>>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 am not interested in an AI program that can only accept reject or assign a probability with no other information, so this reasoning does not apply to me. 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 <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> >> <https://www.listbox.com/member/archive/rss/303/10443978-6f4c28ac> | >> Modify <https://www.listbox.com/member/?&;> Your Subscription >> <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
