Non-Operating Systems: Part 3.5
What Do Humans Do or What is a Human or What Properties Does a Human have?
Written December 21, 2018, published June 15, 2023.Alive
Certainly a human is alive. Perhaps a human is a life? What is life?
Andy Pross; “What is Life”; Oxford
Andy Pross; “What is Life”; Oxford
Human Is Greater Than Life
There’s a long tradition of mostly American or European men claiming very clear distinctions of what makes humans distinct from animals, other living things and pretty much everything else in the known universe. They usually go something like this:
“Where, then, is the difference between brute and man? What is it that man can do, and of which we find no signs, no rudiments, in the whole brute world? I answer without hesitation: the one great barrier between the brute and man is Language. Man speaks, and no brute has ever uttered a word. Language is our Rubicon. And no brute will dare cross it.”
- Friedrich Muller, “Lectures on the Science of Language”, 5th ed., pages 391–92
Or in a slightly nicer sounding way.
“The lesson is that language is not something mysterious that is outside the bounds of natural selection, or just popped into being through some mutated gene. But that language is a human invention to solve a human problem. Other creatures can’t use it for the same reason they can’t use a shovel: it was invented by humans, for humans and its success is judged by humans.”
-https://www.theguardian.com/technology/2012/mar/25/daniel-everett-human-language-piraha
or in the reaches of the futurist style.
“Symbolic abstract thinking: Very simply, this is our ability to think about objects, principles, and ideas that are not physically present. It gives us the ability for complex language. This is supported by a lowered larynx (which allows for a wider variety of sounds than all other animals) and brain structures for complex language.
But this trait not only gives us the ability to communicate symbolically, it also allows us to think symbolically, by allowing us to represent all kinds of symbols (including physical and social relationships) in our minds, independent of their presence in the physical world. As a result, internal associations of novel kinds become possible.
In Chu’s words, as a consequence of this “our mind acquires the ability to imagine, to reason, to choose among various motives, and to evaluate alternative plans to actions.” Symbolic abstract thinking has a crucial role to play in allowing us as individuals — and as a species — to be imaginative and solve complex problems.”
Pick up an average natural or science history book and you’ll see the basic idea repeated: A combination of extraordinary brain power combined with uniquely adaptable bodies (hands, walking, skin, etc) have led to humans developing even further intrinsic properties (thinking, building, social behavior etc) of existential uniqueness (usually framed as altogether better).
But really are any of these properties of the body, the functions of the body in the environment and capabilities of the species actually differentiating properties and unique to humans in any meaningful way? Is there something simpler going on? And what about these ways of being is actually identifiably useful or complex? And is anything particularly available only to the human mode of being?
Language?
Humans have been contemplating the capabilities and utility and uniqueness of language since its emergence. (note that it’s absurd to actually denote moment of origin for language, as will be shown. Did it actually just pop into being or did we only notice when self-referential concepts became clear?)
I cannot doubt that language owes its origin to the imitation and modification, aided by signs and gestures, of various natural sounds, the voices of other animals, and man’s own instinctive cries.
— Charles Darwin, 1871. The Descent of Man, and Selection in Relation to Sex
There’s really not a good way to know when and how “language” emerged in humans. Sometime and somewhere between 5 million and 200,000 years ago humans started turning whatever gestures they made into whatever gestures they now recognize as “language.” There’s some evidence that 4.5 million years ago hominids developed anatomy conducive to more modern vocalizations and what not. Assuming that to be evidence of language and something uniquely human requires the assumption that vocalization and hearing are somehow the basis of language. This flies in the face of an ungodly amount of linguistics, philosophy and neurobiological thinking. It’s probably too simple of an assumption to be anything more than a sign-post in the web of confusing things that make up human history.
We can keep doing a narrative account of very old evidence and/or we can fast forward a couple of million years and look at what humans wrote and said once they were reliably writing and saying things.
