AI and AGI seek to imitate the principles of human cognition, but most of them are past data. Therefore, as long as we rely on 'big data' as we do now, we will not be able to obtain the natural benefits and efficiency that exist in the 'present'. The recently developed LLM model itself acknowledges this.
AI and AGI are at a point where they will have to confront the task of 'acquiring and analyzing real-time data', which is the fundamental principle of human cognition. Fortunately, the ever-evolving computer communication technology and the rich development of the external neural network, the 'IoT (Internet of Things),' are giving strength to this.

Here, we propose Udynamics, which is designed around 'real-time data processing,' the fundamental principle of human cognition.
This can be called a 'natural principle dynamic equation' that has undergone reflection on Eastern and Western history and humanities and society.
This and its development model will be able to provide a strong research base and optimal algorithms for AI and AGI.

1.Udynamics General Equation

$\mathcal{UL} = \frac{1}{2} g_{ij}(r) \dot{r}^i \dot{r}^j - V(r)$

* $T = \frac{1}{2} g_{ij}(r) \dot{r}^i \dot{r}^j$ is the spatial geometric metric tensor,
* $T = \frac{1}{2} g_{ij} \dot{r}^i \dot{r}^j$ 는is the rate of change of position (velocity),
* $V(r) = -\frac{GMm}{r}$ is the potential energy, which varies with position.

2.UDynamics UH equation

$\mathcal{H}_{\text{Ryoo}} = \sqrt{g} \left( \mathcal{R} - \frac{1}{2} K_{ij} K^{ij} \right)$

* HU is the Hamiltonian density with the time dimension removed,
* g is the determinant of the metric in the spatial geometry,
* $\mathcal{R} = g^{ij} R_{ij}$ is the Ricci curvature in 3D space,
* $\mathcal{H} = \sqrt{g} \left( \mathcal{R} - \frac{1}{2} K_{ij} K^{ij} \right)$ is the extrinsic curvature of the spatial geometry.
* $K_{ij}$ is the extrinsic curvature of the spatial geometry.

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