO environments, but I would use it along side of an >> attempt >> >> >>>> to get the AI program to learn to use a natural language. >> >> >>> >> >> >>> >> >> >>> I did a VERY similar thing in a FORTRAN/ALGOL/BASIC compiler I once >> >> >>> wrote >> >> >>> for Remote Time-Sharing Corp. It started out as a very simplistic >> >> >>> metacompiler, to which I fed it a description of a more capable >> >> >>> metacompiler, in which language I fed it a description of an >> >> >>> optimizing >> >> >>> metacompiler. >> >> >>> >> >> >>> This could easily be done in a language like English, where a >> >> >>> rule-driven >> >> >>> system like I have been discussing here has rules whose function >> is to >> >> >>> introduce new rules. >> >> >>> >> >> >>>> >> >> >>>> One of the >> >> >>>> dreams of old AI was that if you started instructing the program >> to >> >> >>>> learn using the artificialities of some kind of language it would >> >> >>>> eventually have enough information for genuine learning to emerge. >> >> >>> >> >> >>> >> >> >>> The think that seems to be the killer here is erroneous learning of >> >> >>> various sorts. Superstitious learning is theoretical unavoidable. >> Once >> >> >>> you >> >> >>> get something erroneous into such a system, it becomes >> >> >>> difficult/impossible >> >> >>> to get it out. A VERY simple demonstration comes in trying to use >> >> >>> Dragon >> >> >>> NaturallySpeaking's speech input to correct its errors in your >> >> >>> dictation. As >> >> >>> you would expect it makes errors in trying to correct the errors, >> and >> >> >>> this >> >> >>> often compounds to overwhelm any hope of setting things right. >> >> >>> >> >> >>> Add to that not knowing exactly what a computer got wrong, or even >> >> >>> being >> >> >>> able to recognize that the computer got something wrong, and you >> can >> >> >>> see how >> >> >>> difficult/impossible it is to correct wrongly "learned" rules. >> >> >>> >> >> >>>> >> >> >>>> This never really worked. Why not? Partly because computers were >> not >> >> >>>> powerful enough in the old days >> >> >>> >> >> >>> >> >> >>> And still aren't - unless you use my patented LFU methodology. >> >> >>> >> >> >>>> >> >> >>>> and, in my opinion, AI researchers had >> >> >>>> not appreciated the necessity of sophisticated data integration >> >> >>>> methods for some reason. (Old computer systems might one day be >> shown >> >> >>>> to have been potentially powerful enough to run some future >> program >> >> >>>> but they were not powerful enough to entertain the trial and error >> >> >>>> process that would have been required using experimental programs >> of >> >> >>>> the day. >> >> >>> >> >> >>> >> >> >>> The advantage in LFU is about the same as the advantage of a >> modern PC >> >> >>> over an old vacuum tube clunker, so yes, they could have done a LOT >> >> >>> more way >> >> >>> back then. >> >> >>> >> >> >>> The "cycle time" of an IBM-709 computer was 12 microseconds, and >> most >> >> >>> instructions took two cycles - one to access and interpret the >> >> >>> instruction, >> >> >>> and one to access and operate on the operand. >> >> >>> >> >> >>>> >> >> >>>> For example, with better conceptual integration methods a >> >> >>>> future efficient AI program might be used on an old computer >> system >> >> >>>> just to show that it could be run on it.) >> >> >>> >> >> >>> >> >> >>> No, except for a few in the Computer Museum's display in Cupertino >> >> >>> they >> >> >>> have all been melted down for their scrap metal, and the Museum >> won't >> >> >>> turn >> >> >>> them back on. >> >> >>>> >> >> >>>> >> >> >>>> So the artificial referent language would not be a complete >> language >> >> >>>> (of communication) like Esperanto wants to be. And it would not >> be a >> >> >>>> logically sound language like lambda calculus wants to be. It >> could >> >> >>>> be >> >> >>>> used to establish referents from the IO data environment. It would >> >> >>>> need to be capable of denoting a distinction between how those >> data >> >> >>>> objects can be used. For example in natural language there is an >> >> >>>> important distinction between syntax and semantics. So if I used >> this >> >> >>>> referent language with a natural language IO then one of the >> >> >>>> artificialities would be to distinguish syntactic relations from >> >> >>>> semantic relations. On the other hand, this distinction is not >> always >> >> >>>> necessary, desired or clear cut. To explain this, many (or maybe >> >> >>>> most) >> >> >>>> (what I think are) desirable syntactic relations are based on some >> >> >>>> semantic conditions. But then again there is no reason not to >> design >> >> >>>> the artificial language to be able to represent relations that are >> >> >>>> mixes of semantics and syntax. >> >> >>> >> >> >>> >> >> >>> Leaving a stupid computer to untangle such messes is probably a >> >> >>> mistake. >> >> >>> However, it would be fairly easy to provide a mechanism for people >> to >> >> >>> specify such things. >> >> >>>> >> >> >>>> >> >> >>>> As I see it, the main problem with language based AI has been the >> >> >>>> lack >> >> >>>> of a really good conceptual integration solution. >> >> >>> >> >> >>> >> >> >>> This broad statement could be said about ANYTHING people haven't >> yet >> >> >>> seen >> >> >>> a way to make work - like AGI. >> >> >>>> >> >> >>>> >> >> >>>> One of the reasons I write to groups like this is that I want to >> get >> >> >>>> some ideas about how an idea might work. >> >> >>> >> >> >>> >> >> >>> Same here. >> >> >>> >> >> >>>> >> >> >>>> But when I wrote about an >> >> >>>> artificial para-language before I wasn't really sure it I even >> wanted >> >> >>>> to use it. I finally have come to the conclusion that