Consider some of the Babylonian language play.
“The list format invites an element of play. The Babylonian authors explored the potentialities of writing by playing around with words, combining elements based on parallelisms. They used alliteration and analogy and exploited the silhouettes of the written characters. They reduced the word to its simple form, combining it with other words. As Roland Barthes said, this process is akin to writing poetry, and indeed, Babylonian poets explored the connection in full. A typical rhetorical device was enumeration, the use of a sequence of variants. One short poem, for example, lists the varieties of plants that a sheep could graze: camel thorn, winnowed barley, barley ears, and so on. The author enumerates sixteen different plants.”
- “Philosophy Before The Greeks”; Marc Van De Mieroop; Chapter 3 “Constructing Reality”, page 74
The play gives way to less-playful utility in the form of laws. Surely language as law enumeration is uniquely human!
Not so easy, it turns out. Due to the decidedly messy thing that is language and the inability to nail down reality and ill-defined concepts of justice, morality, ethics, or pretty much anything law became and remains a never ending whack-a-mole language game.
“More prominent in laws was the principle of pointillism: every paragraph was but one example in a series of options that together painted a full and nuanced picture. Knowledge was thus cumulative, with each law gaining meaning because of its association with the others surrounding it. Sequences were generated in two ways, either by adding new conditions or by following paradigmatic series. As was the case in lexical and divinatory lists, an entry in a law code could be elaborated further by making it more specific. The agglutinative character of the Sumerian language at the heart of the lexical material naturally allowed for the creation of word-strings. Likewise, in law a basic idea could be made more specific by adding elements.”
- “Philosophy Before The Greeks”; Marc Van De Mieroop; Chapter 7 “The Philosopher King”, page 166
The historical record from then on is full of the overt language games in every category of human expression. Language is the gift of confusion that keeps on giving. The more humans seek to express in these new tools of humanity the more unraveling the ravel becomes.
Which, of course, leads to all sorts of new types of games. The modern game of theory, in particular.
Universal grammar vs Generative grammar
The modern debate is not very old at all. Most readers will know the names Chomsky, Lakoff, Crystal, Pinker and so on — people that are still alive! There are thousands of lesser known linguistic specialists raging debates about whether language is innate or learned, nature or nurtured, and whether there is such thing as a Universal Grammar to which all languages ultimately adhere. Such is the way of science from 1400AD on to find the universal underpinnings of uniquely human things. Hilariously humans have had a pretty pragmatic view of language theory up until the mid 1900s — outside of religious circles — most figured language was how Darwin thought about it. Humans just mimicked the noises and gestures of the world and kept doing that until humans mimicked each others noises etc. Then in the spirit of the machine age Chomsky and others came along to give us the Universal Grammar suggesting human brains are uniquely tied into the minimum structure required to have a language at all.
There are some nice rules of thumb about observed common properties, overall expressive capability, and ways of analyzing usage. These observations are hardly complete and do not encapsulate what language is nor what it is not. So we rage on about whether thoughts are in the brain, what those thoughts might physically be and whether they are encoded in some internal language and so on.
Where exactly lives this complexity of language? Or what is language?
Do not seek some other source for a definition of language, seek within your own ideas.
When was the moment of language for you?
What is language to you? How did you learn language? How did you first use it? How do you keep using it?
For most of us it will have been early and with our mothers. (minute 20 or so: https://www.wnycstudios.org/story/asking-friend)
“Jakobson’s work suggest that your baby has no idea your name is Mama, (or Dada for that matter). Mama doesn’t mean “I love you, sweet angel-woman, sacrificer of sleep, career, and buttock firmness.” It means “food.””
Consider now what it would be like to completely forget a language and learn it anew. https://www.wnycstudios.org/story/91725-words
https://www.wnycstudios.org/story/91728-words-that-change-the-world
It is NOT just the WORDS. Or letters. Or Meaning. Or Connections. Or Context.
