Hi Jim, OK thanks! I don't know what you enjoy, what you are good at. So I don't know what to recommend. When you say "a simpler AI project", I don't know what that means. If you just want to learn - I dunno - Buy books and study them? Create software and see what happens? Read and think and repeat and you'll learn something? If you want to help with a larger project like opencog - there's 1001 things that need to be done, technical and non-technical.We need cheerleaders and janitors, developers and researchers. You name it. Opencog is not a small and simple project, but its also a zillion times smaller and simpler than whatever facebook and IBM and google are doing. But simply taking some inspired idea, and converting it into software, that's just a difficult task. No matter what the idea.
--linas On Mon, Feb 25, 2019 at 3:10 PM Jim Bromer <[email protected]> wrote: > Thank you for that introduction to mathematical symbolic interpretation. > The notation was familiar enough that I could follow what you were saying > but the explanations helped me to avoid getting confused. I found it to be > very interesting and I am thinking about it. As a matter of fact, I want to > work on a simpler AI type of project which would make the insights that you > must have very useful to me. Unfortunately, I am not sure I could find the > time to be of much use to you. If you ever decide to write a textbook about > this topic, I would be very interested. If you do write any additional > chapters I think you should make sure that you include a few worked > examples. If there is anyway I could help you without working full time on > the project, let me know. > Jim Bromer > > > On Sun, Feb 24, 2019 at 11:46 PM Linas Vepstas <[email protected]> > wrote: > >> Attached is a PDF that reviews a relationship between symbols and >> reality. Depending on your outlook, you will probably find it to be either >> trivial, or wrong, or useless or all of the above. -- linas >> >> On Sun, Feb 24, 2019 at 2:32 PM Nanograte Knowledge Technologies < >> [email protected]> wrote: >> >>> You'll probably need both, not one vs the other. I'd think, if the same >>> soft architecture was used for a neural net as the symbol net, the symbol >>> net would eventually outperform the neural net, but only by virtue of the >>> data integrity of the neural net. The symbol net could be viewed as one >>> abstraction of a neural net. The symbol net could be integrated with >>> floating memory. This seems unlikely for a neural net to achieve on its >>> own. >>> >>> I do not have have all the "correct" terms here. I also try and stay >>> away from some terms on purpose, so as to not become trapped by them. To me >>> some of these ideas are functional concepts - a dichotomy. >>> >>> From a dynamical memory store it should be possible to deploy a >>> functional "RNA"-type coding schema as possibly the most-abstract >>> de-abstraction of the whole system. This is the domain of paradox I >>> referred to before. I'll call this paradox because time ceases to exist >>> somewhere along this point, before some of it, or all, or nothing, >>> fragment(s) into multiple versions of 1 entity. This is a point (or >>> universe) where an iterative spark of tacit knowing (read possible >>> consciousness) probably occurs. >>> >>> I think the previously-mentioned "location" may be an "area" from which >>> tacit knowledge might enter the timespace continuum before spinning off >>> explicit artifacts. This is because in my view, location has relevance for >>> gravitational force (if gravity would still exist as such in science after >>> another 10 years). I tend to agree how the process I described might be >>> pertinent to an aspect of recombination, but one has to think >>> limitlessness. Recombination -as we seemingly know it biologically - seems >>> to be an insular concept. However, I think existentially it is a much >>> greater integral holistically than we may currently be able to imagine. At >>> this point, one would be given to much speculation. >>> >>> In my mind's eye, at a conceptual point of spark, I see an environmental >>> state of all and nothing and everything in between, finite infinity and so >>> on. Perhaps then, morphogenesis. I do not call this state "life". At best, >>> I'd refer to it as a superposition within informational communication. >>> >>> I'll need to put my theory to the test though. Hopefully, in my lifetime >>> then. :-) >>> >>> Robert Benjamin >>> >>> ------------------------------ >>> *From:* Jim Bromer <[email protected]> >>> *Sent:* Sunday, 24 February 2019 7:25 PM >>> *To:* AGI >>> *Subject:* Re: [agi] Some thoughts about Symbols and Symbol Nets >>> >>> Nano: I thought that you might be thinking of something like that. As I >>> tried to form a response I finally have started to make sense of it. A >>> discrete mathematical algorithm will suffer from the combinatorial >>> complications. A deep learning net seems to be able to deal with those >>> kinds of problems - as long as there are good approximate solutions >>> available. So then, is there anyway a symbol-net could outperform neural >>> nets (if rapidity was not a fundamental problem)? It seems to me that you >>> may be approaching a possible solution to that kind of situation. Does >>> that analysis make sense to you? >>> Jim Bromer >>> >>> >>> On Sat, Feb 23, 2019 at 3:39 PM Nanograte Knowledge Technologies < >>> [email protected]> wrote: >>> >>> <https://mathinsight.org/definition/network>In other words...the >>> purpose should be functional RNA. >>> >>> Now, is an AGI blueprint justified? >>> >>> https://en.wikipedia.org/wiki/Gene_regulatory_network >>> <https://en.wikipedia.org/wiki/Gene_regulatory_network> >>> Gene regulatory network - Wikipedia >>> <https://en.wikipedia.org/wiki/Gene_regulatory_network> >>> A gene (or genetic) regulatory network (GRN) is a collection of >>> molecular regulators that interact with each other and with other >>> substances in the cell to govern the gene expression levels of mRNA and >>> proteins. These play a central role in morphogenesis, the creation of body >>> structures, which in turn is central to evolutionary developmental biology >>> (evo-devo). >>> en.wikipedia.org >>> >>> >>> >>> >>> >>> ------------------------------ >>> *From:* Jim Bromer <[email protected]> >>> *Sent:* Saturday, 23 February 2019 6:31 PM >>> *To:* AGI >>> *Subject:* Re: [agi] Some thoughts about Symbols and Symbol Nets >>> >>> So now I am beginning to wonder if nano was talking about some kind of >>> syntactic rules of combination for symbols and that all knowledge would >>> have to 'emerge' by selection of which combination, within a network, were >>> selected by learning. >>> Jim Bromer >>> >>> >>> On Fri, Feb 22, 2019 at 1:54 AM Nanograte Knowledge Technologies < >>> [email protected]> wrote: >>> >>> If this were the case, I'd agree with you. What I'm proposing is content >>> independent and context dependent. It is suitable for CAS applications. It >>> is not "designed"to be constrained, but to identify and normalize a >>> primary, contextual constraint in order to resolve it in an adaptive sense. >>> Meaning, humans do not resolve it, but the contextually-bound instance of >>> the system does. By implication, all possible meanings of the symbol are >>> always resident and latent. However, the decisive meaning for a particular >>> context is alive for the duration of that contextual reference in the >>> greater schema of information. In other words, the correct answer is always >>> possible within a particular context. Such is the basis of critical >>> thinking, to derive the correct answer to every situation. Yes, there is an >>> underlying assumption, which is that a correct answer exists for every >>> context, but this could be proven scientifically. >>> >>> Previously, mention was made in the forum about hierarchy (meaning >>> control). Having hierarchy within a systems constructs provides a system >>> with robustness and integrity, which translates into informational >>> reliability. Now, it seems the question of validity has been settled, but >>> not the one on reliability. What I'm proposing already has embedded into >>> its language what could be termed validity and reliability, at scale. >>> >>> That is where the analogy of the tyre hitting the tar has relevance, or >>> the point in project and program management where the the essential truth >>> hits home. It is where the absolute impact on a situation has most effect. >>> We could also argue how it resembles the point of effective complexity, >>> which is the point-of-reasoning we are all desire within an AGI entity. >>> >>> You stated: "The term 'context-free' refers to the syntactic context, >>> not the greater global context (of variable type definitions or >>> redefinitions and so on)." >>> >>> >>I strongly disagree with this view. In a semantic system, which I >>> contend is required for a symbolic