This is interesting. I came across similar ideas too and i posted them on the AGI facebook page last year june. here they are for comparison.
this post is about the way things repeat and change in the world. in short something repeats for a while , such as you passing houses while you are walking down a road. So you pass house after house...then you get to an intersection and there are no more houses, but after that you then find that the thing that repeats is "passing houses AND intersections"...so you group the houses with the intersection and you pass this new grouping many times before you get to a mall, then you group all three things together and you keep walking past this new grouping untill you get outside the city, then you group the city and the country side and you start passing many cities and country sides as you go, then this becomes counries and continents and planets and solar systems and galaxies...in short this process describes reality, from the way a piece of wood bark is rough to the way we even think in mathematics there is a topic called fractals that describes shapes that look the same at different scales,here is a fractal shape that looks the same even when you zoom in. So as you walk past houses , think of that as zooming in to different scales and finding the same object you started with, house after house represents scale after scale. this is more complicated however because after the first set of scales we change focus, and then zoom in on this new grouping/focus as if that was the fractal.... There are other type of fractal like shapes or at least objects that follow the principle that are more applicable to this. Called tilings. These are tiles or identical shapes that are placed side by side and fill a space with no gaps in between them. So the steps you take while walking would each be a tile , while when you stop that becomes a tile of a different shape from the stepping tiles that you join to them...then this new grouping of tiles becomes the shape that you are tiling, when this changes you tile the combination of the change with the original tiles. this is a multi shape tiling that is binary in nature. Even the stepping tiling can be broken into two different tiles, one for each leg...and so on. A meriology is something that is made of parts. A chair is made of parts that are made of parts all the way down to atoms and even further. the parts of a chair are separated by space and time. The parts of the tiling above may be seperated by space and time such as walking or the texture of a surface, but it can also be seperated by something even stranger. think of the tiling where you are left handed while everyone else is right handed. What separtes the lefties as a group from the righties. it cant be the normal space or time as they are not litteraly seperated by a demarcation placed somewhere. If we could specify a type of space that these two tiles are filling wouldnt that simply be a conceptual space? and if we were to tile a space with concepts would that not be thinking? So we already have a way to use this in AI. On Fri, Jun 11, 2021 at 2:33 AM Linas Vepstas <[email protected]> wrote: > I just wrote up a new blog post on ... well, the usual topic. I'm cc'ing > the Link Grammar mailing list, as it has been instrumental in waking me to > these ideas. > > -- Linas > > ---------- Forwarded message --------- > From: OpenCog Brainwave <[email protected]> > Date: Thu, Jun 10, 2021 at 6:55 PM > Subject: [New post] Everything is a Network > To: <[email protected]> > > > Linas Vepstas posted: "The goal of AGI is to create a thinking machine, a > thinking organism, an algorithmic means of knowledge representation, > knowledge discovery and self-expression. There are two conventional > approaches to this endeavor. One is the ad hoc assembly of assorted" > > New post on *OpenCog Brainwave* > <https://blog.opencog.org/?author=5> Everything is a Network > <https://blog.opencog.org/2021/06/10/everything-is-a-network/> by Linas > Vepstas <https://blog.opencog.org/?author=5> > > The goal of AGI > <https://en.wikipedia.org/wiki/Artificial_general_intelligence> is to > create a thinking machine, a thinking organism, an algorithmic means of > knowledge > representation > <https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning>, > knowledge > discovery <https://en.wikipedia.org/wiki/Knowledge_extraction> and > self-expression > <https://en.wikipedia.org/wiki/Natural_language_generation>. There are > two conventional approaches to this endeavor. One is the ad hoc assembly of > assorted technology pieces-parts, > <https://www.youtube.com/watch?v=y_oem9BqUTI> with the implicit belief > that, after some clever software engineering, it will just come alive. The > other approach is to propose some grand over-arching theory-of-everything > that, once implemented in software, will just come alive and become the > Singularity <https://en.wikipedia.org/wiki/Technological_singularity>. > > This blog post is a sketch of the second case. As you read what follows, > your eyes might glaze over, and you might think to yourself, "oh this is > silly, why am I wasting my time reading this?" The reason for this is that, > to say what I need to say, I must necessarily talk in such generalities, > and provide such silly, childish examples, that it all seems a bit vapid. > The problem is that a theory of everything must necessarily talk about > everything, which is hard to do without saying things that seem obvious. Do > not be fooled. What follows is backed up by some deep and very abstract > mathematics that few have access to. I'll try to summon a basic > bibliography at the end, but, for most readers who have not been studying > the mathematics of knowledge for the last few decades, the learning curve > will be impossibly steep. This is an expedition to the Everest of > intellectual pursuits. You can come at this from any (intellectual) race, > creed or color; but the formalities may likely exhaust you. That's OK. If > you have 5 or 10 or 20 years, you can train and work out and lift weights. > You can get there. And so... on with the show. > > The core premise is that "everything is a network > <https://en.wikipedia.org/wiki/Network_theory>" -- By "network", I mean a > graph <https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)>, > possibly with directed edges, usually with typed > <https://en.wikipedia.org/wiki/Type_theory> edges, usually with weights, > numbers, and other data on each vertex or edge. By "everything" I mean > "everything". Knowledge, language, vision, understanding, facts, deduction, > reasoning, algorithms, ideas, beliefs ... biological molecules... > everything. > > A key real-life "fact" about the "graph of everything" is it consists > almost entirely of repeating sub-patterns. For example, "the thigh bone > is connected to the hip bone <https://en.wikipedia.org/wiki/Dem_Bones>" > -- this is true generically for vertebrates > <https://en.wikipedia.org/wiki/Vertebrate>, no matter which animal it > might be, or if it's alive or dead, it's imaginary or real. The patterns > may be trite, or they may be complex. For images/vision, an example might > be "select all photos containing a car > <https://en.wikipedia.org/wiki/CAPTCHA>" -- superficially, this requires > knowing how cars look alike, and what part of the pattern is important > (wheels, windshields) and what is not (color, parked in a lot or flying > through space <https://where-is-tesla-roadster.space/live>). > > The key learning task is to find such recurring patterns, both in fresh > sensory input (what "the computer" is seeing/hearing/reading right now) and > in stored knowledge (when processing a dataset - previously-learned, > remembered knowledge - for example, a dataset of medical symptoms). The > task is not just "pattern recognition > <https://en.wikipedia.org/wiki/Pattern_recognition>" identifying a photo > of a car, but of pattern discovery > <https://en.wikipedia.org/wiki/Frequent_pattern_discovery> -- learning > that there are things in the universe called "cars", and that they have > wheels and windows -- extensive and intensive properties. > > Learning does not mean "training > <https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets>" -- > of course, one can train, but AGI cannot depend on some pre-existing > dataset, gathered by humans, annotated by humans. Learning really means > that, starting from nothing at all, except one's memories, one's sensory > inputs, and one's wits and cleverness, one discovers something