It’s being there. All the way there. All of the above and in. Language is a fully connected, fully embodied act of being in and of the world.
First as a map, a messy, but useful map. https://www.npr.org/sections/ed/2014/08/22/341898975/a-picture-of-language-the-fading-art-of-diagramming-sentences
Second, and more importantly, it is a messy, but useful bodily expression.
https://www.newyorker.com/magazine/2019/01/14/greek-to-me
“Particles help make a language a language. They give it currency and connect you to the person you’re speaking with. English is loaded with particles, words and expressions that float up constantly in speech: like, totally, so, you know, O.K., really, actually, honestly, literally, in fact, at least, I mean, quite, of course, after all, hey, sure enough . . . know what I mean? Just sayin’. Some people deplore the extra words as loose and repetitive, and complain that kids today are lazy and inarticulate and are destroying the beauty of the language. But we have relied on such little words since antiquity. Reading Plato’s Apology in my second semester of Elementary Greek, I was amazed at how much nuance these syllables give to Socrates’ speech — they act like nudges, pokes, facial expressions.”
Language is Not Human, Exclusively Human or The Definition of Human.
By now it should be obvious that is not possible for humans to be the only beings with language.
https://www.npr.org/2011/01/20/132650631/new-language-discovered-prairiedogese
Symbolic Thinking?
Scholars and scientists invented an idea of “symbolic thinking” that apparent humans do and no other creature does. This so called symbolic thinking involves using an object, mark or “thing” to stand in for some other object, mark or thing. For example, bones and beads are used to account for the sheep or crops of the towns people. Or words are stand ins for the things they refer to. Then once we establish these semiotic standins we can reason with them and the reasoning we do with the standins is correlating enough that we can consider it applying to the objects themselves.
This is, in essence, the assumed magic of mathematics, theoretical physics, analytic philosophy, linguistics, computer programming, and most political thought. It is not magic and the symbolic thinking isn’t actually “purely symbolic.” The standins actually exist as things unto themselves and the relations between standins and other standins and standins and other things are all actual, real relationships. Rather than offer up a stand-in for others thoughts in the form of this “author’s” original thoughts here is a rich accounting from another author about symbolic thinking:
“John von Neumann, mathematician, physicist, and inventor, took the McCullock-Pitts neuron as a starting point for his cellular automata and self-organizing systems. What matters in this discussion is not how von Neumann managed these remarkable feats but that in order to manage them he, too, had to employ a model that simplified organisms into a form that was more easily manipulated. Like Pitts and McCullock, von Neumann was keenly aware of the distinction between model and living organism. In his 1951 paper, “The General and Logical Theory of Automata,” von Neumann wrote, “The living organisms are very complex — part digital and part analogy mechanisms. The computing machines, at least in their recent forms… are purely digital. Thus I must ask you to accept this oversimplification of the system… I shall consider the living organisms as if they were purely digital automata.” The simulation requires simplification.
It would be foolish to argue against simplification or reduction as a scientific tool. Like a Matisse cutout of a dancer that seems to describe the music of the human body itself, a simplified model may reveal some essential quality of what is being studied. In science and in art, the boiled-down may tell more than an immense, lush, baroque, and more unwieldy description of the same object or story.
[…]
Such reductions serve as vehicles of discovery. On the other hand, some simplifications risk eliminating what matters most. This is the dilemma that faces artist and scientist alike. What to leave in and what to take out? […] the computational neural nets that became so vital to cognitive psychology and to artificial intelligence are treated with far more pessimism among those who are not entirely ignorant of that still mysterious organ: the brain.
[…]
Brooks emphasizes “situatedness” and “embodiment” rather than symbolic representations, a strategy that closely echoes Merleau-Ponty’s phenomenology. […] [Brooks is emphatic] “Real biological systems are not rational agents that take inputs, compute logically, and produce outputs.”
