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 <[email protected]> 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 <[email protected]> > 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 <[email protected]> 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 <[email protected]> >> 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 >> <[email protected]> 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 <[email protected]> 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 <[email protected]> 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 <[email protected]> >>>>> 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 >>>>> <[email protected]> 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 <[email protected]> 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 >>>>>>> < >>>>>>> [email protected]> 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 <[email protected]> >>>>>>>> *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 >>>>>>>> <[email protected]> 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 <[email protected]> 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-M155d4762ea9c7b0f14fefd47> >>>>> 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: AGI Permalink: https://agi.topicbox.com/groups/agi/Tcc2adcdd20e1add4-M43fa75bcb6c04137e0b657f1 Delivery options: https://agi.topicbox.com/groups
