pf / EZE, 2026

 

Creativity Asemic - Curation - AI / EZE, 2026

Creativity

Found Art

Conceptual Art

Curation

One of the commonplaces directing our relationship with AI is that AI does not think.

Yet the folk wisdom is that we should approach AI as a tool that does what we tell it.

Next, we are "told" that we have to be mindful of what we tell AI as well as be ready to spend time re-iterating what we tell it to refine the results, sometimes even re-starting our AI conversation so as not to have the results too biased on the conversation history.

No less, AI does not think, yet we test it with instruction.

And we still have to think.

We know that in well-defined environments, instructions to AI tend to generate  productive results.

Repeatability is an achievement.

But AI tends to produce syncretic overlap in its responses when its environment is not well-defined. Hence, we find its output confusing or worthless. The same applies to search engine results, though.

AI seems most creative when it is wrong. ... 

Disambiguation is critical here. Hence, re-iteration. Correctness.

Yet, tangent to the "AI does not think" commonplace, is another argument about how AI should improve our thinking.

But why should AI improve our thinking? Good questions are here.

And there are plenty of critics of AI Slop .

And critics such as Gary Marcus have plenty to discuss, especially in terms of the market hype for AI.

But where does any of this take us?

The argument arises that computers are agents of copy-cat, but this capacity has typically been one of the favored aspects of computing.

And AI is also a copycat.

And human beings are also copycats.

The Frankfurt School, of course, developed a theory on the copy-cat tendencies of modern culture, but then, long before the Frankfurt School, Adam Smith developed a theory of economics based on the speed of production, which is largely based on copy-cat. And previous thereto, Plato developed a theory of ideal forms in opposition to copy-cats, sort of. ...

And Andy Warhol also did his take on copy-cat

Yet copy-cat is often a problem for lines of thought that do not understand re-production as necessarily creative. Try this argument, for example.

But we are not lost in the asemics of the indistinguishable so much as stuck in the asemics of creativity and its double sense: to reproduce, to make original.

Hmmm. 

AI Slop often seems so much kitsch.

AI Overview on AI Slop

"AI slop"—defined as low-effort, high-volume AI-generated content (images, text, video)—is generally considered derivative rather than truly creative. While it can demonstrate novelty by producing unexpected, surreal, or "so bad it's good" results, it is often characterized by a lack of intentionality, human emotion, and genuine meaning. 

Here is a breakdown of whether AI slop is considered creative:

1. Arguments Against Its Creativity

• Lack of Intent and Meaning: AI slop is generated without taste, purpose, or understanding. It is often created to exploit the attention economy as clickbait.
• Derivative Nature: AI models are trained on existing human work and "remix" or mimic patterns rather than creating something fundamentally new.
• "Inhuman" Quality: It is characterized as having a "banal, realistic style" that is often formulaic, repetitive, or eerily distorted (e.g., warped shadows, extra fingers).
• Lack of Craft: Because it requires minimal human effort to produce, it lacks the "human labor" and "soul" that traditional art uses to convey emotion. 

2. Arguments for Its "Novelty" or Artistic Value

• A New Aesthetic: Some argue that AI slop is a new, unique, and "surreal" art form in its own right, mirroring the chaotic nature of the internet.
• Democratization of Creativity: It allows non-artists to express ideas or create prototypes quickly.
• Catalyst for Human Creativity: Even if the raw output is "slop," it can act as a "creative tool" to spark ideas in humans, who then refine it into something meaningful.
• Surprising Results: Some people rate AI-generated images as more creative than average human art because of the unexpected combinations AI can produce. 

3. The "Design Problem" Perspective

Many creators argue that "slop" is not a failure of the AI technology itself, but a failure of human editorship. They suggest that if a human guides the AI with intent and refines the output, it ceases to be "slop" and becomes "AI-assisted art". 

In summary, AI slop is typically regarded as generative, not creative—it can produce, but it does not "create" with intent.

Temporary Ground - Abduction and Decision Making / EZE, 2026

On a Philosophy of Decision Making, A Response to Matt Grawitch

What falls short of the pragmatic in decision making as a philosophy?

Be Very, Very Quiet… I’m Hunting Fallacies

 The dynamics of decision making often calls for a temporary grounding 

in order to test out a conjecture, but at least a few implications are here: 

1)  the soundness of the conjecture as is  and  2) the implicit promise to evaluate the test and to adjust the premise ... .

The Illusion of Reason 

These problems also reveal themselves in the current discussion around AI decision making, the main discussion centering on the scope and consequence of a conjecture when implemented, but when consequences are virtualized . . .  , opportunities arise not only for the better evaluation of conjectures, but also for the fulfillment of testing and of subsequent adjustment.

History is usually different from reason, and this difference is often the site of the asemic.

AI on Pragmatic Decision Making

Pragmatic decision-making is a practical, results-oriented approach that prioritizes workable solutions over theoretical ideals or abstract principles. It focuses on "what works" in real-world scenarios, weighing evidence, constraints, and consequences to achieve specific goals efficiently. This method emphasizes adaptability and flexibility to handle  uncertainty. 

Key Components of Pragmatic Decision-Making:

• Results-Oriented: The value of a decision is judged by its practical consequences.
• Realism: Decisions are rooted in the current, actionable reality, rather than perfect, idealistic outcomes.
• Flexibility & Adaptability: Pragmatists adjust their strategies based on experience and changing circumstances.
• Efficiency: It prioritizes the most feasible, effective, and often quickest, course of action. 

Pragmatic vs. Idealistic Decision-Making:

• Idealist: Rejects a job offer because it is not their "dream job".
• Pragmatist: Accepts a lower-level job to gain experience, income, and leverage for future opportunities. 