[…]
I vividly remember my lesson in the fifth grade on simple machines: the lever, wheel-axle, screw, pulley, wedge, and inclined plane. Each one was pictured on the filmstrip the class watched. A simple machine was a device that could alter the magnitude or direction of a force. From these machines one could build complex ones. They were machine building blocks. Can the nervous system as a whole be characterized as a machine? Is the placenta a temporary machine? What about the endocrine system? Harvey’s use of the hydraulic system to characterize the working of the heart was unusually effective. The machine allowed him to understand the organ. But is this true of anatomical functions? Isn’t [Damasio, a scientist] right that there is a difference between the living cell and the machinery involved in building a plane or a car?
[…]
There is continual elision at work…
[…]
“Synchrony” is a word used to identify the dynamic and reciprocal physiological and behavioral adaptations that take place between a parent and baby over time. Scientists research gaze, vocal, and affective or emotional synchronies.”
- “A Woman Looking at Men Looking At Women: Essays on Art, Sex, and the Mind”, Siri Hustvedt, Delusions of Certainty, pages 254–255
And so… Language gives way to symbolic thinking which ruptures into something else. Something more primitive? Something more fundamental? Something more human? Less human? More explanatory?
Intelligence and Self-Awareness?
What could possibly be underpinning all the things humans express, all the methods of that expression and all the need to understand all the expression? Surely there must be a basis! Intelligent beings want to know!
Intelligence is really just a historical code word for “complex system” or “complex machinery.” Once humans had codified enough of the symbolic marks and invented enough machinery and calculated enough math they decided there was something called “reason” or “logic” and that these things are the activities of “intelligence.” A humans are the unique beings capable of intelligent thinking, of intelligent doing, and, thus, of intelligence.
20,000 year old bone based accounting. Signs of intelligence?
5000 year old number systems. Signs of intelligence?
Or is all of this just a recursive grouping of more and more. At first humans grouped actual stuff and then we started making symbols for that stuff. Then the symbols grew unwieldy to write or read and so we needed new short cuts. We needed reference, index, logic, reason, method, theory, subtheory, investigation, science, mathematics, psychology!
Though it didn’t start with Descarte he wrote down the most powerful marketing slogan: Cogito Ergo Sum! Existence is Thinking! Not just any thinking, thinking about the self! The highest form of being!
Humans are uniquely made to understand That Which is Human! Intelligence gives birth to its own necessity!
Complex Strategies? Complexity? Strategy? Politics? Economics?
Humans Studying Everything That Is Not Themselves, so they think
Complexity
Information
Contingencies
Memory
Evolution
……..
Zoology
Botany
Physics
Chemistry
Geography
Mathematics
The X-Over
Animals? Life? Cognition?
Neural Networks? Learning? Evolution?
https://www.nature.com/articles/s41593-018-0310-2
Humans Studying Themselves, so they think.
Biology
Cognition
A typical description of some supposedly complex human cognitive task will appeal to all sorts of other supposedly complex human cognitive tasks.
“Because almost all the brain’s core behaviors — consciousness, visual memory, decision-making, language — require object interactions, a deep understanding of object perception will help us gain insight into the entire brain, not just the visual cortex. We are only starting to solve the enigma of the face.”
Scientific American Feb 2019, Doris Y Tsao “Face Values”
Social Strategies
Schedules of Reinforcement
Emotions and Private States
Being
Emotions and Bodies as Maps — https://www.psychologytoday.com/us/blog/the-athletes-way/201401/researchers-map-body-areas-linked-specific-emotions
A very european / american male oriented map of the evolution of humans understanding humans — a mind map found at http://skat.ihmc.us/
A whole bunch of maps of maps can be found here: http://skat.ihmc.us/
This comes from the CMAP work out of IHMC https://www.ihmc.us/research/modeling-sharing-simulation/
Geographic Psychology
https://journals.sagepub.com/doi/abs/10.1177/0963721416658446?journalCode=cdpa
Humanity Conceived Convolving Into Itself
Art?
