Potentiality and actuality

In Quantum Mechanics, Nothingness Is the Potential To Be Anything | Quanta Magazine

Try as they might, scientists can’t truly rid a space or an object of its energy. But what “zero-point energy” really means is up for interpretation.

The Self-Evidencing Agent: Mind, Existence, and Predictive Processing

How the concept of self-evidencing offers a philosophical principle for understanding mind and behavior, consciousness, value, wisdom, and meaning.What is

Noumenon

In philosophy, a noumenon (/ˈnuːmənɒn/, /ˈnaʊ-/; from Ancient Greek: νοούμενoν; pl.: noumena) is knowledge[1] posited as an object that exists independently of human sense.[2] The term noumenon is generally used in contrast with, or in relation to, the term phenomenon, which refers to any object of the senses. Immanuel Kant first developed the notion of the noumenon as part of his transcendental idealism, suggesting that while we know the noumenal world to exist because human sensibility is merely receptive, it is not itself sensible and must therefore remain otherwise unknowable to us.[3] In Kantian philosophy, the noumenon is often associated with the unknowable "thing-in-itself" (German: Ding an sich). However, the nature of the relationship between the two is not made explicit in Kant's work, and remains a subject of debate among Kant scholars as a result.

We speak often of AI as if it were a future event. But it is already here in the fabric of our being. What Karl Ove Knausgaard captures with devastating clarity in his recent essay ‘The Reenchanted… | Kenneth Mikkelsen | 19 comments

We speak often of AI as if it were a future event. But it is already here in the fabric of our being. What Karl Ove Knausgaard captures with devastating clarity in his recent essay ‘The Reenchanted World’ is the departure of the real. “We live in a virtual world, as if the world had been lifted out of itself, into the air, like a giant roof suspended above the ground,” he writes. It is an existential condition. Across philosophy, from Heidegger to Baudrillard, from Don DeLillo to Debord, a singular thread weaves: the world, once our shelter from the self, has become self-referential. Where once we encountered beings in our Zuhandensein—ready-to-hand, intimate, meaningful—we now face a procession of Vorhandensein: inert, objectified, mediated. Simulation replaces encounter. Perception is swallowed by spectacle. And the digital era, in Knausgaard’s terms, “distances culture from the body”. “It is not just the body that disappears—it is the awareness that we are dying,” he writes. What we face is not merely technological change, but what I would call existential dissonance: the erosion of resonance between our inner world, our social world, and the material world. It is the tension that arises when the way one lives, acts, and relates is misaligned with one’s deeper sense of meaning, reality, and value. It manifests itself as a disturbance in being — an unease that is not merely emotional, but rooted in a felt gap between one’s existence as it is and as it could or should be. It is the voice of the self calling from within the noise of the world. This is why Knausgaard’s essay matters for anyone working in, with, or around AI. Because the question isn’t: What can these systems do? It is: What do they undo? “We can explain everything. But we understand nothing,” he writes. Manipulated constructions of truth, in such a time, becomes a currency. As shown in recent events—from Francesca Gino’s research fraud at Harvard to Bezos’ all-female crew venture into space, and Trump’s detention camp —truth is no longer that which corresponds with reality, but that which performs and sells. As Baudrillard warned, the simulation no longer hides the real—it becomes it. And yet, something remains. Knausgaard writes not to despair, but to stay. In the body. In death. In silence. In the concrete. If you seek to understand what is happening—not just in AI, but in the very metaphysics of modern life—I suggest we begin not with data, but with dissonance. With the feeling that something vital is slipping from view, and that “being in the world” now requires resistance. “We are surrounded by mystery. And yet we act as if everything were known,” he writes. AI will never understand this. But we must. Knausgaard’s full essay from Harper’s Magazine can be read via link in comments. | 19 comments on LinkedIn

Frameworks v0.2

FRAMEWORKS v0.2 Many of the thoughts in this document draw on and synthesize concepts from economics, math, physics, chemistry, biology, and psychology. Here are a handful of core concepts that are often relied on as inputs: Activation energy Behavioral economics Cognitive biases Darwinism En...