it makes a >> lot >> >> >>>> of sense. I can use it to speed up tests about my AI/AGI theories >> but >> >> >>>> then I could also test those theories with more relaxed >> instructions. >> >> >>>> So the artificial para-referent language would not a all >> encompassing >> >> >>>> language of communication, it would not be a logically sound >> language >> >> >>>> other than to denote semantic and syntactic references and >> relations >> >> >>>> based on mixes of semantic and syntactic references. It could also >> >> >>>> denote relations that I think would be important to a text-based >> >> >>>> AI/AGI program. Because the logic of the method would not be tight >> >> >>>> and >> >> >>>> a contradicting case would not (always) lead to an artificially >> >> >>>> reported error, the AI methods would have to do some learning for >> >> >>>> itself. So the para-referent language would not sidetrack the >> whole >> >> >>>> effort because if the AI methods have to have the potential to >> >> >>>> exhibit >> >> >>>> some genuine learning. And because it is not an all encompassing >> >> >>>> language of communication it could be used to test the >> 'emergence' of >> >> >>>> insight that could arise if enough preparatory work had been done, >> >> >>>> even if I haven't figured out how that could be done without the >> >> >>>> artificial referent language. The benefit is that I could use it >> to >> >> >>>> test and develop my AI theories. I am really excited by this idea >> >> >>>> this >> >> >>>> time. >> >> >>> >> >> >>> >> >> >>> There is a VAST chasm between being able to define language >> >> >>> constructions >> >> >>> and meanings, and "insight". >> >> >>> >> >> >>> Steve >> >> >>> ====================== >> >> >>>> >> >> >>>> Jim Bromer >> >> >>>> >> >> >>>> >> >> >>>> On Sat, Nov 7, 2015 at 10:22 PM, Jim Bromer <[email protected]> >> >> >>>> wrote: >> >> >>>> > I was just working on my latest p=np? idea and I hit up against >> >> >>>> > method >> >> >>>> > that is either in np or is otherwise extremely inefficient. So I >> >> >>>> > have >> >> >>>> > to come to the conclusion that the human mind is not capable of >> SAT >> >> >>>> > in >> >> >>>> > p. >> >> >>>> > >> >> >>>> > So then how do we figure how to deal with so many complicated >> >> >>>> > situations? Of course I still don't know because so many >> situations >> >> >>>> > seem similar to a SAT problem. The mind must be able to detect >> many >> >> >>>> > different things that are going on at once or which might be >> useful >> >> >>>> > to >> >> >>>> > recall from memory to deal with a situation. But still, there is >> >> >>>> > nothing in my own introspective analysis of my thinking which >> looks >> >> >>>> > anything like a p=np process. >> >> >>>> > >> >> >>>> > So what is wrong with AI? One thing that AI has been >> consistently >> >> >>>> > lacking is the ability to learn through conversation. My >> feeling is >> >> >>>> > that this is not just a problem with communication but a >> learning >> >> >>>> > problem as well. In other words AI is not able to truly learn >> >> >>>> > except >> >> >>>> > in a few special cases. Most of those special cases are >> examples of >> >> >>>> > narrow AI but there are others where the learning that takes >> place >> >> >>>> > isn't necessarily like other narrow AI but where the domain of >> >> >>>> > learning is so restricted that it is narrow in the sense that >> the >> >> >>>> > applicability of the method is limited. >> >> >>>> > >> >> >>>> > Then I started thinking of an artificial language which can >> refer >> >> >>>> > to >> >> >>>> > situations or objects in the IO data environment and which can >> be >> >> >>>> > used >> >> >>>> > to instruct a program as it is running. I think this is an >> unusual >> >> >>>> > idea. >> >> >>>> > >> >> >>>> > One of the characteristics about programming methods that seem >> to >> >> >>>> > catch on with programmers is that they can be used in a very >> simple >> >> >>>> > manner and in more complicated programming. I think an >> artificial >> >> >>>> > language which could be used to instruct a computer to notice >> >> >>>> > objects >> >> >>>> > in the IO data environment and which could also be used to >> refine >> >> >>>> > those instructions using this artificial language with the >> >> >>>> > references >> >> >>>> > that it had previously established has a lot of potential. And >> it >> >> >>>> > can >> >> >>>> > help us become more clear about what is needed to make better >> AGI >> >> >>>> > programs. >> >> >>>> > Jim Bromer >> >> >> >> >> >> ------------------------------------------- >> >> AGI >> >> Archives: https://www.listbox.com/member/archive/303/=now >> >> RSS Feed: >> https://www.listbox.com/member/archive/rss/303/10443978-6f4c28ac >> >> Modify Your Subscription: https://www.listbox.com/member/?&; >> >> Powered by Listbox: http://www.listbox.com >> > >> > >> > >> > >> > -- >> > Full employment can be had with the stoke of a pen. Simply institute a >> six >> > hour workday. That will easily create enough new jobs to bring back full >> > employment. >> > >> > AGI | Archives | Modify Your Subscription >> >> >> ------------------------------------------- >> AGI >> Archives: https://www.listbox.com/member/archive/303/=now >> RSS Feed: >> https://www.listbox.com/member/archive/rss/303/10443978-6f4c28ac >> Modify Your Subscription: https://www.listbox.com/member/?&; >> Powered by Listbox: http://www.listbox.com >> > > > > -- > Full employment can be had with the stoke of a pen. Simply institute a six > hour workday. That will easily create enough new jobs to bring back full > employment. > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/24379807-653794b5> | > 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