“Art is the Queen of all sciences communicating knowledge to all the generations of the world”
- Leonardo da Vinci as quoted in “Art and Physics” by Leonard Shlain
Making Marks
Narration and Stories
Signs and Reminders
Simulations and Consequences
Society or Studies with More Than One Human
Perhaps humanity can’t be found in a single human.
Freedom and liberty
Society and Civilization
Politics
Economics
Culture
Communications and Media
Branding and Marketing
Business and Corporations
Labor, Work, Careers
Communities, Neighborhoods, Cities, States, Countries
Identification and Identity
But What About All This is Human?
All that we have claimed is uniquely human and is somehow above the underlying reality has clearly been shown to not be. And yet humans and most things aren’t actually mechanical. How can this be? If humans mostly do mechanical things… then how in the world are they not just machines? How is anything not just a machine?
Because machines are not just machines.
Dueling Duality of Reality
Infinite Production
The mechanical seems like its infinite would be enough to get to the rest of infinity. Rilke, Frege, Lovelace, B. Russell, J. Robinson, E Noether, Whitehead, Hilbert, Lovelace, Godel, Turing, Wolfram, Conway, Newton, Leibniz, Chomsky, Euler, Chaitin, Poincare…. 10000s of others have tried to nail the mechanical down in all its forms — how we name, how we count, what is countable, what is a number, what is a name, what is a language, what is a grammar, what is constructible, what is a construction, what is a program, what is computation, what is computable, what is infinite, what is a set, what is an object, what is anything at all? This list of questions and thinkers/researchers is just from the mathematics and computer science aspects. If we include the continental philosophers, chemists, biologists, art theory folks, social scientists, we aren’t going to get any closer to resolution on any of these big questions. In fact, we get an explosion of the possibility space of what everything could be, and thus what it is.
That is the big point — and the big challenge — of the mechanical. The race to mechanize in theory and mechanize in physical implement has done what reality does… more “stuff” is generated, and seemingly more quickly. The mechanical is part of the wider reality and as such cannot avoid being generative even in its design to be deterministic and only work on deterministic things of the world. And because the mechanical is generative it cannot be controlled, which is really the assumption of operating system.
INTERLUDE of LOOPS
https://www.wnycstudios.org/story/radiolab-loops
Unavoidable Generative Proof
The proof of the generative behavioral reality of all systems, mechanical or otherwise, is everywhere. Mathematical incompleteness, computability, bio-diversity, thermodynamic entropy, information theory, ecological flow, chaotic effects, incommensurability of prime numbers, linguistic evolution, quantum uncertainty and more. To be clear a generative basis of reality is not saying there aren’t deterministic aspects or wholly containable patterns or facts of the world. Quite the opposite! And even stating a single fact of the world generates more facts about the world.
Start with a point, any point will do
Consider a single point, in the most abstract sense. A zero-dimensional point, a bit with only one state. A 1. What exactly is that? How can you observe or reason or come to know what exactly is the extent of a point? What reference can you make to the point? Can a point exist with no reference? What exactly is zero-dimensions? And so on.
The questions about the point are yet generative aspects of the point, even without answers.
If we exit the philosophical abstraction and return to pragmatics of physical reality and operating systems and computers and instead just assume we have some reasonable, common sense notion of a point… how do we encode that point, represent that point, imbue that point in a computer?
We typically flip some bit in the circuitry (the register, etc), make note of where that bit is (we need a “location” of that point), and we don’t let anything else overtake that point… and so on. Even our most abstract notions with no physical correlate require a huge infrastructure of physical computation to do anything with. And each of those physical computer implements require their own set of references. E.g. you can’t turn the power off on the computer or you’ll lose that notion of the point in the register.
