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 <linasveps...@gmail.com> 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 < > nano...@live.com> 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 <jimbro...@gmail.com> >> *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 < >> nano...@live.com> 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 <jimbro...@gmail.com> >> *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 < >> nano...@live.com> 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 <jimbro...@gmail.com> >> *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 < >> nano...@live.com> 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 <jimbro...@gmail.com> >> *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 < >> nano...@live.com> 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 <linasveps...@gmail.com> >> *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 <jazzbo...@gmail.com> >> 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 <jimbro...@gmail.com> 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-M79ada4efaa109badec01cb90> > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tcc0e554e7141c02f-Md1802f370b34424b1ae3d250 Delivery options: https://agi.topicbox.com/groups/agi/subscription
