ᛕᎥᕼᗷᗴᖇᑎᗴ丅Ꭵᑕᔕ @Kihbernetics@qoto.org

Most theories of #consciousness start with #information while a proper way to address any neural theory of consciousness should be as a #control science because the primary function of the nervous system is not to process information but to control the body.

Most control is internal to the system, a distributed, analog, homeostatic unconscious #regulation 1️⃣ of essential internal variables that are keeping the body alive and well. None of the mechanisms on this level “cares” about what is happening outside of the body.

Only on the next level do we find the kind of information necessary for the rate-dependent negative #feedback_control mechanisms 2️⃣ keeping some external controlled variables within limits engaging (through the use of regulators) in performing whole-body actions (behavior) in the immediate environment. Those actions can be conducted either consciously or unconsciously.

Finally, on the highest level, we have the rate-independent, open loop always conscious #governance 3️⃣ maintaining the long-term goals and providing stability and direction to the lower level of control that will plan, implement, and track the fulfillment of those goals.

Terrence W. Deacon writes beautifully about this conundrum:

https://royalsocietypublishing.org/doi/10.1098/rsta.2022.0282#d7508913e1

I have a somewhat different position on his second statement, however.

I think there is a self that determines how the system responds to an external perturbation.

A system doesn’t feed on #order (or #negentropy) from the environment it has to create it.

You can’t get your desk organized by just acquiring some order from the environment. You have to do some #work and use some of your #free_energy. Schrödinger admits as much:

1️⃣ Kihbernetic #System with
2️⃣ fundamental #Processes: a recursive #Autopoietic self-production for growth and learning, and a linear #Allopoietic production of “other things”, such as behavior and waste, distributed in
3️⃣ Control #Levels, of #Regulation, immersed in, and dealing with things in the system’s environment, #Control for managing the workload of different regulators, and #Guidance to provide long-term goals and preserve the identity of the system, all using
4️⃣ #Variables: sensory #Input of data and other resources, motor #Output of behavior, #Information as the difference that will make a difference in the subsequent (updated) #Knowledge state, all interconnecting
5️⃣ #Functions: the #Control-ed #Reaction to external stimuli, the #Perception of sensory states, the #Prediction of the expected outcome of past behavior, and the repeated #Integration of new information into an updated knowledge state.

A 3D #space of a natural #system can be visualized as created from the interaction of 2D #planes made of #matter and of #energy that modulate (change the form of or constrain) each other, leaving a 1D #information #sequence as a record of how their interaction unfolds in #time.

All #learning must be open-ended. The learning agent (the #observer) must have the #autonomy to set its own learning goals as well as plan and execute a #sequence of #exploration activities to achieve these goals.

One can never learn all existing data but rather refine their understanding of the data that is available to them. As true for human intelligence, you can either have “deep and narrow” specialized #AI agents or “average and broad” #AGI. You can’t have both in the same entity. Time and #memory “limitation” are the main inspirations for #diversity and #cooperation between learning agents.

People should have figured it out by now that the #distribution of processing power, not the #centralization in gargantuan data and control centers is the right thing to do.

Stop working on LLMs (Large Language Models) and start working on PCAs (Personal Customizable Assistants).

From: https://arxiv.org/pdf/2311.00344.pdf

I read a sample of Robert M. Sapolsky’s new book Determined: A Science of Life without Free Will on Amazon, and I really don’t see why some people find it “revolutionary”. I find it full of half-baked contradictory claims that don’t hold water even under quick superficial scrutiny like this.

Brains don’t generate behaviors. They #produce motor responses to sensory stimuli that an outside observer then interprets as the behavior of the observed individual in their immediate environment. The observer can also stick electrodes in the brain of the individual and then correlate the observed behavior with the measurements performed on some of the neurons and then conclude that those firings have caused the behavior. However, even if it was possible to replicate the exact sequence of the observed firings of all the neurons, the observed behavior would be different if the “response” of the environment was also not exactly the same as during the measurement.

Determinism alone doesn’t “cause” anything even if there are no such things as “causeless causes”. The current #state of the system is obviously determined by its previous state and the current sensory inputs, so there are at least two separate “determinisms” in play here all the time, and, as an individual existing in its particular environment, I have at least some #control over the unfolding of both, my biology (eating, drinking coffee), and my environment (writing this nonsense)😉.

I wish people who are coming up each day with a new “breakthrough” theory using physics and/or quantum mechanics to explain everything from complexity and life to consciousness and free will, would read first what #M_Polanyi has said about it.
This is from:
http://www.polanyisociety.org/MP-On–the-Modern-Mind-1965-ocr.pdf

This is so wrong I wouldn’t know where to start.

