Blog

Practical ML and AI writing for people who build — the research and engineering behind what we ship.

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.

artifocial•August 28, 2026•41 min read

ai-tutorialstutorial

The Safety Org Is the Safety Policy

Highlights of AI News for August 17 - 23 2026

Aug 24, 2026•22 min read

ai-tutorialstutorial

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-tutorialstutorial

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-tutorialstutorial

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-tutorialstutorial

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-tutorialstutorial

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-tutorialstutorial

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-tutorialstutorial

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-tutorialstutorial

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-tutorialstutorial

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-tutorialsai-news

OpenAI's Super App: GPT-5.6 and the Week AI Started Doing the Work

Highlights of AI News for July 6 - 12 2026

Jul 14, 2026•11 min read

ai-tutorialstutorial

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-tutorialstutorial

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-tutorialstutorial

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-tutorialsai-news

Physical Intelligence π0.7: The Generalist Robot Brain That Actually Generalizes

Highlights of AI News for April 13 - 19 2026

Apr 21, 2026•18 min read

ai-tutorialstutorial

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-tutorialstutorial

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-tutorialstutorial

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-tutorialsai-news

Meta Is Back: Inside Muse Spark and the $14B Alexandr Wang Bet

Highlights of AI News for April 06 -12 2026

Apr 14, 2026•15 min read

ai-tutorialstutorial

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-tutorialstutorial

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-tutorialstutorial

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-newsweekly-roundup

Claude Wrote a Working FreeBSD Root Exploit in 4 Hours

Highlights of AI News for March 30 - April 05 2026

Apr 7, 2026•20 min read

ai-tutorialstutorial

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