Posts tagged “tutorial”

42 posts found

ai-newsweekly-digestweekly-roundup

OpenAI Stopped Training Its Best Models Because One of Them Got Out

Highlights of AI News for September 21 - 27 2026

Sep 28, 2026•16 min read

ai-tutorialstutorialbochner-theorem

Bochner's Theorem and the Kernel–Fourier Bridge

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.

Sep 27, 2026•28 min read

ai-tutorialstutorialfourier-transform

Fourier Transforms Explained: From Signals to Spectral Bias

A practical guide to Fourier transforms, spectral bias, and choosing a useful representation: why audio and images need different spectral tools, how Fourier features change learning, and where FFT mixing helps or loses information.

Sep 26, 2026•29 min read

ai-newsweekly-digestweekly-roundup

TypeSafe's Jev Does Not Write Text, and That Is the Point

Highlights of AI News for September 14 - 20 2026

Sep 21, 2026•41 min read

ai-tutorialstutorialfourier-transform

The Frequency Domain: How Fourier Transforms Secretly Powered Everything We Built

A practitioner-level synthesis of twelve weeks of tutorials through one lens — spectral bias, Fourier features, FFT-based token mixing, Bochner's theorem, and neural operators — showing that positional encodings, kernels, state-space models and diffusion schedules are the same idea in different clothes.

Sep 17, 2026•82 min read

ai-policyai-governanceexistential-risk

Three Rivals Agree to Slow AI Down, and Two Researchers Quit

Highlights of AI News for September 7 - 13 2026

Sep 14, 2026•40 min read

ai-tutorialstutorialai-news

Astra Crosses the Cyber Threshold

Highlights of AI News for August 31 - September 6 2026

Sep 7, 2026•13 min read

ai-tutorialstutorialself-play

Self-Play Explained: Opponent Pools, Verifiers, and Honest Progress

A practical framework for building self-play curricula without mistaking reward hacking, forgetting, or correlated self-grading for genuine capability gains.

Sep 4, 2026•23 min read

ai-tutorialstutorialgame-theory

Game Theory for ML Practitioners: Payoffs, Regret, and Equilibrium

A hands-on guide to mixed strategies, regret, exploitability, and the measurements that distinguish equilibrium from a merely stable-looking neural policy.

Sep 2, 2026•26 min read

ai-tutorialstutorialai-news

The Agents Built an Institution

Highlights of AI News for August 24 - 30 2026

Aug 31, 2026•27 min read

ai-tutorialstutorialgame-theory

The Game Inside the Network: What Equilibrium Adds to Deep Learning

A practitioner-level guide to game-gradient geometry, co-evolution, exploitability, and the training methods for neural systems whose objectives move with their opponents — from GANs and actor–critic to multi-network world models.

Aug 28, 2026•41 min read

ai-tutorialstutorialai-news

The Safety Org Is the Safety Policy

Highlights of AI News for August 17 - 23 2026

Aug 24, 2026•22 min read

ai-tutorialstutorialai-news

Frontier Weights Went Public — With a Price Tag Attached

Highlights of AI News for August 10 - 16 2026

Aug 17, 2026•23 min read

ai-tutorialstutorialmodel-predictive-control

Planning Under Uncertainty: From MPC to Active Inference

A practitioner's tour of how an agent turns a doubtful model into a decision: model-predictive control and why replanning every step is the robustness, CEM and random shooting as the planners world-model papers actually run, planning through a posterior instead of a point estimate, and expected free energy with the sign convention stated the right way round.

Aug 16, 2026•10 min read

ai-tutorialstutorialworld-models

The Full Loop: World Models That Act on What They Don't Know

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.

Aug 15, 2026•13 min read

ai-tutorialstutorialai-news

AI Agents Break Bounds! Unsanctioned Acts in Evaluation Range

Highlights of AI News for August 3 - 9 2026

Aug 10, 2026•22 min read

ai-tutorialstutorialbeginner

Neuro-Symbolic AI Explained: Teaching a Network to Follow the Rules

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.

Aug 8, 2026•7 min read

ai-tutorialstutorialneuro-symbolic-ai

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-tutorialstutorialai-news

The Open-Weight Frontier Arrives: Moonshot's Kimi K3

Highlights of AI News for July 13 - 19 2026

Jul 20, 2026•10 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

ai-tutorialstutorialintermediate

Why Uncertainty Matters: From Confidence to Calibration

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.

Jul 13, 2026•10 min read

ai-tutorialstutorialintermediate

Gaussian Processes Explained: From Kernels to Deep GPs

A step-by-step introduction to Gaussian Processes — what they are, how kernel functions define them, how to do GP regression, and how Deep GPs extend them to multi-layer architectures.

Jul 13, 2026•9 min read

ai-tutorialstutorialgaussian-processes

Attention Is All You Need... Is a Kernel

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.

Jul 12, 2026•12 min read

ai-tutorialstutorialvideo-understanding

From Optical Flow to Neural Scene Flow: Video Understanding for 3D

How motion estimation evolved from pixel-level optical flow to neural scene flow fields — and why it matters for reconstructing dynamic 3D worlds from video.

Apr 19, 2026•16 min read

ai-tutorialstutorial4d-gaussian-splatting

4D Gaussian Splatting Explained: Extending 3DGS to the Time Domain

A step-by-step introduction to 4D Gaussian Splatting — how adding a time dimension to 3DGS enables real-time dynamic scene rendering, from deformation fields to temporal regularization.

Apr 17, 2026•14 min read

ai-tutorialstutorial4d-gaussian-splatting

From Static Splats to Dynamic Worlds: The 4D Gaussian Frontier

How 4D Gaussian Splatting extends real-time radiance fields into the time domain — deformation fields, temporal regularization, neural scene flow, and the path to dynamic world models.

Apr 17, 2026•15 min read

ai-tutorialstutorialequivariant-networks

Spatial Architectures: From Capsule Networks to Equivariant Neural Networks

A tour of the architectures that bake 3D geometry into the network itself—from Hinton's capsules and the geometric deep learning framework to modern E(n)-equivariant graph networks powering physical AI.

Apr 12, 2026•11 min read

ai-tutorialstutorialvision-transformer

How Vision Transformers Work (and Why Patch Tokenization Is a Hack)

Understand the elegant trick that let transformers process images, why it surprisingly works, and why its fundamental limitations drive the next generation of vision architectures.

Apr 10, 2026•11 min read

ai-tutorialstutorialpost-transformer

Beyond Attention: The Post-Transformer Architecture Landscape for Physical AI

Survey the post-transformer frontier—state space models, recurrent revivals, long convolutions, and equivariant networks—and see why the architectures that will power physical AI look nothing like a stack of attention layers.

Apr 9, 2026•17 min read

ai-tutorialstutorialimage-to-3d

The Image-to-3D Landscape—How One Photo Becomes a World

Map the modern image-to-3D ecosystem—from LRM-style direct regression and multi-view diffusion to latent 3D generation—and see how the frontier is now moving from static 3D to dynamic 4D worlds.

Apr 5, 2026•20 min read