Search arXivSearch

arXiv · 2501.04111

Postsingular Science

Abstract

This study presents, for the first time, a conceptual and formal model of postsingular science (PSS), which analyses and interprets changes in scientific knowledge driven by accelerating technological progress, singularity, and the integration of artificial intelligence (AI) into scientific processes. The PSS model is based on the interplay of six key components: cumulative knowledge, intelligence, technological synergy, quantum information, social dynamics, and environmental sustainability. The interaction of these variables is described through a system of nonlinear differential equations, reflecting the complex feedback loops and synergetic effects characteristic of the postsingular world. A differentiation table contrasting postsingular and classical science is also presented, highlighting the most fundamental differences between contemporary classical science and future postsingular science. The model emphasizes the synergy between humans and artificial intelligence, the role of quantum technologies in accelerating scientific discovery, and the impact of social and ecological factors that either constrain or stimulate scientific progress. It is anticipated that new forms of scientific information dissemination will replace traditional academic publications and that scientific processing will reach an entirely new level of development following the singularity-driven acceleration of technological progress and the integration of AI into R&D. This will herald an era of nonstop, ultrarapid science operating 24/7. The synergy of humans and artificial intelligence will create a scientific union on the basis of fundamentally new principles and methods. This research provides an initial theoretical foundation for further interdisciplinary studies aimed at developing sustainable strategies and effectively managing scientific progress in the postsingular era.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Eldar Knar. 2025-01-09. Postsingular Science. https://arxiv.org/abs/2501.04111

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Self-Reference in Large Language Models: The Introspection Threshold for Recursive Self-Improvement

The pursuit of self-evolving AI raises a critical question: when is autonomous self-improvement sustainable rather than degenerative? Drawing an analogy to von Neumann's complexity threshold for self-reproducing automata, we argue that sustainable recursive self-improvement in Large Language Models (LLMs) requires a functional analogue: introspection -- the system's capacity to simulate its own operations and target modifications. Grounded in Kleene's Second Recursion Theorem, we demonstrate the theoretical existence of such introspective programs. However, an empirical review reveals that while current LLMs exhibit quasi-introspection (e.g., partial metacognition), they fall short of true introspection due to structural bottlenecks: a lack of complete self-access, the feedforward nature of the Transformer, and computational class constraints that prevent fixed-point iteration. We conclude by outlining architectural paths to cross this complexity threshold and discussing the associated safety implications.

physics.soc-ph

Multilayer Analysis of the Global Trade Network

Global trade is more than a single network of aggregate flows. Beneath the observable exchange of products among economies lies a complex multilayer structure, formed by thousands of product-specific trade relationships that differ in their similarity, interdependence, and temporal evolution. Using the CEPII's BACI database, which records bilateral product-level trade flows between economies, we represent the global trade network from 1995 to 2024 as a temporal multilayer network, with economies as nodes and directed weighted trade flows as edges. To investigate product-level organisation and cross-layer similarity, temporal structural change, and the structural role of individual economies, we introduce a random-walk-based similarity measure that provides a unified framework for comparing weighted and directed trade layers. Our results show that the global trade network remains relatively stable over short periods but undergoes gradual structural change over longer timescales. We also find that similarity-based product communities only partially align with the official product taxonomy, indicating that products assigned to the same official category do not necessarily exhibit similar trade-network structures. Finally, we show that an economy's structural influence is not always determined by its trade volume. These results highlight the value of multilayer network analysis for revealing patterns in global trade that remain hidden at the aggregate level.

physics.soc-ph

Detectability limits of scaling laws

Power law scaling relations between size and output are central to quantitative theories of cities, organisms, and other complex systems. Competing theories predict scaling exponents that differ by small fractions, but there is no existing theory for verifying whether a given dataset can even distinguish exponents at the required resolution to address such discrepancies. Here we derive a resolution limit for scaling exponents, giving the smallest exponent difference that any method of analysis can detect. We find that the Hurst exponents governing the evolution of systems' sizes and deviations from the scaling law determine how long a record of growing systems must be before it can separate competing scaling theories. Empirical results suggest that many available data panels are insufficient for reliable scaling model selection.

physics.soc-ph