Topological Vortex Theory and Its Applications in Artificial Intelligence (3)

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投稿日 2025-12-17 02:35:19
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2.5 Self-Organization and Adaptive Learning

Vortex interactions in TVT involve energy transfer and topological reconstruction, a process mappable to the self-organizing optimization of neural networks. The system can autonomously discover the latent structures of data through dynamic adjustments of the vortex field, enabling efficient learning under unsupervised or semi-supervised conditions.

2.6 Quantized Probability and Decision Uncertainty

TVT interprets quantum superposition states as the co-existence of multiple vortex topological states, providing a mathematical framework for handling fuzzy reasoning and uncertain decision-making in thought. This enables AI systems to simulate human tentative, probabilistic decision-making behaviors when faced with incomplete information or dynamic environments.

2.7 Nonlinear Extension of Time and Causality

TVT allows for non-local spacetime correlations at microscopic scales. By altering the temporal evolution path, it can explain time dilation effects in relativity and interpret "spooky actions at a distance" like quantum entanglement as inherent connections of the spacetime topological network, offering new ideas for quantum causal models.

2.8 A New Path for Gravitational Quantization

By describing the quantized characteristics of vortex topological defects, TVT proposes a possible path for the discretization of the gravitational field. This framework attempts to combine the geometric dynamics of general relativity with quantum information correlations, promoting the construction of quantum gravity theory.

2.9 Breakthrough from a Unifying Perspective

TVT attempts to uniformly describe the spacetime geometry of relativity and the information correlation of quantum entanglement within the topological vortex model, providing an interdisciplinary theoretical platform for establishing a common foundation for quantum gravity and complex system dynamics.

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