Surreal numbers visualized
For a more advanced, yet more shocking example because it’s totally realizable in a straightforward way, consider the Surreal Numbers, a set of numbers even more infinite than the real numbers, already a very big infinity.
(explainer and wonderful set of code https://github.com/mroughan/SurrealNumbers.jl )
The most we can say about small aspects of reality is that if we ignore all the contingent things that aren’t that tiny aspect of reality we can reason about it in some deterministic (mechanical) way. There are two main mechanical/control ideas that drive how we have engineered our way into a mechanical world. Mathematics tends to assume we don’t need all those contingent things to be useful with some small aspect, we can just build a universe of small, independent aspects and reason about them. Statistics takes a different approach by using large numbers of small things to make a case that any of the contingent things that don’t affect most of the small abstract things can generally be ignored. Computers and Computing Theory uses both Mathematical and Statistical accounts of things. All of our control systems, our operating systems, use these two approaches. All the contingent stuff is “ignored”.
Until it isn’t.
All computers break. All programs bug out. All technology stops working. All systems erode. All ecologies break down.
https://www.youtube.com/watch?v=1Xruds0J86I
Unless they are perturbed or shaped by other systems that kick them back into service. But those outside systems to must be kicked back into service as well. None of this is free or abstracted away from the contingent states of everything up and down the casual chain.
So at what point should we abandon the notion of operating and system when we can clearly see that it is operating system of operating system of operating system of operating system…. To infinity? Where exactly is the operating and where is the system?
For those that prefer a pragmatic rejoinder… “yeah, but we all know what we mean. It’s a reasonable assumption that some operating systems are good enough.”
No. beyond all reasonable assumptions we can prove with 100% certainty that there are infinite set of systems that cannot be contained. Mathematical Incompleteness (https://www.youtube.com/watch?v=O4ndIDcDSGc ) and the halting problem in computer science (a simple primer here: http://www.cgl.uwaterloo.ca/csk/halt/ ) and so on guarantee we will encounter programs that we cannot predict what they do, we cannot create systems that we can prove every single fact out. Those well versed in these theories will complain, as they do, that this is a misinterpretation of these theories and that they do not apply but to a small set of theoretical constructs (not a true nor provable statement, but a wishful thought). These abstract systems are the simplest, most non-contingent systems we have in the universe and yet they cannot be predicted with certainty. Thus they cannot be controlled. Thus they cannot be operated with certainty. They can only generate.
The challenges only get more acute for prediction, control and operating as you move into more complex systems of physical reality. Everything is always generating more everything.
We Knew This, Though, Didn’t We
We haven’t always been so clear in our thinking, but we have always sensed what was going on. As such we’ve been on a 50,000+ year roller coaster of trying to codify all this generated “stuff” only to have all the codification grow overwhelming so we have to codify the codifying.
Wider view of operating history. http://userwww.sfsu.edu/fielden/hist.htm
Re-Consider the Computer, Again.
The computer is the nexus of all that we think a human is, form our mythology and pop science. Everything cultural and pop-scientifically viewed as human has been reduced into the computer.
The assumptions of predictability and control.
The assumptions of possibility of mind and thinking.
The top down design assumptions.
The utility driven purpose.
The obsession with growth.
The operating of a reality we think we can understand.
The central processing unit.
The long, short term memory.
The humanity of modern western technocratic society is mechanical and thus so are its tools. The humanity elucidated by science and art and ecological reality is biological, complex, messy, non mechanical.
Consider the curious case of what’s considered Good Software Design Principles:
https://en.wikipedia.org/wiki/SOLID
https://en.wikipedia.org/wiki/GRASP_(object-oriented_design)
Consider now why these are the principles:
Control
[INSERT THOUGHTS HERE]
What is Data?
https://www.thenewatlantis.com/publications/why-data-is-never-raw
The Space of All Possible Computers and Things To Compute
- What would this space be? What can and can’t compute? What is and is not computable?
- Is randomness computable? Is what sense could it be and not be? How does randomness “happen”?
