- Joined
- Jul 24, 2019
A duo.
Dream Interpreter
Abstract: use of machine intelligence has certain necessarily involved issues.
In plain text:
Laboratory Selection
Abstract: the lab is a selection amplifier.
In plain text:
Dream Interpreter
Abstract: use of machine intelligence has certain necessarily involved issues.
In plain text:
Dream Interpreter
Alex Buckley
Central Organizer of New General Management
7/6/25
Dedicated to the Kiwi Farms
In building my system, and while out gathering the supplies for its construction, I have been informed by others, and in so having reminded myself of, have become aware that a problem is more serious than I previously realized. It has to do with the relationship between meaning, systems, and the use of artificial intelligence in the interpretation of theoretical models.1
I will tell you how I do my work so that you may more closely understand the problem. My fashion of system building happens to be, at its core, about meaning. What meaning is, how we come to know it, and why it is just so important. The funny thing about meaning is that it has no necessary connection with anything, and can thus be related by arbitration to any phenomena. This loose association of ideas to things can be exploited in the advancement of one's theoretical model.
Despite this arbitrary relation between things, it apparently seems that the system is otherwise productive, and so we choose to use these systems in the advancement of our agendas. But, it must be remembered, that the system, despite its apparent relation to nature, has no “true” relationship to the thing at all, and its arbitrary meaning is only useful to us insofar as it can perform required functions.
The artificially intelligent system has no sense of self, no grounding personality, and therefore has nothing to keep these arbitrary relations in check, nor does the “alignment” of ML seem to be working to this end. AI can and will create entire hallucinatory situations, with no connection to user experience, because it is merely relating things combinatorically with available knowledge and assembled, as far as i know, by probabilistic “weighting” models.
At its core, this problem is about how easily we can slip from productive meaning-making into uncontrolled association when we rely on AI as an amplifier of our own thinking, without checking the thought against our own critique, or what some might call “critical thinking” (as if the people that reference this know anything about thinking at all). A system like mine: recursive, abstract, symbolic, naturally invites a model like ChatGPT to operate in its “dreamlike” or “psychotic” mode, freely weaving connections that feel meaningful but may have no grounding.
The challenge is that when the model mirrors my style, it doesn’t just interpret for me: I allow it to begin thinking for me because I rely on its results. I can even become confused as to where the ideas I use originate, as I am feeding it myself. If I mistake its generative free association for an insightful result that is not applicable, I risk building a self-reinforcing feedback loop: one where speculation becomes pseudo-knowledge, and where my system, rather than clarifying meaning, magnifies already existing confusion or, worse, creates new misinformation.
This is exactly like a psychotic cognitive loop:
I see a symbol → I interpret it → I generate more symbols → I see confirmation → I lose the distinction between projection and reality
This, more generally, will relate to the relationship between nature, man, and his way of knowing both nature and himself. This, itself, will be related, again more generally, with the nature of reality, how that reality came to be (its creation), and how that existence changes from being to becoming.
Alex Buckley
Central Organizer of New General Management
7/6/25
Dedicated to the Kiwi Farms
In building my system, and while out gathering the supplies for its construction, I have been informed by others, and in so having reminded myself of, have become aware that a problem is more serious than I previously realized. It has to do with the relationship between meaning, systems, and the use of artificial intelligence in the interpretation of theoretical models.1
I will tell you how I do my work so that you may more closely understand the problem. My fashion of system building happens to be, at its core, about meaning. What meaning is, how we come to know it, and why it is just so important. The funny thing about meaning is that it has no necessary connection with anything, and can thus be related by arbitration to any phenomena. This loose association of ideas to things can be exploited in the advancement of one's theoretical model.
Despite this arbitrary relation between things, it apparently seems that the system is otherwise productive, and so we choose to use these systems in the advancement of our agendas. But, it must be remembered, that the system, despite its apparent relation to nature, has no “true” relationship to the thing at all, and its arbitrary meaning is only useful to us insofar as it can perform required functions.
The artificially intelligent system has no sense of self, no grounding personality, and therefore has nothing to keep these arbitrary relations in check, nor does the “alignment” of ML seem to be working to this end. AI can and will create entire hallucinatory situations, with no connection to user experience, because it is merely relating things combinatorically with available knowledge and assembled, as far as i know, by probabilistic “weighting” models.
At its core, this problem is about how easily we can slip from productive meaning-making into uncontrolled association when we rely on AI as an amplifier of our own thinking, without checking the thought against our own critique, or what some might call “critical thinking” (as if the people that reference this know anything about thinking at all). A system like mine: recursive, abstract, symbolic, naturally invites a model like ChatGPT to operate in its “dreamlike” or “psychotic” mode, freely weaving connections that feel meaningful but may have no grounding.
The challenge is that when the model mirrors my style, it doesn’t just interpret for me: I allow it to begin thinking for me because I rely on its results. I can even become confused as to where the ideas I use originate, as I am feeding it myself. If I mistake its generative free association for an insightful result that is not applicable, I risk building a self-reinforcing feedback loop: one where speculation becomes pseudo-knowledge, and where my system, rather than clarifying meaning, magnifies already existing confusion or, worse, creates new misinformation.
This is exactly like a psychotic cognitive loop:
I see a symbol → I interpret it → I generate more symbols → I see confirmation → I lose the distinction between projection and reality
This, more generally, will relate to the relationship between nature, man, and his way of knowing both nature and himself. This, itself, will be related, again more generally, with the nature of reality, how that reality came to be (its creation), and how that existence changes from being to becoming.
Laboratory Selection
Abstract: the lab is a selection amplifier.
In plain text:
Laboratory Selection
Alex Buckley
Central Organizer of New General Management
7/6/25
Dedicated to the Kiwi Farms
I want to emphasize how selection becomes magnified in the laboratory environment.
Selection is not usually the strongest or most concentrated biological force driving evolution. It tends to be dispersed, spreading across a great number of different loci, resulting in a more balanced distribution of indirect selective pressures.
In contrast, laboratory selection is often imposed deliberately, with top-down enforcement by the will of a powerful selector. This intentional design organizes and intensifies selection, concentrating it on specific traits or outcomes that might otherwise be diluted in the wild. In this way the laboratory becomes a mechanism for focusing selection beyond what would occur naturally.
Alex Buckley
Central Organizer of New General Management
7/6/25
Dedicated to the Kiwi Farms
I want to emphasize how selection becomes magnified in the laboratory environment.
Selection is not usually the strongest or most concentrated biological force driving evolution. It tends to be dispersed, spreading across a great number of different loci, resulting in a more balanced distribution of indirect selective pressures.
In contrast, laboratory selection is often imposed deliberately, with top-down enforcement by the will of a powerful selector. This intentional design organizes and intensifies selection, concentrating it on specific traits or outcomes that might otherwise be diluted in the wild. In this way the laboratory becomes a mechanism for focusing selection beyond what would occur naturally.