How to Make Pragmatic Decisions:

1. Define Realistic Goals: Focus on what is attainable now.
2. Evaluate Evidence: Use available data to understand constraints and risks.
3. Prioritize Feasibility: Choose actions that can be implemented with current resources.
4. Experiment and Adapt: Test solutions using short feedback loops and adjust as needed. 

Pragmatic decision-making is valuable for managing risks and ensuring sustainability in dynamic situations. 

AI on Pragmatism

Pragmatism is a philosophical tradition that defines the meaning and truth of ideas by their practical consequences. Instead of looking for absolute, "mirror-like" descriptions of reality, pragmatists treat concepts and theories as tools or instruments for problem-solving and guiding action. 

Core Principles

• The Pragmatic Maxim: Formulated by Charles Sanders Peirce, it states that to understand a concept, you must consider what "practical effects" the object of that concept might have. Your idea of those effects is your whole idea of the object.
• Truth as "What Works": William James famously argued that truth is not a static property; it "happens" to an idea when it is verified through experience. A belief is true if it is "good in the way of belief"—meaning it helps us navigate the world successfully.
• Fallibilism: Pragmatists believe that no human knowledge is absolute or beyond the possibility of error. All beliefs should be treated as "working hypotheses" that may be refined as new evidence emerges.
• Inquiry over Spectatorship: John Dewey rejected the "spectator theory of knowledge," arguing that we are active participants in the world. Knowing is a process of inquiry—using intelligence to transform a confused situation into a resolved one. 

The "Big Three" Founders

Philosopher	Key Focus	Contribution
C.S. Peirce	Science & Logic	Founded the movement; emphasized using the scientific method to clarify meaning.
William James	Psychology & Morality	Popularized the movement; argued that beliefs (including religious ones) are justified if they provide practical value to a person's life.
John Dewey	Education & Democracy	Applied pragmatism to social reform; championed "learning by doing" in schools.

Pragmatism in Everyday Life

In a modern context, being "pragmatic" means choosing a sensible, realistic path over a purely idealistic or dogmatic one. 

• Example: Instead of spending hours arguing about the "perfect" way to split a restaurant bill (Idealism), a pragmatic group might just split it evenly to save time and enjoy the evening (Pragmatism). 

Would you like to dive deeper into how pragmatism influenced modern education or see how it compares to idealism?

AI on Abduction

Abduction, or "inference to the best explanation," is a generative reasoning process used to form hypotheses, where provisional (temporary) conclusions are adopted based on available evidence, subject to future revision. It works as a tentative foundation, where anomalies are observed, and hunches are tested to create a workable, yet non-final, understanding of a phenomenon. 

Key Concepts in Abduction and Temporary Foundations:

• Abduction Mechanism: It begins with observing a surprising fact, then inferring a hypothesis that, if true, would explain the fact. It is crucial for generating new ideas and theories, particularly in uncertain or complex situations.
• Provisional Foundations: The conclusions drawn through abduction are not considered absolute truths. Instead, they are "tentative," serving as a bridge to further investigation.
• Pragmatic Approach: Charles Sanders Peirce, who pioneered the study of abduction, argued that these foundations are grounded in practical action (pragmatism), adapted based on how they work in reality.
• Conditional Abduction: Abduction can be conditional, meaning a new, temporary rule is created to make sense of a situation, which is then formally tested.
• Structure of Reasoning: The process often involves four steps: observing anomalies, generating hunches, creating a deductive structure, and finally testing it through induction. 

Applications:

• AI and Modeling: Abduction serves as a foundation for artificial intelligence to create mental models and understand the world, particularly in creating agent-based systems.
• Research and Theory: It is essential for researchers to develop new theories when existing ones cannot explain new observations.
• "Inference to the Best Explanation": Known as IBE, this method selects the most plausible explanation from a set of candidates, which serves as a temporary, "best" foundation, for example as detailed in this Wikipedia page on Abduction. 

 AI Overview of Epistemology

Epistemology is the branch of philosophy focused on the nature, origin, scope, and justification of knowledge. It investigates what knowledge is, how we acquire it, and the difference between justified belief and opinion. Key questions include: What is knowledge? How do we know what we know? And, what can we know?. 

Etymology
Derived from the Greek words epistḗm(ē), meaning "knowledge," and -logy, which means "study of" or "discourse". 

Key Questions and Concepts

• Definition of Knowledge: Traditionally viewed as justified true belief (JTB), though this is debated, especially following Gettier problems.
Justification: The reasons or evidence supporting a belief.
Internalism vs. Externalism: Internalists argue that justification is based on internal mental states, while externalists believe external factors (like reliable processes) can justify knowledge.
The Scope of Knowledge: Investigating the limits of what can be known, including problems regarding reality and knowledge of other minds. 

Main Branches/Types of Epistemology

• Propositional Knowledge: Focuses on knowledge of facts ("knowing that...").
• Acquaintance Knowledge: Knowledge gained through direct experience or familiarity (e.g., knowing a person or place).
• Procedural Knowledge: "Know-how" or understanding how to perform tasks.
• Virtue Epistemology: Focuses on the intellectual virtues of the person (the agent) seeking knowledge.
• Epistemic Consequentialism: Suggests that ways of forming beliefs are justified if they lead to true beliefs.
• Common Research Approaches: Include Positivism (empirical observation), Interpretivism (human understanding), and Pragmatism (practical application). 

Famous Epistemologists

• Plato: Explored knowledge as justified true belief.
• René Descartes: Focused on doubt and foundational knowledge.
• John Locke & David Hume: British Empiricists focusing on experience.
• Immanuel Kant: Bridged rationalism and empiricism, developing the analytic-synthetic distinction.
• Edmund Gettier: Challenged the traditional JTB definition.
• Bertrand Russell: Distinguished between knowledge by acquaintance and description.