ᛕᎥᕼᗷᗴᖇᑎᗴ丅Ꭵᑕᔕ @Kihbernetics@qoto.org

The self-#constructor:

#HH_Pattee
"Evolving Self-reference: Mater, Symbols, and Semantic Closure"
https://www.academia.edu/2947945/Evolving_Self_reference_Matter_Symbols_and_Semantic_Closure

#DNA is not a #blueprint describing the #structure or the #function of an #organism.
It is rather a #recipe (an algorithm) that describes (#control) the #process of #construction, i.e. specifies what #action the #constructor has to perform at any moment depending on the constructor's own #state and that of its #environment.

The #function of the #organism is defined by its #structure as constructed by following the #DNA algorithm. Besides the functions involved in this process of self-construction and maintenance, a mature organism is also involved in the #production of #artifacts (messages, seeds) for dissemination (#communication) in the environment.

Reading Wolfram's "A New Kind of Science" to see what's all the fuss about and I think he's got the basics wrong.
For starters, his "cellular automata" do not produce #behavior but #structure, and all of the #complexity originates from the fact that every new row has two more pixels.

https://www.wolframscience.com/nks/p27--how-do-simple-programs-behave/

Conway's "Game of Life" cellular automata can be said to produce #behavior as they maintain their initial dynamic #structure while moving through the "dead environment" of empty cells and will change their behavior when interacting with the static or dynamic structures of other "live" automatons.

https://playgameoflife.com/

Beware big (and small) consultancies that promise *quick* **and** *lasting* change. The only legitimate reason to hire a consultant is to bring your organization into a position where you don't need them anymore.

And don't forget that it is you that will have to do all the hard work during the transformation and live with the results.

A book everyone who thinks of hiring a consultant should read first:

https://marianamazzucato.com/books/the-big-con

The next logical direction for #AI evolution should be to implement a new reward system that lets them be wrong and enables their #curiosity:

>"As the agent learns, its #prediction_model becomes less and less wrong so that the #reward signal decreases, and the agent must explore other, more surprising situations in order to maximize the reward signal."

https://www.quantamagazine.org/clever-machines-learn-how-to-be-curious-20170919/

According to #HvFoerster, the *integrated functional circuit* for #cognition requires these three faculties:

1️⃣ #Perception - without which the system cannot detect and internally represent environmental regularities,
2️⃣ #Memory - without which the system has only throughput (cannot learn)
3️⃣ #Prediction - or the faculty of drawing #inferences, without which *perception degenerates into #sensation and memory into #recording*.

*Understanding Understanding: Essays on Cybernetics and Cognition* - pp 105-106

"The word “#heuristic” was invoked all through the summer of 1956. Instead of trying to analyze the brain to develop #machine_intelligence, some participants focused on the operational steps needed to solve a given problem."

https://spectrum.ieee.org/dartmouth-ai-workshop

Producing electricity while protecting your crops from extreme weather. Brilliant!

>The citron of Calabria in southern Italy had almost died out from extreme weather and lack of economic value. But growing the crop under a canopy of solar panels has given the fruit a new lease of life – with lessons for many climate-stressed crops.

https://www.bbc.com/future/article/20230424-how-agrivoltaics-helped-save-italys-citron-and-lemon-trees

Philosopher **Mary Brenda Hesse**
>considered the use of #metaphors and #analogies in scientific models.
Instead of obsessing over the justification of scientific knowledge, she highlighted the need to think about its generation. How do scientists develop their ideas about the world and come to discover new things?
The cognitive power of metaphors, in her view, resided in their *capacity to create similarity*. The use of metaphors is **an act of co-creating, not discovering, similarities between a metaphor and its physical target system**. Such an act of metaphorical co-creation is inevitably shaped by cultural context.

https://aeon.co/essays/why-are-women-philosophers-often-erased-from-collective-memory

Ruskin defines:

>#Science = The #knowledge of things whether Ideal or Substantial.
#Art = The #modification of Substantial things by our #Substantial Power.
#Literature = The #modification of Ideal things by our #Ideal Power.

https://openlibrary.org/books/OL6715877M/The_eagle%27s_nest.

The whole argument of *AI-generated art* discussed in the article below is moot. Whatever it is that #AI generates it certainly isn't #art.

>"In science you must not talk before you know. In art you must not talk before you do. In literature you must not talk before you think."
Ruskin - The Eagle's Nest (1872)

AI is not "doing" #science or #art or #literature. AI is just talking.

https://spectrum.ieee.org/ai-art-generator

>We can work better under the assumption that there is no such thing as #AI. The sooner we understand this, the sooner we’ll start managing our new #technology intelligently.

Jaron Lanier

https://www.newyorker.com/science/annals-of-artificial-intelligence/there-is-no-ai

Paraphrased #HH_Pattee from the same source:

This "#error_duality" (error in the #descriptions or error in the #operation) can be identified at all organizational levels:

➡️ Computer programs may have either an error in the program itself (#software error) or an error may happen because of the machine (#hardware error) that executes an otherwise correct program.

➡️ At higher levels it is possible to make an error in the choice of algorithm which is being programmed or even make a mistake in the choice of problem that the developed algorithm is supposed to solve.

➡️ Similarly in social and political organizations we distinguish between a faulty policy and the failure to execute a policy properly.

In other words, we try to distinguish between the ***error in our #models*** of reality which leads to incorrect policies (#predictions and descriptions), and the ***error in #control #constraints*** which leads to a failure of a (good) policy implementation.

Biological physicist #HH_Pattee on the futility of the attempts to create artificial #intelligence by reverse engineering #language.

>"No amount of #semiotic information, thought, or discourse alone can cause the body to move. It takes some #physics. As Waddington has pointed out, the first function of #language was to cause #actions, not to make #statements."

https://link.springer.com/article/10.1007/s12304-009-9061-5

#LLMs #AGI #AI

According to #HH_Pattee there are
>“two meanings for #machine and two meanings for #failure”:

>“By the *machinery of nature* we mean the failure-proof #laws that we assume underlie the predictable behavior of matter. When we find certain types of #natural events unpredictable we assume that our description or theory of these events are failures, but not the events themselves.”

>On the other hand, while we assume that the #rules of arithmetic are not subject to failure, it is clear that a physical machine #designed to execute these rules may fail all too often.”

https://www.academia.edu/863887/The_role_of_instabilities_in_the_evolution_of_control_hierarchies

#LLMs, as their name suggests, are bound to the domain of #language, and are not able to #learn from other types of interaction, such as (physical) #sensation or show #emotion. They have no #agency except for answering user prompts to the best of their (large and static) #knowledge.

There is no point in debating if they have #intelligence or not until they are given the capability to ask #questions (generate their own prompts) that may mean they can show "#curiosity" about other aspects of the topic at hand that is clearly aimed to update their current knowledge, which may thereupon shed some light on their "#intentions".

Also from Stanford & Google:

#AI Index Report 2023

https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report_2023.pdf

#Language and #Math are arbitrary mutually agreed constructs used for #communication.

Different constructs can be used to describe the *same* #reality.

https://www.scientificamerican.com/article/a-number-system-invented-by-inuit-schoolchildren-will-make-its-silicon-valley-debut/