🧪 Artificial Intelligence Generative Ai Simulation

Every modern generative AI model — GAN, VAE, or diffusion — works the same underlying way: it samples a point from a simple Gaussian "latent space" and runs it through a learned decoder to produce an output. This simulator makes that process visible and tangible. A cloud of points sampled from a 3D Gaussian prior represents the latent space a real model would sample from (compressed here from hundreds of dimensions down to three so it can be seen directly). Two of those points, A and B, anchor a path you can walk with the interpolation slider, watching a decoded object morph smoothly from one to the other — exactly the "latent walk" technique used to explore what a trained generative model has learned. A linear/spherical toggle shows why real latent walks use spherical interpolation to stay on the high-density shell of the Gaussian, and a temperature control demonstrates the fidelity/diversity trade-off every sampling-based generator has to make.