Re-Engineering Wimsatt for Limited Beings

Science is the best way to produce facts about reality. The best, at least, that limited human beings have devised so far. Yet, not even scientists quite seem to understand how scientific knowledge is generated. This is not only a philosophical but also a practical problem, as our misunderstandings affect the quality of our research and limit the directions it can take. In light of this, it may be good if we reflected a bit more on how we do science — to become better researchers through philosophy. Here, I provide an accessible introduction to a philosophical approach that achieves precisely this: William Wimsatt’s multi-perspectival realism. It disabuses us of widespread but misleading myths and idealizations about science, such as the idea that everything in the world can be reduced to a fundamental level, or that we can approach a “view from nowhere” — complete and objectively detached knowledge of the world. Wismatt proposes an alternative view based on his thorough studies of actual research practice. It cuts deeply into the layered yet messy structure of reality, and the improvised but potent tools we have available, as limited and evolved beings, to explore it. Wimsatt reframes science as an irregular yet adaptive process rather than a cumulative repository of unalterable facts. His philosophy provides a workable and grounded middle way between radical skepticism and naïve belief in the objective truth of science. It explains how knowledge is conceptually constructed by humans, but still connects us to reality in a trustworthy way. We need such a new view of science, not only to improve our research practices and outcomes but, more generally, to gain a more realistic understanding of ourselves, the world, and our place and role within it.

What does it really mean to have a moment of insight? | Psyche Notes to Self

From art to science to love, the moment of insight happens when we see a likeness between different things

Guy Who Sucks At Being A Person Sees Huge Potential In AI

SAN MATEO, CA—After spending the past three decades of his life being totally unable and unwilling to engage in any meaningful way with the world around him, James Parker, a local guy who sucks at being a person, told reporters Thursday that he saw huge potential in AI. “While it’s still in its early phase, artificial intelligence will one day accomplish things that humans could have never even dreamed of doing,” said Parker, who, by all accounts, has never stretched himself to do something he found difficult; has never created anything truly original; and, deep down, has absolutely zero understanding of what makes things good, enjoyable, or rewarding. “Just yesterday, I asked an AI program to write an entire sci-fi novel for me, and [as someone who will die an empty shell of a man who wasted his life doing nothing for the world and, perhaps, should never have been born] I was super impressed. Soon, humans won’t need to do anything at all! Awesome.” At press time, Parker added that as someone whose contributions to society would almost certainly be measured cumulatively as a net loss, he also saw great potential in the future of the metaverse.

The Abstraction Fallacy: Why AI Can Simulate But Not Instantiate Consciousness

Computational functionalism dominates current debates on AI consciousness. This is the hypothesis that subjective experience emerges entirely from abstract causal topology, regardless of the underlying physical substrate. We argue this view fundamentally mischaracterizes how physics relates to information. We call this mistake the Abstraction Fallacy. Tracing the causal origins of abstraction reveals that symbolic computation is not an intrinsic physical process. Instead, it is a mapmaker-dependent description. It requires an active, experiencing cognitive agent to alphabetize continuous physics into a finite set of meaningful states. Consequently, we do not need a complete, finalized theory of consciousness to assess AI sentience—a demand that simply pushes the question beyond near-term resolution and deepens the AI welfare trap. What we actually need is a rigorous ontology of computation. The framework proposed here explicitly separates simulation (behavioral mimicry driven by vehicle causality) from instantiation (intrinsic physical constitution driven by content causality). Establishing this ontological boundary shows why algorithmic symbol manipulation is structurally incapable of instantiating experience. Crucially, this argument does not rely on biological exclusivity. If an artificial system were ever conscious, it would be because of its specific physical constitution, never its syntactic architecture. Ultimately, this framework offers a physically grounded refutation of computational functionalism to resolve the current uncertainty surrounding AI consciousness.

Reconciling truthfulness and relevance as epistemic and decision-theoretic utility.

