From Big Bang to AI, Unified Dynamics Enables Understanding of Complex Systems

The evolution of complexity, from the earliest moments of the universe to the emergence of artificial intelligence, forms the central theme of new research by Pradeep Singh, Mudasani Rushikesh, Bezawada Sri Sai Anurag, and Balasubramanian Raman, all from the Indian Institute of Technology Roorkee. This work presents a unified framework that views cosmology, biology, and machine learning not as separate fields, but as successive stages in the evolution of dynamic systems, linked by fundamental principles of change and adaptation. The team demonstrates how processes ranging from the growth of structures in the early universe to the development of life and the creation of artificial intelligence can be understood through a common lens of instability, adaptation, and evolving complexity. By identifying recurring mathematical patterns across these vast scales, the researchers offer a novel perspective on the universe’s history, ultimately framing the development of intelligent systems as a natural culmination of this ongoing evolution of dynamics itself.

Cosmic Evolution, From Big Bang to Complexity

This work establishes a unified framework for understanding the universe’s evolution, tracing a continuous path from the Big Bang to the emergence of complex systems like human societies and artificial intelligence. Researchers demonstrate that cosmology, astrophysics, biology, and machine learning represent successive regimes of dynamic processes occurring on increasingly complex state spaces. The study begins by modeling inflationary field dynamics and the growth of primordial perturbations, revealing how gravitational instability sculpts the cosmic web, the large-scale structure of the universe. This approach extends to understanding how dissipative collapse in baryonic matter leads to the formation of stars and planets, establishing planetary-scale geochemical cycles that create long-lived, non-equilibrium attractors.

Within these attractors, the origin of life emerges as self-maintaining reaction networks, while evolutionary biology is modeled as flows on high-dimensional genotype-phenotype-environment manifolds. The study further proposes that brains function as adaptive dynamical systems operating near critical surfaces, maximizing complexity and information processing. Human culture, including modern machine learning, is then interpreted as symbolic and institutional dynamics that refine engineered learning flows, recursively reshaping their own phase space. Researchers employed mathematical motifs, instability, bifurcation, multiscale coupling, and constrained flows to analyze these transitions across scales. This cross-scale approach aims to provide a theoretical perspective on the universe’s history, culminating in biological and artificial systems capable of modeling, predicting, and perturbing their own future trajectories.

Cosmic Evolution, Complexity, and Initial Conditions

Scientists have developed a unified framework describing the universe’s evolution, tracing a continuous path from the Big Bang to contemporary human societies and artificial intelligence. This work views cosmology, astrophysics, biology, cognition, and machine intelligence not as separate fields, but as successive regimes of dynamics unfolding across increasingly complex state spaces. The research demonstrates that inflation generates a specific probability distribution over initial conditions, effectively defining a measure on the space of possible cosmological trajectories. Observations from the Planck mission tightly constrain the spectral index, confirming a well-characterized ensemble of small inhomogeneities that later amplify through gravitational instability.

Experiments reveal that inflation not only smooths the universe but populates it with a specific distribution of initial perturbations, creating a foundation for structure formation. The team measured how quantum fluctuations during inflation are stretched and amplified, transitioning from quantum to classical behavior through a process of decoherence and coarse-graining. This process yields an emergent classical stochastic process, captured by Langevin or Fokker-Planck equations, demonstrating how classical stochastic dynamics can emerge from underlying quantum dynamics. The research highlights that the “initial conditions” for galaxy formation are not arbitrary, but constrained by the Gaussian field generated during inflation, possessing specific correlations. This framework provides a cross-scale narrative, linking microphysics and cosmology to life, brains, culture, and ultimately, artificial intelligence, demonstrating a continuous evolution of dynamics across the universe.

Universe’s Evolution, From Cosmos to Cognition

This research presents a unified, cross-scale narrative of the universe’s evolution, framing cosmology, astrophysics, biology, and artificial intelligence as successive regimes of dynamical systems. Rather than viewing these fields as separate, the work demonstrates how each builds upon the previous, connected by phase transitions, symmetry-breaking events, and attractors, ultimately tracing a continuous chain from the Big Bang to contemporary learning systems. The team illustrates how gravitational instability shapes the cosmic web, leading to star and planet formation, and how geochemical cycles establish stable, long-lived attractors, providing the foundation for life’s emergence as self-maintaining reaction networks. The study emphasizes that the universe is not simply evolving in state, but also in its capacity for description and learning, with each transition.