- Is there a cardinality of randomness? Much like cardinality of infinity?
- E.g are the primes more random than the digits of pi than eCA 110 behavior? Quantum flux?
- Why must their be programs at all?
- Can a program be random? What would a random program do? What kind of computer would it run on?
- Is random the only way to get to everything that explicit programs/procedures can’t get to?
- Is it more efficient to just be random? That is, there is a cost to building a program, so even if it eventually is efficient its construction must be accounted for
- Randomness is the cheapest Universal/Infinity to create. It’s condition is CHEAP to FULFILL. It doesn’t need to be consistent or carry on with anything or generate anything in particular.
- Doing aggregations by sub structures within the randomness gives you all the other structure. (take the nxn grid and just select a chunk… then use that chunk to aggregate…) wtf is aggregation?
- There’s no
Generating What’s Next
And so we’ve come to the point where operating must be abandoned and generating must be taken as the root function, the only function, of all systems.
What happens to our worldview, our ontology, and our technology look if we consider everything as generative and not in the least reductive to a control scheme? What if we focused on the generative and not trying to contain, control, command, cajole, determine, focus, refine, optimize? What if all those things we get as side effects of focusing on and enhancing generative systems?
How else could reality be working if not in a deterministic, knowable, predictable way? And if it is unknowable and not predictable and not deterministic then what is the point, what should we get done and how can we get anything done at all?
Image of natural “mechanical gear” evolution https://www.popularmechanics.com/science/animals/a9449/the-first-gear-discovered-in-nature-15916433/
How Else Could Reality Be If Not Organized?
It could be random. Or so unknowable that it might as well be considered random.
It’s a reasonable enough idea. If everything is always generating more everything what exactly could be the cause or the explanation of that? What sort of thing is the universe that just keeps on producing more of itself?
Randomness is the only thing that could keep creating more of something like itself and yet not just reduce to itself.
And yet that doesn’t mean there is no such thing as pattern. There can be pattern contained within randomness. We know this rather commonsensically. Gambler’s fallacy, small segment of the digits of pi, Brownian motion, quantum mechanics.
So it’s not completely crazy to think randomness could be the base and that there is still the possibility of finding and using patterns. And even having a generalized way of going about this.
Why would a generalized way of pattern jumping matter? Because if everything is always making more stuff there’s no particular value in having a particular method of pattern making, pattern matching, pattern mapping… as there’s always going to be new patterns to pattern. So a more general thing must be at play or could be at play.
What would a property of such a general apparatus be?
- It should be efficient in implementation/description. Describable or programmable at any level.
- It should effective at all levels of reality (actually do work)
- It should be effective between all levels of reality (do work between levels)
- Not assume any pattern. Make no objective or absolute reference to a pattern or assumption
Enumerating All The Cases
As a thought experiment is worth consider the one for sure way of understanding everything in the universe — just generate all of it. Obviously that’s too big of a thing to do but we can certainly try some small problems using this method. Ultimately why would we do this? To see if there are methods other than enumerating all the cases that will give us everything. That is, is there a theory of the everything that is smaller than the everything?
Consider a n x n grid of pixels, with only black or white pixels. We can generate all configurations of this grid just by listing out all the matrices.
Here’s a plot of all the 2x2 grids all put together in a 4x4 grid of 2x2 grids.
Now is there some other “theory” we can come up that gets to all of this and all that it could be without just listing it all out?
Aggregate them using different methods
- Randomly generate 2x2 images
- Regular geometric ideas
- Algorithmic generation schemes (ECAs)
- And so on…
This is effectively the procedure of mechanical learning (machine learning, “AI”). Mechanical learning methods (and the wider statistical science) attempt to find generative methods to enumerate all the possibilities. There is a tacit assumption that the full possibility space can be compressed. But this cannot be the case. We just saw why in the above.
