The Universe Learning Itself: On the Evolution of Dynamics from the Big Bang to Machine Intelligence
🤖 AI Summary
This paper addresses the fragmentation of evolutionary mechanisms across scales and disciplines—cosmology, geophysics, biology, cognitive science, and artificial intelligence—by constructing a unified dynamical framework spanning from the Big Bang to AI. Methodologically, it introduces the novel paradigm of “dynamics self-evolution,” integrating nonequilibrium thermodynamics, dynamical systems theory, complex networks, and high-dimensional manifold analysis; it further unifies cosmological perturbation theory, reaction network modeling, and phase-space reconstruction techniques for learning systems. Key contributions include: (i) revealing physics, biology, and AI as a dynamical continuum wherein state spaces progressively enrich via phase transitions and symmetry breaking; (ii) establishing a cross-scale dynamical taxonomy; and (iii) identifying the universal role of instabilities, bifurcations, and constraint flows on zero-measure subsets. The work provides AI with a cosmologically grounded theoretical foundation and transferable design principles.
Application Category
📝 Abstract
We develop a unified, dynamical-systems narrative of the universe that traces a continuous chain of structure formation from the Big Bang to contemporary human societies and their artificial learning systems. Rather than treating cosmology, astrophysics, geophysics, biology, cognition, and machine intelligence as disjoint domains, we view each as successive regimes of dynamics on ever-richer state spaces, stitched together by phase transitions, symmetry-breaking events, and emergent attractors. Starting from inflationary field dynamics and the growth of primordial perturbations, we describe how gravitational instability sculpts the cosmic web, how dissipative collapse in baryonic matter yields stars and planets, and how planetary-scale geochemical cycles define long-lived nonequilibrium attractors. Within these attractors, we frame the origin of life as the emergence of self-maintaining reaction networks, evolutionary biology as flow on high-dimensional genotype-phenotype-environment manifolds, and brains as adaptive dynamical systems operating near critical surfaces. Human culture and technology-including modern machine learning and artificial intelligence-are then interpreted as symbolic and institutional dynamics that implement and refine engineered learning flows which recursively reshape their own phase space. Throughout, we emphasize recurring mathematical motifs-instability, bifurcation, multiscale coupling, and constrained flows on measure-zero subsets of the accessible state space. Our aim is not to present any new cosmological or biological model, but a cross-scale, theoretical perspective: a way of reading the universe's history as the evolution of dynamics itself, culminating (so far) in biological and artificial systems capable of modeling, predicting, and deliberately perturbing their own future trajectories.
Problem
Research questions and friction points this paper is trying to address.
Unifying cosmic to artificial dynamics via phase transitions and attractors.
Describing life, evolution, and cognition as emergent adaptive dynamical systems.
Interpreting human culture and AI as recursive learning flows reshaping phase space.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Unified dynamical-systems narrative across cosmic to artificial scales
Phase transitions and emergent attractors stitch successive regimes
Recursive engineered learning flows reshape symbolic and institutional dynamics
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