What Humanity Needs To Flourish In The Next Decade
We are headed toward a world of productivity without prosperity, execution without verification and capacity without constraint. But this outcome is not inevitable.
The Ecological Theatre of Selfish Elements
Abstract. Some selfish genetic elements enhance their transmission to the next generation by interfering with and eliminating competing variants within the
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.
Utopian media studies: Introduction
This introduction to the special issue ‘Utopian Media Studies’ sketches a framework for how media studies can become a site of utopian praxis, research and experimentation. It first addresses three apprehensions media scholars might have about the notion of utopian media studies and its appeal to a loaded concept like ‘utopia’. The introduction then outlines five sensibilities to explicate the nature and significance of this special issue’s contribution to the field: a utopian media studies must adopt an ecological approach to media; recognize and be invigorated by limits; work toward the technological liberation of time; connect imagination with practice; and emphasize and enable conditions for transmitting hope. Lastly, the introduction considers Ruth Levitas’s distinction between utopia’s archaeological, ontological, and architectural mode to map some of the concrete ways in which existing media studies research is doing utopian work that aligns with these sensibilities. In connecting work that operates in these modes with the various contributions to this special issue, the introduction reveals the utopian currents that already run through media studies as a discipline and opens up new conceptual and methodological approaches to studying and modeling digital utopias.
The Desktop Regulatory State
Kevin A. Carson The Desktop Regulatory State The Countervailing Power of Individuals and Networks March 2016
The ecology of AI risk
Understanding the risk from applications of artificial intelligence (AI) is a critical part of creating AI governance strategies. Building on the idea of studying AI using ecological and evolutionary perspectives, we propose a novel approach for assessing risk from AI using indicators derived from theoretical ecology models. We illustrate our methods by deriving 3 indicators from population and ecosystem models originating from theoretical ecology. We conclude with a discussion of limitations of our analysis and considerations for improving AI governance policy.
2026 Competitive and Complementary Tools — DAVID C. KRAKAUER
Everything in public life has already ceased to be narrative and no longer follows a thread, but instead spreads out as an infinitely interwoven surface. —Robert Musil, The Man Without Qualities, Chap. 122, “Going Home”
Social Influence and the Logic of Collective Action
An integrated quantitative framework for understanding the dynamics of collective action
Is the Multiverse in the Mind or is the Mind in the Multiverse? with Bernard Carr
Data Annotations as Pedagogical Hints: From Subjective Labels to Critical Thinking
Machine learning courses often use pre-labeled datasets, hiding the subjectivity of human annotation. This creates students with an overly trusting view of AI data and models, undervaluing interpretive diversity. We investigated whether manual data annotation tasks teach students about subjective labeling. Study Design: An annotation activity was implemented at two universities: Fontys (Netherlands) and IT University Copenhagen (Denmark). Students annotated skin lesion images for hair coverage on a 3-point scale. Surveys were collected from 43 participants measuring their understanding of annotation ambiguity, data quality, bias, fairness, implementation barriers, and pedagogical effectiveness. Key Findings: Self-reported familiarity with course content increased substantially across all concepts. Most students recognised that personal interpretation affects annotations. Students rated the activity as more effective than traditional lectures for understanding bias. Participants were motivated to learn more. Main Drawbacks: Emotional unease from viewing medical images was the primary issue. Many students still requested clearer guidelines to reduce disagreement, suggesting they hadn't internalised that disagreement from different perspectives is a learning feature, not a bug. Recommendations for Future Iterations: Ensure sufficient interpretive ambiguity in materials. Reduce repetitive annotation workload. Mitigate emotional unease from sensitive content. Explicitly frame disagreement as a learning opportunity rather than a problem to solve. Manual data annotations effectively teach students that human judgment shapes model behavior and that disagreement reflects domain complexity, not just noise.
All Projects — GLOBAÏA
Explore GLOBAÏA's portfolio of planetary visualizations, interactive tools, and science communication projects.
Georgie Newson - Thinking Collectives: Biological Naturalism and the Problem of Group Minds
Existential Technologies
Who controls whom? Is technology controlling us, or are we controlling it? Does it lead us where it wants to, even to our ruin, or can we force it to yield to our intentions? But what else would our intentions be, if not some further technology? Is the humanity–technology relationship always the same or historically variable? Where is this unknown force heading?
Horismos: Self-representation and the Derived Constitutional Boundary in Enriched Cognitive Systems
We present a theory of self-representing cognitive systems grounded in $$([0,\infty ],+)$$([0,∞],+)-enriched category theory and the Yoneda lemma. The central object is a self-representing $$([0,\infty ],+)$$([0,∞],+)-enriched category $$\mathcal{C}$$C—a Lawvere metric space whose objects are complete epistemic architectures, whose hom-values record directed informational upgrade costs, and which is separated, closed under internal homs, and bilaterally Cauchy complete—together with a contractive cognitive endofunctor $$F:\mathcal{C}\rightarrow \mathcal{C}$$F:C→Cmodelling iterative self-improvement. We establish eight results in a single logical arc. The Horizon Theorem shows that the Yoneda embedding $$\varphi (A)=\mathcal{C}(-,A)$$φ(A)=C(-,A)is never essentially surjective: $$\mathcal{C}$$C sits strictly inside its own free Cauchy completion $$\mathcal{P}(\mathcal{C})$$P(C), with the non-representable presheaves forming a topologically dense family, proved via a reflexivity argument. The Lawvere–Banach Attractor Theorem shows that every contractive endofunctor on a bilaterally complete, separated $$([0,\infty ],+)$$([0,∞],+)-enriched category converges to a unique fixed point $$\mathbf {\Omega }$$Ωat a geometric rate. The Boundary Derivation Theorem shows that $$\mathbf {\Omega }$$Ωis the minimal F-invariant substructure of $$\mathcal{C}$$C, with all of $$\mathcal{C}$$Cas its basin of attraction—the constitutional boundary, derived rather than postulated. The Horizon Expansion Theorem shows that each strictly ascending self-modification produces a new, quantitatively distinct non-representable witness. Beyond these four central results, we prove that Kleene and Bourbaki–Witt conditions yield only non-expansiveness when metrised, that contractive endofunctors form a monoid, and that the Yoneda horizon admits an observable diagnostic stabilising in finite time. The architectural section derives structural corrigibility and the alignment-incompleteness duality among five implications. The organising duality is exact: the non-surjectivity of $$\varphi $$φ and the existence of $$\mathbf {\Omega }$$Ωare two faces of the same $$([0,\infty ],+)$$([0,∞],+)-enriched structure. $$\mathbf {\Omega }$$Ωinhabits the space between them—not as a postulate, but as a proof. We argue that the eight theorems constitute universal laws of contractive cognitive systems: a stable constitutional boundary is not an engineering design choice but a topological inevitability for any reliably self-improving agent operating within a self-representing enriched metric space. The postulate becomes a theorem. The boundary is not imposed. It emerges.
