The Universe Learning Itself: On the Evolution of Dynamics from the Big Bang to Machine Intelligence

The Universe Learning Itself: On the Evolution of Dynamics from the Big Bang to Machine Intelligence Pradeep Singh ∗, Mudasani Rushikesh †, Bezawada Sri Sai Anurag ‡, Balasubramanian Raman § Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee-247667, India 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. Keywords: cosmic evolution; dynamical systems; complex systems; learning dynamics; machine learning; artificial intelligence; anthropic reasoning; self-organization 1 Introduction Contemporary science offers an astonishing empirical narrative: from the hot, dense plasma of the early universe to galaxies and stars, from chemically active planets to cells, organisms, brains, and finally to cultures, technologies, and artificial learning systems that model the cosmos which produced them [24, 25, 61, 77]. Yet this story is usually told as a sequence of disciplinary episodes—cosmology, astrophysics, geophysics, biology, neuroscience, cognitive science, machine learning—each with its own language and models. In this work we ask a different question: Can we read the entire history of the universe as a single, cross-scale dynamical trajectory, structured by a small set of recurring mathematical motifs? ∗Email: pradeep.cs@sric.iitr.ac.in †Email: mudasani r@cs.iitr.ac.in ‡Email: bezawada ssa@cs.iitr.ac.in §Email: bala@cs.iitr.ac.in 1 arXiv:2512.16515v2 [nlin.AO] 21 Dec 2025 Our starting point is the view, central to dynamical systems theory, that the fundamental object of interest is a state space, a flow on that state space, and the invariant sets and bifurcations that organize long-term behaviour. Over the last decades, this viewpoint has reshaped our understanding of nonequilibrium structure formation in physics [101], self-organization and critical phenomena in complex systems [11], the emergence of autocatalytic chemistry and early life [63, 57, 139], and the collective dynamics of neural tissue poised near criticality [14, 142]. In parallel, it has become a dominant language for machine learning and artificial intelligence: training deep networks is understood as gradient flow on high-dimensional loss landscapes, while inference and control are seen as dynamical processes on representation manifolds and policy spaces [96, 127, 1, 129, 128]. At the same time, “cosmic evolution” and “big history” programmes have emphasized the continuity of structure formation across cosmological, astrophysical, biological, and cultural scales, often using energetic metrics such as energy rate density as a unifying quantity [25]. What is still missing, we argue, is a systematic dynamical- systems reading of this full cross-scale history that also incorporates artificial learning systems as a late-stage continuation of the same motifs. In such a reading, the early universe is not merely “initial conditions”, but a regime of field dynamics whose fluctuations and instabilities define the measure on later trajectories. Gravitational amplification of these fluctuations becomes a concrete example of linear instability giving way to nonlinear pattern formation: small Gaussian perturbations grow, couple, and collap

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