new, and > remembers it. > > OK, fine, the above is obvious to all. The novelty begins here: The best > way to represent a graph with recurring elements in it is with "jigsaw > puzzle <https://en.wikipedia.org/wiki/Jigsaw_puzzle> pieces". (and NOT > with vertexes and edges!!) The pieces represent the recurring elements, and > the "connectors" on the piece indicate how the pieces are allowed to join > together. For example, the legbone has a jigsaw-puzzle-piece connector on > it that says it can only attach to a hipbone. This is true not only > metaphorically, but (oddly enough) literally! So when I say "everything is > a network" and "the network is a composition of jigsaw puzzle pieces", the > deduction is "everything can be described with these (abstract) jigsaw > pieces." > > That this is the case in linguistics has been repeatedly rediscovered by > more than a few linguists. It is explained perhaps the most clearly and > directly in the original > <https://www.cs.cmu.edu/afs/cs.cmu.edu/project/link/pub/www/papers/ps/tr91-196.pdf> > Link > Grammar > <https://www.cs.cmu.edu/afs/cs.cmu.edu/project/link/pub/www/papers/ps/LG-IWPT93.pdf> > papers, although I can point at some other writings as well; one from a > "classical" > (non-mathematical) humanities-department linguist > <https://www.academia.edu/36534355/The_Molecular_Level_of_Lexical_Semantics_by_EA_Nida>; > another from a hard-core mathematician - a category theorist - who > rediscovered this from thin air > <http://www.cs.ox.ac.uk/people/bob.coecke/NewScientist.pdf>. Once you > know what to look for, its freakin everywhere. Say, in biology, the Krebs > cycle <https://en.wikipedia.org/wiki/Citric_acid_cycle> (citric acid > cycle) - some sugar molecules come in, some ATP goes out, and these > chemicals relate to each other not only abstractly as jigsaw-pieces, but > also literally, in that they must have the right shapes > <https://en.wikipedia.org/wiki/Molecular_recognition>! The carbon atom > itself is of this very form: it can connect, by bonds, in very specific > ways. Those bonds, or rather, the possibility of those bonds, can be > imagined as the connecting tabs on jigsaw-puzzle pieces. This is not just > a metaphor, it can also be stated in a very precise mathematical sense. (My > lament: the mathematical abstraction to make this precise puts it out of > reach of most.) > > The key learning task is now transformed into one of discerning the > shapes of these pieces > <https://github.com/opencog/atomspace/blob/master/opencog/sheaf/docs/sheaves.pdf>, > given a mixture of "what is known already" plus "sensory data". The > scientific endeavor is then: "How to do this?" and "How to do this quickly, > efficiently, effectively?" and "How does this relate to other theories, > e.g. neural networks > <https://en.wikipedia.org/wiki/Artificial_neural_network>?" I believe the > answer to the last question is "yes, its related", and I can kind-of > explain how > <https://github.com/opencog/learn/blob/master/learn-lang-diary/skippy.pdf>. > The answer to the first question is "I have a provisional way of doing > this <https://github.com/opencog/learn>, and it seems to work > <https://github.com/opencog/learn/blob/master/learn-lang-diary/connector-sets-revised.pdf>". > The middle question - efficiency? Ooooof. This part is ... unknown. > > There is an adjoint task to learning, and that is expressing and > communicating. Given some knowledge, represented in terms of such jigsaw > pieces, how can it be converted from its abstract form (sitting in RAM, on > the computer disk), into communications: a sequence of words, sentences, or > a drawing, painting? > > That's it. That's the meta-background. At this point, I imagine that you, > dear reader, probably feel no wiser than you did before you started > reading. So what can I say to impart actual wisdom? Well, lets try an argument > from authority <https://en.wikipedia.org/wiki/Argument_from_authority>: a > jigsaw-puzzle piece is an object in an (asymmetric) monoidal category > <https://en.wikipedia.org/wiki/Monoidal_category>. The internal language > of that category is ... a language ... a formal language > <https://en.wikipedia.org/wiki/Formal_language> having a syntax > <https://en.wikipedia.org/wiki/Syntax>. Did that make