system to become operational, syntax >>> lends itself to context specificality. I think that point was born out via >>> recent discussions on the forum. >>> >>> I think no designer should (be allowed) arbitrarily decide local and >>> global boundaries. That's a recipe for disaster. Boundaries are outcomes of >>> the inherent (natural) design resident within components and collective >>> contexts. In addition to a specified, context boundary, the underlying >>> methodology should allow for scalability, which is not only an issue of >>> size, but also of adaptive scope (implying boundary adaptiveness). In this >>> sense, a contextual/systems boundary would be structured/unstructured in a >>> construct of thesis/antithesis - 2 parts of the same coin. Perhaps in using >>> this approach, we would achieve Haramein's et al's perspective on a >>> finite-infinity in a computational model. >>> >>> When looked at via a report, or a snapshot view, such a system would >>> appear to be structured (which it also is). However, if you could view it >>> as a continuous value stream, as a movie, it would be possible to watch >>> (and trace) how it morphed relative to its adaptive algorithm - as an >>> unstructured system. In time, for each specific context, it should become >>> possible to identify the patterns of such morphing, and apply predictive >>> algorithms. >>> >>> I think one outcome (there are multiple outcomes) of such a system would >>> resemble a Symbol Net. It should theoretically be possible to extract such >>> nets from the "live" system. I think this is rather similar to how we do it >>> within society today. >>> >>> Robert Benjamin >>> ------------------------------ >>> *From:* Jim Bromer <[email protected]> >>> *Sent:* Thursday, 21 February 2019 11:46 PM >>> *To:* AGI >>> *Subject:* Re: [agi] Some thoughts about Symbols and Symbol Nets >>> >>> A contextual reference framework, designed to limit the meaning of a >>> symbol to one meaning within a particular context, would only displace the >>> ambiguity - unless the language was artificially designed to be that way. >>> So called 'context-free' languages, ironically enough, do just that. They >>> have some value in AI, but it is difficult to see how it could be used as >>> an effective basis for stronger AI. The term 'context-free' refers to the >>> syntactic context, not the greater global context (of variable type >>> definitions or redefinitions and so on). Perhaps the term is misunderstood >>> or misused in compiler design, but, a lot like applied logic, its >>> application is useful because it can be limited to 'a framework' (like a >>> local function and so on). So perhaps industry did develop a way to limit >>> ambiguity within a contextual framework, but so far it has not proven to be >>> very useful in stronger AI. The nature of *limiting* ambiguity of a symbol >>> (or possible referential signification) does not seem to be a very powerful >>> tool to rely on when you are trying to stretch the reach of current (or 30 >>> year old) ideas to attain greater powers of 'understanding'. >>> Jim Bromer >>> >>> >>> On Thu, Feb 21, 2019 at 2:49 PM Nanograte Knowledge Technologies < >>> [email protected]> wrote: >>> >>> If one had a contextual reference framework, each symbol would always >>> have one meaning within a particular context. Searches would always be >>> optimal. An example of this is evidenced within the Japanese language. So, >>> the 30+ years of waiting was for no good reason. If only the industry had >>> developed appropriate theory for dealing with scalable ambiguity, which it >>> probably had. >>> >>> ------------------------------ >>> *From:* Jim Bromer <[email protected]> >>> *Sent:* Thursday, 21 February 2019 8:13 PM >>> *To:* AGI >>> *Subject:* Re: [agi] Some thoughts about Symbols and Symbol Nets >>> >>> I asked myself the question: If a theory of symbols was a feasible basis >>> for stronger AI, then the earlier efforts in discrete AI or weighted >>> reasoning should have show some promise. They should have worked. So why >>> didn't they work? Then I remembered that they did work with small data >>> sets. GOFAI did work as long as it could make a rapid search through the >>> possible candidates of meaning, but because combinations of symbols have >>> meaning, and because each symbol may have more than one meaning or referent >>> the problems of combinatorial complications presented a major obstacle to >>> developing the theories