7 posts found
For continuous stationary positive-definite kernels, Bochner’s theorem turns kernel design into a choice of spectral measure. We derive random Fourier features, connect positional rotations to frequency, and explain where the bridge to attention and spectral transformers needs extra assumptions.
Highlights of AI News for September 14 - 20 2026
Calibrated confidence is a permission slip — this week we spend it. From PILCO's 17.5 seconds of robot experience to V-JEPA 2 planning zero-shot on a Franka arm, we trace how uncertainty becomes action. Then we look at the 2026 result that breaks the arc's own thesis: a world model can be locally well-calibrated and globally, confidently wrong.
A beginner-friendly tour of neuro-symbolic AI: why neural networks perceive brilliantly but can't guarantee anything, why symbolic engines reason perfectly but can't see, and how the field is finally joining the two — with calibrated confidence as the glue at the seam.
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.
Why AI systems that express calibrated uncertainty are safer and more useful — covering overconfident models, epistemic vs aleatoric uncertainty, and calibration metrics including ECE and reliability diagrams.
How scaled dot-product attention is secretly Nadaraya–Watson kernel regression — and what that reveals about the GP–Transformer duality, the Deep GP revival, and why uncertainty is the missing ingredient for world models.