an impression? > Obviously, languages (the set of all syntactically valid expressions) and > model-theoretic > theories <https://en.wikipedia.org/wiki/Model_theory> are dual to > one-another (this is obvious only if you know model theory). The learning > task is to discover the structure > <https://en.wikipedia.org/wiki/Model_(model_theory)>, the collection of > types <https://en.wikipedia.org/wiki/Type_(model_theory)>, given the > language <https://en.wikipedia.org/wiki/Text_corpus>. There is a wide > abundance of machine-learning software that can do this in narrow, specific > domains. There is no machine learning software that can do this in the > fully generic, fully abstract setting of ... jigsaw puzzle pieces. > > Don't laugh. Reread this blog post from the beginning, and everywhere that > you see "jigsaw piece", think "syntactic, lexical element of a monoidal > category", and everywhere you see "network of everything", think "model > theoretic language". Chew on this for a while, and now think: "Is this > doable? Can this be encoded as software? Is it worthwhile? Might this > actually work?". I hope that you will see the answer to all of these > questions is yes. > > And now for a promised bibliography. The topic both deep and broad. > There's a lot to comprehend, a lot to master, a lot to do. And, ah, I'm > exhausted from writing this; you might be exhausted from reading. A > provisional bibliography can be obtained from two papers I wrote on this > topic: > > - Sheaves: A Topological Approach to Big Data > > <https://github.com/opencog/atomspace/blob/master/opencog/sheaf/docs/sheaves.pdf> > - Neural-Net vs. Symbolic Machine Learning > <https://github.com/opencog/learn/blob/master/learn-lang-diary/skippy.pdf> > > The first paper is rather informal. The second invoked a bunch of math. > Both have bibliographies. There are additional PDF's in each of the > directories that fill in more details. > > This is the level I am currently trying to work at. I invite all > interested parties to come have a science party, and play around and see > how far this stuff can be made to go. > *Linas Vepstas <https://blog.opencog.org/?author=5>* | June 10, 2021 at > 11:55 pm | Categories: Uncategorized > <https://blog.opencog.org/?taxonomy=category&term=uncategorized> | URL: > https://wp.me/p9hhnI-cl > > Comment > <https://blog.opencog.org/2021/06/10/everything-is-a-network/#respond> See > all comments > <https://blog.opencog.org/2021/06/10/everything-is-a-network/#comments> > > Unsubscribe > <https://public-api.wordpress.com/bar/?stat=groovemails-events&bin=wpcom_email_click&redirect_to=https%3A%2F%2Fsubscribe.wordpress.com%2F%3Fkey%3Da9a418716d1b232b4d6f1b1829be75ae%26email%3Dlinasvepstas%2540gmail.com%26b%3DIuFxBBRYbGv3Hprjfcb1nN1xTFHgj5HkuY-x-lT6QKxqkZMgov9AM8QuMvodMCGn32Q3oTFW0et24AFIz1oCUdSZiyQlYOmTn36q6nLoKLLJow%253D%253D&sr=1&signature=674cb680ce749d47971c3d661d17cd4d&user=3747872&_e=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&_z=z> > to no longer receive posts from OpenCog Brainwave. > Change your email settings at Manage Subscriptions > <https://public-api.wordpress.com/bar/?stat=groovemails-events&bin=wpcom_email_click&redirect_to=https%3A%2F%2Fsubscribe.wordpress.com%2F%3Fkey%3Da9a418716d1b232b4d6f1b1829be75ae%26email%3Dlinasvepstas%2540gmail.com&sr=1&signature=ece1c66e405464c8308b765ebceb3f20&user=3747872&_e=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&_z=z>. > > > *Trouble clicking?* Copy and paste this URL into your browser: > https://blog.opencog.org/2021/06/10/everything-is-a-network/ > > > > -- > Patrick: Are they laughing at us? > Sponge Bob: No, Patrick, they are laughing next to us. > > > -- > You received this message because you are subscribed to the Google Groups > "opencog" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To view this discussion on the web visit > https://groups.google.com/d/msgid/opencog/CAHrUA34gQSDZnbkV1Mp7aEOSGoENOkQTAZoBBRVL6%3DwJWfJv%3DA%40mail.gmail.com > <https://groups.google.com/d/msgid/opencog/CAHrUA34gQSDZnbkV1Mp7aEOSGoENOkQTAZoBBRVL6%3DwJWfJv%3DA%40mail.gmail.com?utm_medium=email&utm_source=footer> > . > -- You received this message because you are subscribed to the Google Groups "opencog" group. 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