much further, My opinion is that the ambiguities or >>> multiple possible relevancies of a symbol (sub-net) can themselves be used >>> to narrow the possible meaning of the symbol (sub-net) when needed in >>> reasoning. We just need a huge amount of memory in order to create an index >>> of generalizations to use the information adequately. We now have that >>> scale of memory and processor speed available to us so we can try things >>> that could not be tried in the 1970s and 80s. >>> Jim Bromer >>> >>> >>> On Tue, Feb 19, 2019 at 12:45 AM Nanograte Knowledge Technologies < >>> [email protected]> wrote: >>> >>> Linas, Mike and Jim >>> >>> I find this to be a most-interesting conversation. Primarily, because it >>> suggests that the development of AGI may not only be challenged by the >>> development of competent theory, but also by programming capabilities to >>> put the theory into practice. >>> >>> Evolving such an architecture then, should desired outcomes be for an >>> AGI entity to achieve self-theory and self-programming? In its >>> most-simplistic from, a symbol is but a suitable abstraction of a greater >>> reality, similarly to how a symbol of a red-heart might be an abstraction >>> of a sentient being. Concept? Context? Meaning? Transaction. >>> >>> Who, or what decides what the symbolic world should look like and its >>> meaningfulness? The global state of social evolution may cause terrible >>> confusion in any learning entity. The learning objectives should be >>> specific, not generalized. Isn't learning incorrectly worse than not >>> learning at all? >>> >>> I think, there should be a general agi-architecture, replete with the >>> capacity to develop and function within a generic world view. Furthermore, >>> I think the real value would be derived from specialized AGI. Maybe beyond >>> that, an AGI architecture would - in future - morph via its own social >>> networking and inherent capabilities to become more than the sum of its >>> parts. >>> >>> To do so, would take a lot more than intersections. I agree with the >>> statements made about binary/vector theory, but it seems obvious to me that >>> this would not be sufficient for this task. You implied fractals. To my >>> mind, that would be the only way to proceed. As such, I think the primary >>> issue remains a design issue. >>> >>> Robert Benjamin >>> >>> ------------------------------ >>> *From:* Linas Vepstas <[email protected]> >>> *Sent:* Monday, 18 February 2019 10:36 PM >>> *To:* AGI >>> *Subject:* Re: [agi] Some thoughts about Symbols and Symbol Nets >>> >>> >>> >>> On Mon, Feb 18, 2019 at 1:17 PM Mike Archbold <[email protected]> >>> wrote: >>> >>> I'm not sure I completely follow your point, but I sort of get it. >>> >>> I tend to think of symbols as one type of the "AI stuff" a computer >>> uses to think with -- the other main type of "AI stuff" being neural >>> networks. These have analogies to the "mind stuff" we use to think >>> with. >>> >>> >>> Symbol systems and neural-net systems can be seen to be variants of the >>> same thing; two sides of the same coin. I posted an earlier thread on this. >>> There's a 50-page long PDF with math, here: >>> https://github.com/opencog/opencog/raw/master/opencog/nlp/learn/learn-lang-diary/skippy.pdf >>> >>> roughly: both form networks. They differ primarily in how they represent >>> the networks, and how they assign weights to network connections (and how >>> they update weights on network connections). >>> >>> >>> On their own, symbols don't mean anything, of course, and inherently >>> don't contain "understanding" in any definition of understanding. >>> >>> Is there a broad theory of symbols? We kind of proceed with loose >>> definitions. I remember reading the Newell and Simon works, and they >>> say AI strictly in terms of symbols and LISP (as I recall anyway). >>> >>> >>> Yes. The "broad theory of symbols" is called "model theory" by >>> mathematicians. It's highly technical and arcane. It's most prominent >>> distinguishing feature as that everything is binary: it is or it ain't. >>> Something is true, or false. A formula takes values, or there is no such >>> formula. A relation binds two things together, or there is no relation. >>> There's no blurry middle-ground. >>> >>> So, conventionally, networks of symbols, and the relations between them, >>> and the formulas transforming them -- these form a network, a graph, and >>> everything on that network/graph is a zero or a