A few "gems" from #WR_Ashby on the "accumulation of adaptations":
>"A compound event that is impossible if the components have to occur simultaneously may be readily achievable if they can occur in sequence or independently...
Thus, for the accumulation of adaptations to be possible, **the system must not be fully joined**.
>The idea so often implicit in physiological writings, that all will be well if only sufficient cross-connexions are available, is, in this context, quite wrong."
I recommend reading the whole book ($20 on Amazon) but if not, here is a good overview of some interesting parts:
#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.
Our brains evolved as control mechanisms for the body to ensure its survival. That's the reason why #AI is beating humans primarily in areas requiring computation and abstract reasoning. We only recently added those to our tool repertoire and didn't have too much time to perfect them as for our ancient sensory-motor control tools.
>“Encoded in the large, highly evolved sensory and motor portions of the human brain is a billion years of experience about the nature of the world and how to survive in it. The deliberate process we call reasoning is, I believe, the thinnest veneer of human thought, effective only because it is supported by this much older and much more powerful, though usually **unconscious, sensorimotor knowledge**. We are all prodigious Olympians in perceptual and motor areas, so good that we make the difficult look easy. Abstract thought, though, is a new trick, perhaps less than 100 thousand years old. We have not yet mastered it. It is not all that intrinsically difficult; it just seems so when we do it.”
Moravec, H. Mind Children: The Future of Robot and Human Intelligence. (Harvard University Press, 1988).
as reported in:
There is a lot of talking against "#linear thinking" and planning in #complexity and how the only "good" hierarchy is a "flat" one.
The above diagram shows how a #hierarchy is a natural effect of folding the linear "information #flow" to match the physical structure of the system.
For nearly 4 decades in organizational change management, I've been using this idea of #hierarchy layers emerging from the #folding of a sequential "information processing" #flow and just found that a whole area in biology deals with this exciting topic.👇
https://www.sciencedirect.com/science/article/pii/S1877050922017811
>"An algorithm solves a problem only if it produces the correct output for every possible input — if it fails even once, it’s not a general-purpose algorithm for that problem."
https://www.quantamagazine.org/alan-turing-and-the-power-of-negative-thinking-20230905/
Most commenters do not realize that no "information processing" (#computation on symbol sequences) of any kind is necessary for an agent to have #control over their internal #states and surroundings.
Think of a #homeostat or comparator such as in #PCT (Perceptual Control Theory).
>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
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"
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.
#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.