Finding out the complexity of complexity is a really complex problem😀
Fortunately, the things that we think are really complex to compute don't know (or care about) how complex they are.
>"Complexity theorists are confronting their most puzzling problem yet: complexity theory itself"
Same source:
>Self-organizing systems are characterized by their intrinsic, nonlinear operators, (i.e., the properties of their constituent elements, macromolecules, spores of the slime mold, bees, etc.), which generate macroscopically (meta-) stable patterns maintained by the perpetual flux of their constituents. A special case of #self_organization is #autopoiesis. It is that organization which is its own Eigen-state: *the outcome of the productive interactions of the components of the system are those very components*. It is the organization of the #living, and, at the same time, the organization of #autonomy.
>The #purpose of invoking the notion of "purpose" is to emphasize the irrelevance of the #trajectories traced by such a system en route from an arbitrary initial #state to its #goal. In a synthesized system whose inner workings are known, this irrelevance has no significance. This irrelevance becomes highly significant, however when the analytic problem, the machine identification problem, cannot be solved, because, for instance, it is ***trans-computational*** in the sense that with known algorithms the number of elementary computations exceeds the age of the universe expressed in nanoseconds.
From #H_v_Foerster's definition of CYBERNETICS in the *Encyclopedia of Artificial Intelligence*, Wiley, 1987, as presented in:
Excellent piece in Tech Review on on evaluation issues for LLMs.
>At the heart of #TESCREAL-ism is a techno-utopian vision of the future in which we become a new species of “enhanced” posthumans, colonize space, subjugate nature, plunder the cosmos for its vast resources and build giant computers floating in space to run virtual-reality simulations in which trillions and trillions of “happy” digital beings live. The ultimate aim is to maximize the total amount of “value” in the universe.
https://www.truthdig.com/articles/before-its-too-late-buddy/
>#Logic_Theorist is a computer program written in 1956 by Allen Newell, Herbert A. Simon, and Cliff Shaw. It was the first program deliberately engineered to perform automated reasoning, and has been described as ***the first artificial intelligence program***.
Logic Theorist proved 38 of the first 52 theorems in chapter two of Whitehead and Russell's *Principia Mathematica*, and found new and shorter proofs for some of them.
>“Living in the world we live in, in which Elon runs this company and it is a private business under his control, we are living off his good graces,” a Pentagon official told me. “That sucks.”
https://www.newyorker.com/magazine/2023/08/28/elon-musks-shadow-rule
>It is the self-organizing and self-assembling properties of *inanimate* molecules that make the origin problem much less difficult. The mystery probably does not have a complete solution, but amazing partial solutions have been discovered in my lifetime. Most promising are:
(1) the rich *abiogenic* syntheses of amino acids, nucleobases, and many other biomolecules, and
(2) the *spontaneous* folding and assembly of linear copolymers of amino acids and nucleotides to form enzymes and higher order molecular structures.
>The origin of the genetic code and genetic language is still the greatest mystery, but whatever the answer, it is a fact that for over 4 billion years of evolution linear copolymer folding has remained the essential symbol grounding process that enables *1-dimensional genetic symbolic descriptions* to construct and control the *3-dimensional molecular machinery of all life*. These folded macromolecules also have remained as the *initial detectors of sensory information* at all levels of evolution. Any biosemiotic studies should understand these universal ***molecular symbol-matter and matter-symbol transitions***.
https://www.academia.edu/44551141/The_Primary_Biosemiosis_Symbol_Sequence_Grounding_by_Folding
Talking about the "nervous system" in isolation from the rest of the body and its environment is like trying to figure out what is the automobile about by looking at its motor running on a stand. You need to look at where the "rubber hits the road" to really understand what's going on.
Only "bodies can do things" not the isolated nervous systems. Of course, brains have a #function within the body, the same way that individual bodies (with their respective brains) have a function within a social organization.
The nervous "(sub)system" has no need to be in direct contact with the environment, but it also can't function or even survive (is not viable) without the support of all the other body parts, some of which *are* and *must* be in direct contact with the #environment.
According to #HH_Pattee, The whole purpose of the nervous system and cognition is the survival of the body:
>"The major function of the brain is actually not to sit around and discuss things like we are doing now, but it is to make decisions - it has to decide whether to fight or run or eat ..."
https://www.academia.edu/6576779/Rosen_and_Pattee_on_Theoretical_Biology
Thinking about the nervous "system" in isolation is typical for #Cybernetics thinking which separates the #control (management) system from the #controlled system (the plant) and does not recognize the fact that they depend on each other and should be thought of as one system.