one -- an edge exists >>> between two nodes, or it doesn't. >>> >>> The obvious generalization is to make these fractional, to assign >>> weights. Neural nets do this. But neural nets do something else, that they >>> probably should not: they jam everything into vectors (or tensors) This is >>> kind-of OK, because the algebra of a graph is a lot like the algebra of a >>> vector space, and the confusion between the two is an excusable mistake: it >>> takes some sophistication to realize that they are only similar, but not >>> the same. >>> >>> I claim: fix both these things, and you've got a winner. Use symbolic >>> systems, but use fractional values, not 0/1 relations. Find a good way of >>> updating the weights. So, deep-learning is a very effective weight-update >>> algorithm. But there are other ways of updating weights too (that are >>> probably just as good or better. Next, clarify the >>> vector-space-vs-graph-algebra issue, and then you can clearly articulate >>> how to update weights on symbolic systems, as well. >>> >>> (Quickly explained: probabilities are not rotationally-symmetric under >>> the rotation group SO(N) whereas most neural-net vectors are: this is the >>> spot where deep-learning "gets it wrong": it incorrectly mixes gibbs >>> training functions with rotational symmetry.) >>> >>> So Jim is right: discarding symbolic systems in favor of neural nets is >>> a mistake; the path forward is at the intersection of the two: a net of >>> symbols, a net with weights, a net with gradient-descent properties, a net >>> with probabilities and probability update formulas. >>> >>> -- Linas >>> >>> >>> On 2/18/19, Jim Bromer <[email protected]> wrote: >>> > Since I realized that the discrete vs weighted arguments are passe I >>> > decided that thinking about symbol nets might be a better direction >>> for me, >>> > >>> > 1. A symbol may be an abstracted 'image' of a (relatively) lower level >>> > object or system. >>> > An image may consist of a feature of the referent, it may be an icon >>> of >>> > the referent or it may be a compressed form of the referent. >>> > 2. A symbol may be more like a 'label' for some object or system. >>> > 3. A generalization may be represented as an image of what is being >>> > generalized but it also may be more of a label. >>> > 4. An 'image', as I am using the term, may be derived from a part or >>> > feature of an object or from a part of a system but it may be used to >>> refer >>> > to the object or system. >>> > 5. An image or label may be used to represent a greater system. A >>> system >>> > may take on different appearances from different vantage points, and >>> > analogously, some features of interest may be relevant in one context >>> but >>> > not from another context. A symbol may be correlated with some other >>> > 'object' and may stand as a referent to it in some contexts. >>> > >>> > So, while some symbols may be applied to or projected onto a 'lower' >>> corpus >>> > of data, others would need to use an image to project onto the data >>> field. >>> > I use the term, 'lower' somewhat ambiguously, because I think it is >>> useful >>> > to symbolize a system of symbols so a 'higher' abstraction of a system >>> > might also be used at the same level. And it seems that a label would >>> have >>> > to be associated with some images if it was to be projected against the >>> > data. >>> > >>> > One other thing. This idea of projecting a symbol image onto some >>> data, in >>> > order to compare the image with some features of the data, seems like >>> it >>> > has fallen out of favor with the advancements of dlnns and other kinds >>> of >>> > neural nets. Projection seems like such a fundamental process that I >>> cannot >>> > see why it should be discarded just because it would be relatively slow >>> > when used with symbol nets. And, there are exceptions, GPUs, for >>> example, >>> > love projecting one image onto another. >>> > Jim Bromer >>> >>> >>> -- >>> cassette tapes - analog TV - film cameras - you >>> >> >> -- >> cassette tapes - analog TV - film cameras - you >> > *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/agi/subscription> Permalink > <https://agi.topicbox.com/groups/agi/Tcc0e554e7141c02f-Md1802f370b34424b1ae3d250> > -- cassette tapes - analog TV - film cameras - you ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tcc0e554e7141c02f-Ma10490221fd36de4ab48363c Delivery options: https://agi.topicbox.com/groups/agi/subscription
