I am not a member of "research gate". Jim Bromer
On Tue, Jun 26, 2018 at 2:15 AM, Nanograte Knowledge Technologies via AGI < agi@agi.topicbox.com> wrote: > https://www.researchgate.net/publication/4363016_Last-mile_ > knowledge_engineering_Quest_for_the_holy_grail > > <https://www.researchgate.net/publication/4363016_Last-mile_knowledge_engineering_Quest_for_the_holy_grail> > Last-mile knowledge engineering: Quest for the holy grail? > <https://www.researchgate.net/publication/4363016_Last-mile_knowledge_engineering_Quest_for_the_holy_grail> > Download citation | Last-mile knowledge... | The problem of reliably > structuring unseen knowledge, at scale, persists within systems > engineering. An emergence-based method was developed to test the theory of > applying de-abstraction reasoning to tacit-knowledge engineering. > www.researchgate.net > > ------------------------------ > *From:* Jim Bromer via AGI <agi@agi.topicbox.com> > *Sent:* Monday, 25 June 2018 10:32 PM > > *To:* AGI > *Subject:* Re: [agi] Discrete Methods are Not the Same as Logic > > Please provide a link to the method you are talking about. > Jim Bromer > > > On Sun, Jun 24, 2018 at 6:59 AM, Nanograte Knowledge Technologies via > AGI <agi@agi.topicbox.com> wrote: > > Convention is a language. The program has to find a way to understand > what > > people say, and not say. It has to be able to learn the deeper meaning > > within the human conversation, and systemically get to the heart of every > > matter. It has to do this in the most-true manner, utilizing > evidence-based > > objectivity where possible. That method exists. It's the one I shared > here. > > That's exactly what it does. What it needs to become automated is the > > development of its own GUI, as translator. > > ________________________________ > > From: Jim Bromer via AGI <agi@agi.topicbox.com> > > Sent: Saturday, 23 June 2018 3:55 PM > > > > To: AGI > > Subject: Re: [agi] Discrete Methods are Not the Same as Logic > > > > I am thinking of a program that would learn by communicating with > > people using language. It would learn from interacting with people. > > The problem with that strategy is that it would tend to acquire > > superficial knowledge. It would however be required to do some true > > learning. One reason is that a person cannot think of all the > > relations and implicit categories that an intelligent entity would > > have to rely on. Secomdly, we cannot, at this time, understand all the > > sorts of a knowledge items that it would need to gain greater > > understanding. > > It would not be given predetermined categories other than some default > > second level abstract categories. These second level abstractions > > might be concerned with abstractions of relations found in discrete > > relationships that would be expected to found in networks of related > > information. It would have to work around the complexities that might > > develop. I am not talking about pure logic but discrete learning so > > the np problem is not a problem. The "discrete networks" would also > > include weighted reasoning of course. I am just saying that weighted > > reasoning isn't necessary but that discrete learning, learning by > > using ideas and developing principles of thought is important. > > But I have to be able to develop this as an extremely simple > > programming project that will quickly show some simple results (like > > feasibility tests) or else I am not going to have anything to start > > with. > > > > Jim Bromer > > > > > > On Sat, Jun 23, 2018 at 8:51 AM, Nanograte Knowledge Technologies via > > AGI <agi@agi.topicbox.com> wrote: > >> Jim > >> > >> I agree with making things simple, but one should not make it more > simple > >> than necessary. Any algorithm relying on deabstraction to provide proof > of > >> true learning would be highly complex. There's no simple solution to > that > >> problem. However, I'm enjoying your sentiment how, within deabstraction, > >> even complexity should become relative over time. Maybe one day, the > >> machine > >> would've learned how to invent deabstraction algorithms until it became > a > >> simple matter of instinct. > >> > >> Since when do human beings discover all its learning by itself? That's a > >> fallacy. An AGI platform also does not have to discover all of its > >> learning > >> by itself. It can be taught