People are "social animals" and the emergent capability and knowledge of an #organization as a system of people are quite different from the collection of all the individual learning capabilities and knowledge of the individuals it is composed of, so it is, therefore, appropriate to treat the organization as another dynamical #learning system.
Unless I see evidence that someone else already introduced it in similar terms, I will claim here that I've come up with (yet another😀) theory of consciousness I will aptly name a "Kihbernetic Theory of #Consciousness" or "#KTC".
According to this theory #regulation in a dynamical system is always #unconscious (innate or learned, like driving a bike), #guidance is always #conscious (i.e. it assumes there is #intention, teleology, conscious seeking for answers), and #control can be either conscious or unconscious.
For example, one can be focused on (have conscious control over) a conversation while unconsciously controlling a vehicle they are driving, and then momentarily switch their #attention to some unexpected situation on the road that the regulators were unable to resolve by themselves.
Reading about different theories of #consciousness while trying to understand why it is such a "hard problem" and just had an idea. I'm sure someone has already thought of it, but here it is just in case:
Both #unconscious and #conscious neural activity are carried on by the same neural network mechanisms. The only difference between the two is that unconscious activities happen in #parallel, like a wave, while all conscious activity is performed in a #sequence.
We can experience multiple things and are able to perform multiple **unconscious** activities at the same time, but we can focus our conscious #attention only on *one thing at a time*. This is why we are flipping between two different views of the Necker cube and we can't *see* both versions at the same time.
Both conscious and unconscious activity affects the current #state of the neural network (our thoughts), but only conscious thoughts can be memorized and later recalled because they are "recorded" as sequences of events (stories).
This is totally different from computer #memory which mainly stores and retrieves separate, out-of-context data points.
Introducing the *qualitative* category of #Wisdom in the triad made of *quantifiable* #Data, #Information, and #Knowledge items adds nothing to the better understanding of the matter.
Saying someone or something is "wise" is just a subjective judgment made by an external #observer about another (#observed) system's behavior *appropriateness* to the given situation in the environment without knowing anything about the observed system's internal state, goals, or motives.
In addition, a really "wise" entity would never identify itself as such.😀
>"The mathematics of quantum mechanics works incredibly well as a predictor of what this data should be. It will not give you certainty, but it will give you reliable probabilistic predictions."
It is definitely **not** a *mystery*. #Quantum #Constructivism is just like every other #Science, which will also *not give you certainty, but it will give you reliable probabilistic predictions.*
The #control functions in a dynamical system such as a living organism are distributed on three levels:
1️⃣ The automated and predominantly *unconscious* #regulatory functions are responsible for any *immediate* response and maintaining the system's *homeostasis* in the face of external disturbances.
2️⃣ The working parameters for these "regulators" are changed based on actions planned, directed, and modulated by the *conscious* #control functions seeking to optimize the use of the regulators and fulfill "high-level" goals, aspirations, and other *needs* that originate on
3️⃣ The "highest"#governance control level which maintains *long-term* drives that the system may be either aware (conscious) of (voluntary), or deeply ingrained in some unconscious habits, or innate.
It is evident from this short presentation that #consciousness resides primarily on the *middle* control level that has the ability to make *predictions* of future events and compare such expectations with the *perception* of reality as provided by the regulators. All in order to extract the *difference* between the two, or the #information that will be subsequently *integrated* into the #knowledge structure of the system to improve control.
Many people think that "history doesn't repeat itself" so they dislike #models because "they are based on the #past and thus not useful for identifying all the #complexity associated with the #changes that will happen in the #future."
This is most likely because they think models are for producing #forecasts, while the best use of models is, instead, to plan future #experiments.
#Predictions are often made as statements about what **will** occur in the future, while they should be only statements about #expectations of what **may** most probably happen in the (most immediate) future.
Predictions based on historical data define the boundary of the narrow conical "#possibility_space", the "volume" of which rapidly increases with longer prediction times.
idem ...
>The concept of #language was modeled more and more after those emerging from interactions with computers, the "computer languages." It is clear that the syntax of these languages must be obeyed meticulously, otherwise ''garbage in - garbage out."
>Unfortunately, under the leadership of one of the foremost linguists in America, Noam Chomsky, the logico-mathematical principle of fulfilling rigorous #syntactic requirements in so-called "well-formed formulae" was transplanted into the domain of natural languages and became a criterion for "linguistic competence." This aspect of language ignores the essential role as a means of #communication and perceives language as an end in itself. It is in this castrated form that one believes language is "linear," that questions have unique answers, that the linguistic problem is to generate "well-formed sentences," and other misconceptions that have their roots in perceiving language as a monologue.
