Ai No Data – Zero-Training AI™ Interactive Demos

Time-Augmented Decision Space™

Let's Go To Mars!

A spacetime-style “decision worldline” rendered as a tube: Z = time, X/Y = decision coordinates, tube radius + color = constraint pressure.

Controls

Samples

n: 320

Time step

dt: 0.06

Tube thickness

scale: 1.8

Mars Mission Parameters

Thrust limit

thrust: 0.55

Fuel budget

fuel: 0.65

Time-of-flight priority

time: 0.55

Earth gravity well strength

gEarth: 1.00

Mars gravity well strength

gMars: 0.85

Mid-course instability / perturbation

perturb: 0.18

Mission goal tolerance

tolerance: 0.18

Interpretation: the tube is the system’s decision trajectory through time. Thick / bright segments indicate higher constraint pressure.

Real-World Simulation — Earth ➜ Mars

Goal: reach Mars with thrust + fuel limits

Cost Readout

FuelCost: —
TimeCost: —
Instability: —
Total: —

Loading simulation…

Decision Worldline

Drag: orbit • Wheel: zoom • Right-drag: pan

Loading tube…

Time-Augmented Decision Space™

In the full Zero-Training AI™ framework, Decision Space™ is extended beyond static variables into Time-Augmented Decision Space™, where the system evaluates not only the current state of a problem but also how that state is changing over time. Additional dimensions capture temporal dynamics such as rate-of-change, acceleration of inputs, system delays, and stability across future moments. Instead of reacting frame-by-frame, the engine resolves the optimal trajectory of decisions through this expanded space. The mathematics mirrors the principle of least action in physics: out of all possible paths a system could take, the one that actually occurs is the one that minimizes a governing quantity. Likewise, Zero-Training AI™ computes the decision path that minimizes a structured decision functional across time—balancing objectives, constraints, risk, and system dynamics—allowing intelligent behavior to emerge directly from mathematics rather than from training data.