Moyo,

My reply was perhaps too short. I can give very precise and exact
descriptions of how all this relates to fractals and tilings ... If you
prod me, I can supply details.  A very important, key bridge  to this is
understanding is the work of  Przemyslaw Prusinkiewicz at Algorithmic
Botany -- http://algorithmicbotany.org/papers/ -- and the easiest way to
understand that is to go in historical order, reading the earliest papers
first. I believe the work there is  nothing short of mind-blowing, and
stunningly important, yet is curiously very under-appreciated, for some
unclear reason.

--linas

On Fri, Jun 11, 2021 at 3:01 PM Linas Vepstas <[email protected]>
wrote:

> Similar ideas have been circulating for decades or longer. Yes, the
> concept of fractals and tilings are similar. My goal here is to point out
> that these ideas can be implemented in software. I'm trying to drum up the
> practical conversation, the one of "how can we do this?" .
>
> On Fri, Jun 11, 2021 at 2:24 AM Tofara Moyo <[email protected]> wrote:
>
>> 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>
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