#HvFoerster on the origins of #computational_neuroscience
>For reasons that still baffle me, it was the pragmatic American engineers and scientists, not the romantic Europeans, who began to toss anthropomorphic sand into the gear box of evolving notions and ideas. To name two such cases, the computer people began to talk about a machine's storage system as if it were a computer's #memory, and the communication engineers began to talk about signals as if they were #information.
>Perhaps these were the precursors for the second derailment which, ironically, was the inverse of the first. lt worked as follows. The first phase was #anthropomorphization: mental functions projected into machines. However, we knew how these machines worked because we built them and wrote the programs. Consequently, an appropriate "#mechanomorphization," the concepts dealing with computer hard- and software were projected back into the workings of the brain and, presto!, we knew how the #mind worked.
*Heinz Von Foerster
*To know and to let know - an applied theory of knowledge*
Canadian Library Journal, Vol. 39, No. 5, October l982.*
Some pretty obvious and standard things are described here as novelties:
Basically:
1️⃣ #Leaders exist because they have #Followers,
2️⃣ Leaders are here to provide #Guidance within the #Regulation and #Control "hierarchy"
3️⃣ All #Measurement is personal and beneficial only to the measurer
4️⃣ The "hated" #Matrix organizations are connecting silos
5️⃣ Removing what is #NOT determines what IS #Possibile (the cone of possibilities)
6️⃣ Groups of people will form #Living systems that cannot be engineered but can only be maintained as a "garden"
7️⃣ #Negative feedback is the only useful #feedback. A "pat on the back" never helped anyone. 😀
Also, this is all set backward:
> How can we ensure there are useful leadership Artefacts (tools, processes) and leadership Heuristics (rules of thumb, ways of doing things) - as well as Skills, Experiences and (of course) Natural talent.
I understand that as a consultant you have the "urge" to sell something tangible (a product) but you always have to start with the #People and their experience, skills, and natural talent, then identify the #Processes, what they do, and how are they connected (collaborate). Selecting the #Products (tools, SW, forms, space, facilities) that will support that is the last and the least important thing.
Btw, a process is not an #artifact (product). Only the "rule book" that describes the process is, and there is a huge difference between the two.
Another very interesting passage showing a deep understanding and appreciation of Pattee's work:
>This is how the two processes that necessitate symbolic description connect in Pattee’s theory: **the biological function of #measurement is to #control**. Living organisms are able to form many kinds of such measurement–control networks. The outcome of the measurement process may feed directly into the control network (as in tropisms). But, according to Pattee, for control to be displaced in space and time (e.g., delayed with respect to measurement), a symbolic coding must exist. **The two processes of measurement and control fulfill the difficult ‘‘cementing’’ role between the #symbolic and the #dynamic, the discrete and the continuous, the static and the time-dependent**. Even though the measurement process may be a dynamical one, its function, according to Pattee, cannot usefully be described by the same dynamics it is measuring. It is only in this sense that dynamics and symbols (the informational record of a measurement) are irreconcilable’’ or complementary
Not all #control in real #life requires an immediate response of the dynamical #system to what's happening in its environment.
>Constraining the behavior of a system in a functional way, i.e., control, can be exerted here and now—by the specific parameters of environments. Such is the case of tropisms: a plant turns in the direction of a light. However, in cases in which control is displaced in time, the functional ‘‘freezing’’ of some degrees of freedom has to be written somehow and somewhere (i.e., some form of memory must occur), and—if one wants to have a physical description of this memory process—according to Pattee, one has to employ an alternative sort of description in the form of time-independent constraints. This description is "symbolic’’ in the sense of consisting of timeless structures, having external significance, that—themselves—form a system, being non-arbitrarily linked together by certain rules. According to Pattee, the two kinds of description (symbolic code and physical laws) are incommensurate. Neither is reducible to the other.
J. Rączaszek-Leonardi building on #HH_Pattee's work on Reconciling #symbolic and #dynamic aspects of #language
#Kihbernetics is the study of #Complex #Dynamical #Systems with #Memory which is quite different from other #SystemsThinking approaches. Kihbernetic theory and principles are derived primarily from these three sources:
1️⃣ #CE_Shannon's theory of #Information and his description of a #Transducer,
2️⃣ #WR_Ashby's #Cybernetics and his concept of #Transformation, and
3️⃣ #HR_Maturana's theory of #Autopoiesis and the resulting #Constructivism
Although equally applicable to any dynamical system with memory (mechanisms, organisms, or organizations) the Kihbernetic worldview originated from my helping navigate organizations through times of #change.