Posts tagged “probabilistic-machine-learning”

3 posts found

Reasoning on Purpose: Neuro-Symbolic AI and the Confidence to Act

Neural networks perceive fluently but hallucinate confidently; symbolic engines reason reliably but shatter on noisy inputs. This week we join the two — and argue the hinge that makes the join work is the calibrated uncertainty we built last week. From Tensor Logic's single equation to confidence-gated inference, here is how AI reasons about what it sees.

Aug 6, 2026•8 min read

ai-tutorialstutorialbeginner

Bayesian Rendering Explained: When a 3D Model Admits What It Doesn't Know

A beginner-friendly tour of Bayesian 3D reconstruction: why an ordinary 3D scan gives one confident answer everywhere, how turning geometry into a distribution produces honest confidence maps, and where the 'prior' that fills in unseen regions comes from.

Jul 18, 2026•7 min read

ai-tutorialstutorialgaussian-splatting

When Gaussians Get Uncertain: Probabilistic 3D Reconstruction

3D Gaussian Splatting gives us a single best-fit scene — but no sense of where that geometry is trustworthy. This week we turn point-estimate splats into distributions over geometry, connecting Bayesian rendering, Fisher information, and diffusion priors back to last week's GP framework.

Jul 16, 2026•8 min read