until such time it can learn how to organize > >> resources in order to teach itself and learn via reflection. > >> > >> Rob > >> ________________________________ > >> From: Jim Bromer via AGI <agi@agi.topicbox.com> > >> Sent: Saturday, 23 June 2018 2:41 AM > >> > >> To: AGI > >> Subject: Re: [agi] Discrete Methods are Not the Same as Logic > >> > >> Maybe I should use a name different than judgement. Reflection? > >> Insightful reflection. The depth of the insight would be relative to > >> how much knowledge, related to the questions being examined, was > >> available. So in the primitive model this insight would not be very > >> good and the program would have to be dependent on what the teacher > >> could convey to it. But insight would have to be based on putting > >> different kinds of information together. Novel insight might be > >> reinforced simply by being in the ballpark, it would not have to be > >> perfect as long as it was tagging along somewhere within the subject > >> matter being discussed, described or within the boundaries of > >> understanding something about a situation that was occurring. I think > >> different agi's would have to be different if they were thinking for > >> themselves - to some extent. > >> Jim Bromer > >> > >> > >> On Fri, Jun 22, 2018 at 3:10 PM, Mike Archbold via AGI > >> <agi@agi.topicbox.com> wrote: > >>> Judgments are fascinating. It seems like most approaches are some > >>> variation of reinforcement learning. What have you got in mind? One > >>> thought from Hegel which always sticks in my mind is that a "judgment > >>> could be other than what it is." So just think about that last > >>> sentence. How on earth could anyone automate that? But, more so, two > >>> distinct AGI's would always be different on that account. > >>> > >>> On 6/22/18, Jim Bromer via AGI <agi@agi.topicbox.com> wrote: > >>>> I need to start with something that is extremely simple and which will > >>>> produce some kind of result pretty quickly. I have had various ideas > >>>> about it for some time but what I see now is that a necessary > >>>> advancement for AI would have to exhibit some kind of judgment about > >>>> what it learns about. I realized the importance of making a program > >>>> that could learn new ways of thinking. Since I believe that > >>>> categorical reasoning is important then that means that it would not > >>>> only have to use abstractions but it would also have to be able to > >>>> discover abstractions of its own. This does not seem too difficult > >>>> because I am not being unreasonable about requiring it to be a > >>>> historical singularity inflexion point. I need to start with > >>>> something simple that demonstrates an ability for true learning. What > >>>> I see now is that it also has to exhibit some kind of simple > >>>> judgement. I need to come up with simple judgement algorithms. I > >>>> cannot get started unless I can come up with simple feasible models > >>>> that I can test. > >>>> I respectfully disagree with you about one thing. The elaboration of > >>>> an extensive framework and management system is, in my opinion, a > >>>> waste of time. It is like planning an AI program that will create AGI > >>>> for you completely on its own. It might be ok to think about such a > >>>> thing but it is nowhere to start out for an actual programming > >>>> project. I have to start with something that is very simple and which > >>>> can show some immediate results. For me, simplification is a necessity > >>>> but it is also necessary to avoid the wrong kinds of simplification. > >>>> Jim Bromer > >>>> > >>>> > >>>> On Fri, Jun 22, 2018 at 12:13 AM, Nanograte Knowledge Technologies via > >>>> AGI <agi@agi.topicbox.com> wrote: > >>>>> Jim, I think for this kind of reasoning to evolve, one would always > >>>>> have > >>>>> to > >>>>> return to an ontological platform. For example, for reasoning, one > >>>>> would > >>>>> require a meta-methodology for reasoning effectively with. For > >>>>> selectively > >>>>> forgetting and learning, an evolution-based methodology is required. > >>>>> For > >>>>> managing Logic, one would need a suitable framework and management > >>>>> system, > >>>>> and so on. These are all critical components, or nodes, that would > have > >>>>> to > >>>>> exist for self-optimized reasoning functionality to become > >>>>> spontaneous.The > >>>>> real IP lie not only in the methods, in the sense of AI apps. > >>>>> > >>>>> Yuu stated: "...DL story is compelling it is not paying out to > stronger > >>>>> AI > >>>>> (Near AGI)..." > >>>>>>>>Is it possible that AGI is an outcome, an act of becoming, and not > a > >>>>>>>> discrete objective at all? > >>>>> > >>>>> Rob > >>>>> ________________________________ > >>>>> From: Jim Bromer via AGI <agi@agi.topicbox.com> > >>>>> Sent: Thursday, 21 June 2018 5:20 PM > >>>>> To: AGI > >>>>> Subject: Re: [agi] Discrete Methods are Not the Same as Logic > >>>>> > >>>>> Symbol Based Reasoning is discrete, but a computer can use discrete > >>>>> data that would not make sense to us so the term symbolic might be > >>>>> misleading. I am not opposed to weighted reasoning (like neural > >>>>> networks or Bayesian Networks) and I think reasoning has to use > >>>>> networks of relations. If weighted networks can be thought of as a > >>>>> symbolic network then that suggests that symbols may not be discrete > >>>>> (as different from Neural Networks.) I just think that there is > >>>>> something missing with DL, and while the Hinton...DL story is > >>>>> compelling it is not paying out to stronger AI (Near AGI). For > >>>>> example, I think that symbolic reasoning which is able to change its > >>>>> categorical bases of reasoning is something that is badly lacking in > >>>>> Discrete Learning. You don't want your program to forget everything > it > >>>>> has learned just because some doofus tells it to, and you do not want > >>>>> it to write over the most effective methods it uses to learn just to > >>>>> deal with some new method of learning. So, that, in my opinion is > >>>>> where the secret may have been hiding. A program that is capable of > >>>>> learning something new must be capable of losing its more primitive > >>>>> learning techniques without wiping out the good stuff that it had > >>>>> previously acquired. This requires some working wisdom. > >>>>> I have been thinking about these ideas for a long time but now I feel > >>>>> that I have a better understanding of how this insight might be used > >>>>> to point to simple jumping off point. > >>>>> Jim Bromer > >>>>> > >>>>> > >>>>> On Thu, Jun 21, 2018 at 2:48 AM, Mike Archbold via AGI > >>>>> <agi@agi.topicbox.com> wrote: > >>>>>> So, by "discrete reasoning" I think you kind of mean more or less > "not > >>>>>> neural networks" or I think some people say, or used to say NOT > "soft > >>>>>> computing" to mean, oh hell!, we aren't really sure how it works, or > >>>>>> we can't create what looks like a clear, more or less deterministic > >>>>>> program like in the old days etc.... Really, the challenge a lot of > >>>>>> people, myself included, have taken up is how to fuse discrete (I > >>>>>> simply call it "symbolic", although nn have symbols, typically you > >>>>>> don't see them except as input and output) and DL which is such a > good > >>>>>> way to approach combinatorial explosion. > >>>>>> > >>>>>> To me reasoning is mostly conscious, and kind of like the way an > >>>>>> expert system chains, logically. The understanding is something > else > >>>>>> riding kind of below it and less conscious but it has all the common > >>>>>> sense rules of reality which constrain the upper level reasoning > which > >>>>>> I think is logical, like "if car won't start battery is dead" would > be > >>>>>> the conscious part but the understanding would include such mundane > >>>>>> details as "a car has one battery" and "you can see the car but it > is > >>>>>> in space which is not the same thing as you" and "if you turn around > >>>>>> to look at the battery the car is still there" and all such details > >>>>>> which lead to an understanding. But understanding is an incredibly > >>>>>> tough thing to make a science out of, although I see papers lately > and > >>>>>> conference topics on it. > >>>>>> > >>>>>> On 6/20/18, Jim Bromer via AGI <agi@agi.topicbox.com> wrote: > >>>>>>> I was just reading something about the strong disconnect between > our > >>>>>>> actions and our thoughts about the principles and reasons we use to > >>>>>>> describe why we react the way we do. This may be so, but this does > >>>>>>> not > >>>>>>> show > >>>>>>> how we come to understand basic ideas about the world. This attempt > >>>>>>> to > >>>>>>> make > >>>>>>> a nearly total disconnect between reasons and our actual reactions > >>>>>>> misses > >>>>>>> something when it comes to explaining how we know anything, > including > >>>>>>> how > >>>>>>> we learn to make decisions about something. One way to get around > >>>>>>> this > >>>>>>> problem is to say that it all takes place in neural networks which > >>>>>>> are > >>>>>>> not > >>>>>>> open to insight about the details. But there is another explanation > >>>>>>> which > >>>>>>> credits discrete reasoning with the ability to provide insight and > >>>>>>> direction and that is we are not able to consciously analyze all > the > >>>>>>> different events that are occurring at a moment and so we probably > >>>>>>> are > >>>>>>> reacting to many different events which we could discuss as > discrete > >>>>>>> events > >>>>>>> if we had the luxury to have them all brought to our conscious > >>>>>>> attention. > >>>>>>> So logic and personal principles are ideals which we can use to > >>>>>>> examine > >>>>>>> our > >>>>>>> reactions - and our insights - about the what is going on around us > >>>>>>> but > >>>>>>> it > >>>>>>> is unlikely that we can catalogue all the events that surround us > and > >>>>>>> (partly) cause us to react the way we do. > >>>>>>> > >>>>>>> Jim Bromer > >>>>>>> > >>>>>>> On Wed, Jun 20, 2018 at 6:06 AM, Nanograte Knowledge Technologies > via > >>>>>>> AGI > >>>>>>> < > >>>>>>> agi@agi.topicbox.com> wrote: > >>>>>>> > >>>>>>>> "As Julian Jaynes put it in his iconic book *The Origin of > >>>>>>>> Consciousness > >>>>>>>> in the Breakdown of the Bicameral Mind* > >>>>>>>> > >>>>>>>> Reasoning and logic are to each other as health is to medicine, > or — > >>>>>>>> better — as conduct is to morality. Reasoning refers to a gamut of > >>>>>>>> natural > >>>>>>>> thought processes in the everyday world. Logic is how we ought to > >>>>>>>> think > >>>>>>>> if > >>>>>>>> objective truth is our goal — and the everyday world is very > little > >>>>>>>> concerned with objective truth. Logic is the science of the > >>>>>>>> justification > >>>>>>>> of conclusions we have reached by natural reasoning. My point here > >>>>>>>> is > >>>>>>>> that, > >>>>>>>> for such natural reasoning to occur, consciousness is not > necessary. > >>>>>>>> The > >>>>>>>> very reason we need logic at all is because most reasoning is not > >>>>>>>> conscious > >>>>>>>> at all." > >>>>>>>> > >>>>>>>> > >>>>>>>> https://cameroncounts.wordpress.com/2010/01/03/ > mathematics-and-logic/ > >>>>>>>> > >>>>>>>> > >>>>>>>> > >>>>>>>> > >>>>>>>> <https://cameroncounts.wordpress.com/2010/01/03/ > mathematics-and-logic/> > >>>>>>>> Mathematics and logic | Peter Cameron's Blog > >>>>>>>> > >>>>>>>> > >>>>>>>> <https://cameroncounts.wordpress.com/2010/01/03/ > mathematics-and-logic/> > >>>>>>>> Apologies: this will be a long post, and there will be more to > come. > >>>>>>>> But > >>>>>>>> it may be useful to you if you are getting to grips with logic: I > >>>>>>>> have > >>>>>>>> tried to keep the overall picture in view. > >>>>>>>> cameroncounts.wordpress.com > >>>>>>>> > >>>>>>>> > >>>>>>>> ------------------------------ > >>>>>>>> *From:* Jim Bromer via AGI <agi@agi.topicbox.com> > >>>>>>>> *Sent:* Wednesday, 20 June 2018 12:01 PM > >>>>>>>> *To:* AGI > >>>>>>>> *Subject:* Re: [agi] Discrete Methods are Not the Same as Logic > >>>>>>>> > >>>>>>>> Discrete statements are used in programming languages. So a symbol > >>>>>>>> (a > >>>>>>>> symbol phrase or sentence) can be used to represent both data and > >>>>>>>> programming actions. Discrete Reasoning might be compared to > >>>>>>>> something > >>>>>>>> that has the potential to be more like an algorithm. (Of course, > >>>>>>>> operational statements may be retained as data which can be run > when > >>>>>>>> needed) > >>>>>>>> For an example of the value of Discrete Methods, let's suppose > >>>>>>>> someone > >>>>>>>> wanted more control over a neural network. Trying to look for > logic > >>>>>>>> in > >>>>>>>> a neural network does not really make all that much sense if you > >>>>>>>> want > >>>>>>>> to find relationships between actions on the net and output. Using > >>>>>>>> Discrete Methods makes a lot of sense. You might want to try > >>>>>>>> fiddling > >>>>>>>> with the weights of some of the nodes as the nn is running. If > >>>>>>>> certain > >>>>>>>> effects