Hurricanes are ‘selected’ based on their ability to perform functions dictated by the environment, the researchers found.

Gimme a break!🤨

A dynamical system with #memory with the ability to learn and adapt to its environment or to change it will need at most these three #control mechanisms:

1️⃣ The #internal immediate control (#regulation) of state variables essential for preserving the stability or #homeostasis of the system. This is a simple #reaction of the system to a perturbance, like, for example, sweating when the core temperature of the body increases beyond some preset margin.

2️⃣ The #proximal control of the surrounding environment is used when 1️⃣ is overwhelmed and there is a need for the coordinated engagement of different lower-level regulators, the #measurement (tracking), and negative #feedback control of multiple time-dependent variables like for example, when taking off layers of clothes, moving the body into a shade, or taking a cold shower until the temperature gets again within limits.

3️⃣ The #distal, long-term, open-loop control with delayed feedback is the highest form of control, like for example when building a house with an HVAC system that will remove the necessity for a continuous employment of proximal control (2️⃣) by creating a private controlled environment.

All #living systems feature this 3-layered control architecture, with the only difference being in what degree the activities on each level are the result of #conscious deliberation as opposed to a natural, innate behavior.

People often “blame” Shannon’s theory of #communication for completely ignoring #meaning, maybe also because Shannon himself stated that “the semantic aspects of communication are irrelevant to the engineering aspects“😀

However, if one recognizes that the #information content as defined by the #entropy is the measure of #uncertainty in a receiver about the sender’s #state when producing the message, can it perhaps be interpreted that the receiver is trying to #understand what the sender was #meaning to send?

The information the sender encodes in the message is never the same as that the receiver decodes from it on the other side of the channel.

Below is Shannon’s description of the standard #transducer used for encoding and decoding the information in messages. The block diagrams are my rendering of the description (F is a “#memory” function):

The purpose of #design is to #create new or/and different #artificial structures (artifacts), so speaking of design makes sense only if it is in the context of other creative #production activities such as writing, painting, engineering, manufacturing, etc.

Klaus Krippendorff has a nice description of the difference between #object and #artifact and the relationship to Gibson’s #affordances in this 2007 paper published in “Kybernetes”:

https://researchgate.net/publication/45597493_The_Cybernetics_of_Design_and_the_Design_of_Cybernetic

However, he is wrong, IMO, in accentuating the difference between #scientists and #designers.

Every designer is often a scientist in “describing what can be observed” and every scientist also has to design new hypotheses, theories, and experiments for the “not yet observable and measurable”.

#WR_Ashby in his “Design for a Brain” writes about the importance of the #preservation of #adaptations. Following his ideas I’ve made this little experiment using a LibreOffice Calc spreadsheet that shows three different scenarios:

When re-tossing all of the 10 coins every time like in the first case there is no preservation of “1s” whatsoever. Every new toss starts from scratch.

In the second case, each coin is tossed separately until it shows “1” when the tosser moves on tossing the next coin until all 10 show “1” which usually happens around the 10th tossing.

In case #3 only the remaining “0s” of the previous toss are re-thrown until all coins show a “1” which is by far the most efficient way of preservation, needing less than half of the time and ending in about 4 tosses.

“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/

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.

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 #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.

A #hierarchy is an #emergent property of an otherwise “flat” #network composed of randomly connected elements.

I see more and more individuals doing “organizational change in complex systems” frowning on the mention of “best practices”, documentation, and planning, because:

“in an increasingly interconnected world where technology, information, and customer expectations evolve at an accelerating rate, insights from past performance quickly become irrelevant in many scenarios”

https://medium.com/topology-insight/best-practices-are-useless-in-complex-systems-f7797a12071

All such “modern approaches” to dealing with complex systems forget that the insight from past performances is the only thing we can actually rely on while preparing for the uncertain future.

They also forget that organizations normally work, not in any one of the clear, complicated, complex, and chaotic domains at any point in time, but they are rather in and out of all of those situations all the time, and different parts of the same organization can also be in different situations at the same time.

Best practices are also not “silver bullets” as they would like you to believe. Best practices are the default (only possible) response the system can produce to par the current situation because it depends primarily on the #knowledge_state the system is currently in.

Having diverse perspectives, allowing time for experimentation, and maintaining short and direct learning loops are not some new and “improved” methods the organization should start adopting when things get complicated or complex, but should be rather part of the (documented and planned) very best practices an organization adopts as the normal way of doing business.