Spirits and the incompleteness of physics

Complexity, renormalization, and the spirits beyond the horizon of theory

From risk to flourishing: Seeds for responding to a complex world

Flourishing is the actualization of all beings toward their good -- not only individually but collectively, answering the question of `how do we live well with and for each other?'. In science as in society, the tendency to reduce, segregate, specialize, and settle has precluded embracing the concept of flourishing as a guiding principle. We aim to identify an approach to risk science research that promotes the flourishing of socio-ecological-technological systems (SETS). We discuss the normative role of an orientation toward flourishing for the field of risk science. We explore the possibility that (risk) science could be done in a way that is capable of pointing society toward a different future, one more resilient and more capable of flourishing. Our exploration mirrors the conversational nature of flourishing itself: examine ideologies traditionally believed to be rigidly in opposition and consider what emerges in the space(s) and conversation between them. We call this a dialectical approach. Within this paradigm grounded in conversation, pluralism, and pragmatism, we revisit three longstanding tensions in risk science: physical$|$social, determinism$|$uncertainty, contextuality$|$generality. Although these tensions are not new, revisiting them together through the lens of complexity science, and with explicit attention to the normative commitments that shape risk research, reveals underexplored possibilities for the field. Examining them in conversation with the other allows us to explore the provisional seeds for what might be termed complex risk science: interdependence, responsivity, participation, pluralism, openness, and ongoingness. Enacting the dialectical paradigm towards a risk science for a flourishing world will depend on creating collectives toward complex risk science, supporting them with skillfully governed commons, and centering care in our research.

(PDF) Building a Conceptual Framework: Philosophy, Definitions, and Procedure

PDF | In this paper the author proposes a new qualitative method for building conceptual frameworks for phenomena that are linked to multidisciplinary... | Find, read and cite all the research you need on ResearchGate

Illusions of Understanding in the Sciences

Scientists seek to understand the causes of observed phenomena. Beliefs that they have succeeded are based on understanding that is rarely or possibly never complete, and varies in depth and quality. Most often scientists believe they understand more than they do, making their belief an illusion. This illusion then persists in explanations scientists provide in print, in talks, or in discussions. The illusion that a scientist has a valid and complete explanation tends to be magnified when the data are well described by mathematical and computer simulation models due to the precision of such models and their ability to predict well; prediction does not imply causality, but gives the illusion that it does. The first part of this essay supports the case for the universality of partial and incomplete levels of understanding by showing the difficulty of reaching a deep level of understanding for even a simple analysis and model that most scientists use and believe they understand: linear regression. The second part highlights some implications of the existence of many levels of understanding and explanation, and their use by scientists for design, testing, analysis, and theory development. It discusses the way that deduction and induction depend on the levels of understanding and the implications of the illusion that a scientist’s understanding is deep. It makes a case that the many incomplete levels of understanding affect, often unwittingly, the ways scientists design experiments, test theories, comprehend, communicate, and teach.

Argumentation theory | Communication and Mass Media | Research Starters | EBSCO Research

<p>Argumentation theory explores the processes and methods of reasoning and debate used by individuals in both formal and informal contexts. The theory has roots in ancient philosophical discourse, particularly from figures like Aristotle, and has evolved through the contributions of modern philosophers such as Chaïm Perelman and Stephen Toulmin. It highlights how arguments are structured, identifying key components such as claims, grounds (or data), and warrants, which collectively help participants make their case. </p> <p>Additionally, arguments can be categorized into three main types: factual claims, which are verifiable; judgment or value claims, which are subjective; and policy claims, which pertain to proposed courses of action. This framework acknowledges the influence of personal biases, often shaping the reasoning process, and emphasizes the importance of logical support, backing, qualifiers, and rebuttals in strengthening arguments. In academic contexts, the theory suggests that creating valid topics should focus on policy arguments, while also addressing counterarguments to foster a comprehensive debate. Overall, argumentation theory serves as a critical tool for understanding how reasoning and persuasive communication function in various scenarios.</p>

Hypostasis (philosophy and religion)

Pseudo-Aristotle used "hypostasis" in the sense of material substance.[3]