can be described (or sensed by some algorithm) then > >>>>>>>> describing > >>>>>>>> what was done and what effects were observed would be the next > step > >>>>>>>> in > >>>>>>>> the research. Researchers are not usually able to start with > >>>>>>>> detailed > >>>>>>>> knowledge of exactly what is going on. So they need to start with > >>>>>>>> descriptions of some actions they took and of what effects were > >>>>>>>> observed. If these actions and effects can be categorized in some > >>>>>>>> way > >>>>>>>> then the chance that more effective observations will be obtained > >>>>>>>> will > >>>>>>>> increase. > >>>>>>>> Jim Bromer > >>>>>>>> > >>>>>>>> > >>>>>>>> On Tue, Jun 19, 2018 at 11:12 PM, Mike Archbold via AGI > >>>>>>>> <agi@agi.topicbox.com> wrote: > >>>>>>>> > It sounds like you need both for AI, certainly there is always a > >>>>>>>> > place > >>>>>>>> > for logic. What's "discrete reasoning"? > >>>>>>>> > > >>>>>>>> > On 6/18/18, Jim Bromer via AGI <agi@agi.topicbox.com> wrote: > >>>>>>>> >> I am wondering about how Discrete Reasoning is different than > >>>>>>>> >> Logic. > >>>>>>>> >> I > >>>>>>>> >> assume that Discrete Reasoning could be described, modelled or > >>>>>>>> >> represented by Logic, but as a more practical method, logic > would > >>>>>>>> >> be > >>>>>>>> >> a > >>>>>>>> >> tool to use with Discrete Reasoning rather than as a > >>>>>>>> >> representational > >>>>>>>> >> substrate. > >>>>>>>> >> > >>>>>>>> >> Discrete Reasons and Discrete Reasoning can have meaning over > and > >>>>>>>> >> above the True False values of Logic (and the True False > >>>>>>>> >> Relationships > >>>>>>>> >> between combinations of Propositions.) > >>>>>>>> >> > >>>>>>>> >> Discrete Reasoning can have combinations that do not have a > >>>>>>>> >> meaning > >>>>>>>> >> or > >>>>>>>> >> which do not have a clear meaning. This is one of the most > >>>>>>>> >> important > >>>>>>>> >> distinctions. > >>>>>>>> >> > >>>>>>>> >> It can be used in various combinations of hierarchies and/or in > >>>>>>>> >> non-hierarchies. > >>>>>>>> >> > >>>>>>>> >> It can, for the most part, be used more freely with other > >>>>>>>> >> modelling > >>>>>>>> >> methods. > >>>>>>>> >> > >>>>>>>> >> Discrete Reasoning may be Context Sensitive in ways that > produce > >>>>>>>> >> ambiguities, both useful and confusing. > >>>>>>>> >> > >>>>>>>> >> Discrete Reasoning can be Active. So a statement about some > >>>>>>>> >> subject > >>>>>>>> >> might, for one example, suggest that you should change your > >>>>>>>> >> thinking > >>>>>>>> >> about (or representation of) the subject in a way that goes > >>>>>>>> >> beyond > >>>>>>>> >> some explicit propositional description about some object. > >>>>>>>> >> > >>>>>>>> >> You may be able to show that Logic can be used in a way to > allow > >>>>>>>> >> for > >>>>>>>> >> all these effects, but I believe that there is a strong > argument > >>>>>>>> >> for > >>>>>>>> >> focusing on Discrete Reasoning, as opposed to Logic, when you > are > >>>>>>>> >> working directly on AI. > >>>>>>>> >> > >>>>>>>> >> Jim Bromer > >>>>>>>> *Artificial General Intelligence List > >>>>>>>> <https://agi.topicbox.com/latest>* > >>>>>>>> / AGI / see discussions <https://agi.topicbox.com/groups/agi> + > >>>>>>>> participants <https://agi.topicbox.com/groups/agi/members> + > >>>>>>>> delivery > >>>>>>>> options <https://agi.topicbox.com/groups> Permalink > >>>>>>>> > >>>>>>>> > >>>>>>>> > >>>>>>>> <https://agi.topicbox.com/groups/agi/Tcc2adcdd20e1add4- > >>>>> Artificial General Intelligence List / AGI / see discussions + > >>>>> participants > >>>>> + delivery options Permalink > >> Artificial General Intelligence List / AGI / see discussions + > >> participants > >> + delivery options Permalink > > Artificial General Intelligence List / AGI / see discussions + > participants > > + delivery options Permalink > *Artificial General Intelligence List <https://agi.topicbox.com/latest>* > / AGI / see discussions <https://agi.topicbox.com/groups/agi> + > participants <https://agi.topicbox.com/groups/agi/members> + delivery > options <https://agi.topicbox.com/groups> Permalink > <https://agi.topicbox.com/groups/agi/Tcc2adcdd20e1add4-M2cb6fbb9cc9ea093627d9334> ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tcc2adcdd20e1add4-Ma68c03e3db0b5ed46981a86b Delivery options: https://agi.topicbox.com/groups