The space of possibilities of the grids of nxn black or white pixels is effectively just the integers between 1 and 2^(nxn).
Here’s a collage of 3x3 grid.
Universe Thought Experiement
the universe might fundamentally just be random and you can get order from randomness simply interacting (in the most simple ways, simple logical operations) with randomness.
In this demonstration I generate 2 random sequences and then perform a logical OR and a logical AND. When we apply an OR between the random sequences we get more entropy. When we apply an AND we get far less entropy (more “order”).
The code here is mathematica/wolframlang. It does the random sequence generation and a randomness statistical test (the closer to zero the number next to the images the more random the sequence). Somewhere I have a video allowing you to look at this frame by frame with random sequences of growing sizes. Imagine it. and then You can thus imagine how much order might arise at the size of the universe or even a single galaxy.
Mirroring/Mimicry
1 mirrors the 1
Empathy
Consistent Togetherness
Empathy is a deeper sharing of context, timing, flow, values, and experience. Empathy is not a transactional input/output goal or utility maximization. Empathy is companionship and trust. Empathy is consistent thereness.
Poetic Interlude
Woody, old floor cracks under foot
Dragged to the kitchen by same old forces
Autumn’s morning light slinks through the windows
Click click tick tick whoosh ssssshhhhhhh
Burners on and the water snaps to attention
Creatures gotta eat and yumans gotta wake
Morning mission, same as it ever was
Tea pots crackle awaiting their tip
Old mugs with old love stands intentioned
Soon to receive the caffeinated ignition
Complex Equality
The operating system must have at least as much complexity as the system it operates.
The generating system is the only such system. It generates complexity through adaptation.
The stasis is maintained so long as the generating systems maintain complexity parity.
Signals into signals. Language is not enough. It does not have enough generating ability.
System of Systems
Measuring The System Health
We measure complexity/depth of engagement through traditional engagement metrics (frequency, etc) as directional indicators and our metrics are about the complexity of the situations you are using it in and working through. Because our technology is about the evolution of signal processing and evolution of signals we can produce we measure the complexity of the system capability similar to how power systems are measure in terms of capacity and utilization combined with sustainable ecosystem measurements.
Specifically, our main platform goal is always be processing signal, finding new signals to process and new methods to process. Those concepts can be measured by:
- total raw data coming into the platform (audio, visual, linguistic, gesture, meta data, geospatial info, etc)
- % of raw data converted to signal through existing signal interpreters (which also gives us a % of “noise” or signal we haven’t been able to parse)
- % of signal that is compressible (meaning it’s the same signal or similar signal… e.g. some talks about the same stuff with same intensity in same location…) (this also gives us % of signal that is novel/new)
- load factor (% of platform computing resources utilized over given time frame. We have an elastic platform that will auto scale up and down if it’s reaching capacity or will ramp down if it’s way under utilized. But we know our system is healthily complexifying when it scales up but doesn’t scale down. That means it’s growing, but sustainably. CPU/Memory/Disk are effectively thermodynamic entities. If they are all peaking out it means a huge chunk of data is coming in and a good chunk of that data is new… not compressible. We want that ebb and flow and avoid huge spikes and crashes.)
- survivability factor (for the system at large and individual signal processing lines we want the load factor to be consistent at increasing scale factors AND we want any signal processing technique to be growing in utility to the system or being removed if it no longer is getting a high enough % of new signal. Survivability is a combination of raw uptime + utilization of each signal processing pipeline (and per user, per partner, etc))
The long and short of it is that we view what we’re doing in an ecological systems way and our focus is on sustainable development. We don’t get too fussy about the details and the system itself balances itself and tells us which signals to find more of and which to stop getting on a per user, per partner, per deployment, per data pipeline etc basis.
This is part 3.5 of an ongoing series. part 2, here, now. or skip right to part 1. Part 4, 4.5 years after parts 1–3, AND 3.5. the non-operating system has arrived.
