ᛕᎥᕼᗷᗴᖇᑎᗴ丅Ꭵᑕᔕ @Kihbernetics@qoto.org

>Anthropomorphizing image generators and describing them as merely being “inspired” by their training data, like artists are inspired by other artists, is not only misguided but also harmful. Ascribing #agency to image generators diminishes the complexity of human creativity, robs artists of credit (and in many cases compensation), and transfers #accountability from the organizations creating image generators, and the practices of these organizations which should be scrutinized, to the image generators themselves

https://dl.acm.org/doi/abs/10.1145/3600211.3604681

The premise of this article is solid. The brain evolved first and foremost as a #control mechanism. Symbolic "information processing" is a later development.

However, just from reading the reactions in the comments section, one can easily see that #computationalism is still very much the mainstream theory of mind.

https://aeon.co/essays/your-brain-does-not-process-information-and-it-is-not-a-computer

The 20th century is said to have been the "age of #machines" because all explanations of how things work would end in some kind of computing or "information processing" by a known #mechanism.

Some people think that the 21st century will be the "age of #biology" because science seems starting to look at nature and the living #organism for inspiration about how things really work.

If this is true then #Computationalism must be one of the last remnants of the past century.

>The twenty-first century is the Century of Biology *(Brown, A. The Futurists: September-October 2008)*. Just as the twentieth century looked to machines, the twenty-first century is looking to biology to inform how we think, organize, design, and lead our organizations.

Allen, Kathleen E.. Leading from the Roots: Nature-Inspired Leadership Lessons for Today's World (p. 20). Morgan James Publishing. Kindle Edition.

>"Given that organizations are filled with human beings, it doesn’t take a huge leap of faith to believe that a living system would emerge from all the life that shows up every day"

https://kathleenallen.net/works/

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"

https://www.quantamagazine.org/complexity-theorys-50-year-journey-to-the-limits-of-knowledge-20230817/

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:

https://apps.dtic.mil/sti/tr/pdf/ADA171072.pdf

>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.

#AI #heuristics

https://en.wikipedia.org/wiki/Logic_Theorist

>“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.*

https://bigthink.com/13-8/quantum-mystery-interaction/

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.